From 35bc11f5b0d85b7cacdf0cfca1329af2f7af7fab Mon Sep 17 00:00:00 2001 From: johnathan Date: Wed, 13 Nov 2024 02:19:15 -0600 Subject: [PATCH 01/80] added model interpretability with SHAP/LIME --- .../Untitled-checkpoint.ipynb | 1383 + .../.ipynb_checkpoints/__init__-checkpoint.py | 1 + .../.ipynb_checkpoints/main-checkpoint.nf | 36 + .../.ipynb_checkpoints/main-checkpoint.py | 37 + .../requirements-checkpoint.txt | 9 + .../variant_model_interpreter-checkpoint.py | 190 + bin/model_interpreter/__init__.py | 1 + .../LIME_TO_JSON-checkpoint.py | 91 + .../SHAP_to_JSON-checkpoint.py | 46 + .../.ipynb_checkpoints/__init__-checkpoint.py | 2 + bin/model_interpreter/bin/LIME_TO_JSON.py | 91 + bin/model_interpreter/bin/SHAP_to_JSON.py | 46 + bin/model_interpreter/bin/__init__.py | 2 + bin/model_interpreter/example.ipynb | 1392 + bin/model_interpreter/main.nf | 32 + bin/model_interpreter/main.py | 45 + .../results/lime_analysis.json | 195860 +++++++++++++ .../results/shap_analysis.json | 225992 +++++++++++++++ .../genomic_variant_report_template.html | 163 + .../.ipynb_checkpoints/__init__-checkpoint.py | 1 + .../numpy_to_python-checkpoint.py | 16 + bin/model_interpreter/utils/__init__.py | 1 + .../utils/numpy_to_python.py | 16 + .../variant_model_interpreter.py | 190 + main.nf | 36 + 25 files changed, 425679 insertions(+) create mode 100644 bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py create mode 100644 bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf create mode 100644 bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py create mode 100644 bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt create mode 100644 bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py create mode 100644 bin/model_interpreter/__init__.py create mode 100644 bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py create mode 100644 bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py create mode 100644 bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py create mode 100644 bin/model_interpreter/bin/LIME_TO_JSON.py create mode 100644 bin/model_interpreter/bin/SHAP_to_JSON.py create mode 100644 bin/model_interpreter/bin/__init__.py create mode 100644 bin/model_interpreter/example.ipynb create mode 100644 bin/model_interpreter/main.nf create mode 100644 bin/model_interpreter/main.py create mode 100644 bin/model_interpreter/results/lime_analysis.json create mode 100644 bin/model_interpreter/results/shap_analysis.json create mode 100644 bin/model_interpreter/templates/genomic_variant_report_template.html create mode 100644 bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py create mode 100644 bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py create mode 100644 bin/model_interpreter/utils/__init__.py create mode 100644 bin/model_interpreter/utils/numpy_to_python.py create mode 100644 bin/model_interpreter/variant_model_interpreter.py diff --git a/bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..dd5b3af --- /dev/null +++ b/bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,1383 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1aa4b5e6-4e6e-451a-8496-3c13d3b8fe75", + "metadata": {}, + "outputs": [], + "source": [ + "from variant_model_interpreter import ModelInterpreter" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba16b3e6-b9a4-4ecb-a471-8b22fa7392f7", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0bf68cf0-92ae-4ca4-a8f0-14a57cce1e23", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:variant_model_interpreter:Loaded model with 41 features\n", + "INFO:variant_model_interpreter:Model features: ['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED', 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg', 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax', 'cons_transcript_ablation', 'cons_splice_acceptor_variant', 'cons_splice_donor_variant', 'cons_stop_gained', 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost', 'cons_transcript_amplification', 'cons_inframe_insertion', 'cons_inframe_deletion', 'cons_missense_variant', 'cons_protein_altering_variant', 'cons_splice_region_variant', 'cons_splice_donor_5th_base_variant', 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD', 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L', 'simple_repeat']\n", + "INFO:variant_model_interpreter:Missing features: set()\n", + "INFO:variant_model_interpreter:Extra features: {'identifier', 'confidence level', 'curationScoreHGMD', 'c_HGMD_Exp_Different_pChange', 'omimVarFound', 'nc_HGMD_Exp', 'M_CAP_score', 'isB/LB', 'max_ranking', 'c_CLNREVSTAT', 'c_isPLP', 'GERPpp_RS', 'nc_ClinVar_Exp', 'c_isBLB', 'Polyphen2_HDIV_score', 'phyloP100way_vertebrate', 'Polyphen2_HVAR_score', 'SIFT_score', 'clinvarNumB', 'hgmd_rs', 'REVEL_score', 'c_HGMD_Exp_Same_pChange', 'c_HGMD_Exp_Stop_Loss', 'recessive', 'ranking', 'hgmdVarFound', 'omimGeneFound', 'omimSymMatchFlag', 'nc_isBLB', 'c_ClinVar_Exp_Different_pChange', 'AR.matched', 'diffuse_Phrank_STRING', 'clinVarVarFound', 'hgmdSymMatchFlag', 'decipherVarFound', 'clinvarNumLB', 'nc_CLNREVSTAT', 'CLASS', 'DANN_score', 'c_HGMD_Exp_Del_to_Missense', 'CADD_phred', 'min_ranking', 'curationScoreOMIM', 'nc_isPLP', 'c_ClinVar_Exp_Same_pChange', 'hgmdSymptomScore', 'nc_RANKSCORE', 'isP/LP', 'predict', 'c_RANKSCORE', 'dgvVarFound', 'hgmdSymptomSimScore', 'clinVarSymMatchFlag', 'omimSymptomSimScore', 'c_ClinVar_Exp_Del_to_Missense', 'curationScoreClinVar', 'phrank', 'hgmdGeneFound', 'AD.matched', 'gnomadGeneOELof', 'clinvarNumLP', 'dominant', 'MutationAssessor_score', 'clinVarGeneFound', 'clinvarNumP', 'c_HGMD_Exp_Start_Loss', 'fathmm_MKL_coding_score', 'FATHMM_score', 'confidence'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2511, 41)\n", + "Index(['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score',\n", + " 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI',\n", + " 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED',\n", + " 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg',\n", + " 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax',\n", + " 'cons_transcript_ablation', 'cons_splice_acceptor_variant',\n", + " 'cons_splice_donor_variant', 'cons_stop_gained',\n", + " 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost',\n", + " 'cons_transcript_amplification', 'cons_inframe_insertion',\n", + " 'cons_inframe_deletion', 'cons_missense_variant',\n", + " 'cons_protein_altering_variant', 'cons_splice_region_variant',\n", + " 'cons_splice_donor_5th_base_variant',\n", + " 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD',\n", + " 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L',\n", + " 'simple_repeat'],\n", + " dtype='object')\n", + " conservationScoreDGV gnomadAF gnomadAFg LRT_score \\\n", + "10-135085084-C-T 1.0 0.000019 0.000019 0.000000 \n", + "7-102281151-G-GA 3.0 0.000000 0.000000 0.126376 \n", + "6-17602826-G-T 3.0 0.000000 0.000000 0.000000 \n", + "18-42532220-G-A 3.0 0.000008 0.000032 0.000000 \n", + "23-39932684-A-G 3.0 0.000000 0.000000 0.000000 \n", + "\n", + " LRT_Omega gnomadGeneZscore gnomadGenePLI \\\n", + "10-135085084-C-T 0.000000 0.38049 2.198300e-14 \n", + "7-102281151-G-GA 1.341925 0.18170 1.130200e-01 \n", + "6-17602826-G-T 0.000000 0.30017 1.747100e-02 \n", + "18-42532220-G-A 0.000000 1.09650 1.000000e+00 \n", + "23-39932684-A-G 0.000000 1.87970 1.000000e+00 \n", + "\n", + " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", + "10-135085084-C-T 0.926 3.0 24.600000 ... \n", + "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", + "6-17602826-G-T 0.687 4.0 40.000000 ... \n", + "18-42532220-G-A 0.110 3.0 29.000000 ... \n", + "23-39932684-A-G 0.141 3.0 24.400000 ... \n", + "\n", + " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", + "10-135085084-C-T 0 3.0 2.0 \n", + "7-102281151-G-GA 0 4.0 1.0 \n", + "6-17602826-G-T 0 4.0 1.0 \n", + "18-42532220-G-A 0 3.0 2.0 \n", + "23-39932684-A-G 0 3.0 2.0 \n", + "\n", + " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M No.Var.L \\\n", + "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 0.0 \n", + "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 0.0 \n", + "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 0.0 \n", + "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 0.0 \n", + "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 0.0 \n", + "\n", + " simple_repeat \n", + "10-135085084-C-T 0 \n", + "7-102281151-G-GA 0 \n", + "6-17602826-G-T 0 \n", + "18-42532220-G-A 0 \n", + "23-39932684-A-G 0 \n", + "\n", + "[5 rows x 41 columns]\n", + "CPU times: user 3.1 s, sys: 228 ms, total: 3.33 s\n", + "Wall time: 303 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "# Initialize interpreter\n", + "interpreter = ModelInterpreter()\n", + "\n", + "# Load model\n", + "interpreter.load_model(\"/home/johnathanj/model_xgb_sep2024_nd.job\")\n", + "\n", + "input_df = pd.read_csv('/home/johnathanj/projects/aim/explainable_AI/filtered_joint_2199195_SAMPLE_NAME_2199195_nd_predictions.csv', index_col = 0)\n", + "\n", + "# Prepare your data\n", + "processed_df = interpreter.prepare_data(input_df)\n", + "print(processed_df.shape)\n", + "print(processed_df.columns)\n", + "print(processed_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "54416630-d199-4f4c-b792-e58ed7f67310", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:variant_model_interpreter:Created SHAP explainer for xgboost model\n", + "INFO:variant_model_interpreter:SHAP values calculated successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 10.2 s, sys: 294 ms, total: 10.5 s\n", + "Wall time: 812 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "# Calculate SHAP values\n", + "interpreter.calculate_shap_values(processed_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ca6e2bc3-cd07-4e59-97c6-b3419d83172b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7.97 s, sys: 205 ms, total: 8.18 s\n", + "Wall time: 314 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "# Save SHAP JSON to custom location\n", + "shap_json = interpreter.run_shap_analysis(output_file='results/shap_analysis.json')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5682ab7f-bec3-4000-910f-30fb7d706d6a", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%time\n", + "# Save LIME JSON to custom location\n", + "lime_json = interpreter.run_lime_analysis(\n", + " processed_df=processed_df,\n", + " num_features = 5,\n", + " output_file='results/lime_analysis.json'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18ee2a07-f8b6-43ae-8c2d-cf4cc2fdb207", + "metadata": {}, + "outputs": [], + "source": [ + "break" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a4120b3e-b226-4385-8e53-7c1aabd05d52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "from bin import SHAP_to_JSON\n", + "importlib.reload(SHAP_to_JSON)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "afce815d-67a0-40f1-a91e-392241a0c2ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from bin import LIME_TO_JSON\n", + "importlib.reload(LIME_TO_JSON)" + ] + }, + { + "cell_type": "markdown", + "id": "4d4c5489-4b4c-464e-a5c8-750e2d39d58d", + "metadata": {}, + "source": [ + "# STOP HERE\n", + "### DEBUGGING/TESTING BELOW" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fe784ff8-65f2-4262-876e-222490cf7a68", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from lime import lime_tabular\n", + "import numpy as np\n", + "import pandas as pd\n", + "import json\n", + "\n", + "def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode=\"classification\"):\n", + " \"\"\"\n", + " Create a LIME explainer object\n", + " \"\"\"\n", + " if categorical_features is None:\n", + " categorical_features = []\n", + " \n", + " explainer = lime_tabular.LimeTabularExplainer(\n", + " training_data=np.array(X_train),\n", + " feature_names=feature_names,\n", + " class_names=class_names,\n", + " categorical_features=categorical_features,\n", + " mode=mode,\n", + " verbose=True,\n", + " random_state=42\n", + " )\n", + " \n", + " return explainer\n", + "\n", + "def create_lime_json(variant_ids, feature_names, processed_df, model, explainer, output_file=\"./lime_values.json\", num_features=20):\n", + " \"\"\"\n", + " Create JSON File from LIME explanations\n", + " \"\"\"\n", + " json_data = []\n", + " \n", + " # Get explanations for all instances\n", + " for idx, instance in enumerate(processed_df.values):\n", + " # Get explanation for this instance\n", + " if hasattr(model, 'predict_proba'):\n", + " explanation = explainer.explain_instance(\n", + " instance, \n", + " model.predict_proba,\n", + " num_features=num_features\n", + " )\n", + " else:\n", + " explanation = explainer.explain_instance(\n", + " instance, \n", + " model.predict,\n", + " num_features=num_features\n", + " )\n", + " \n", + " # Get the feature explanations as list of (feature, importance) tuples\n", + " exp_list = explanation.as_list()\n", + " \n", + " # Create entry for this instance\n", + " entry = {\n", + " \"variant_id\": variant_ids[idx],\n", + " \"base_value\": float(explanation.intercept), # Added intercept\n", + " \"confidence\": float(explanation.score), # Added confidence score\n", + " \"prediction_local\": float(explanation.local_pred), # Added local prediction\n", + " \"model_output_score\": {},\n", + " \"feature_values\": {}\n", + " }\n", + " \n", + " # Add feature contributions and values\n", + " for feature_name in feature_names:\n", + " # Find matching feature in explanation\n", + " feature_exp = next((x for x in exp_list if feature_name in x[0]), None)\n", + " \n", + " # Add contribution (0 if feature not in top features)\n", + " entry[\"model_output_score\"][feature_name] = float(feature_exp[1]) if feature_exp else 0.0\n", + " \n", + " # Add actual feature value\n", + " entry[\"feature_values\"][feature_name] = float(instance[feature_names.index(feature_name)])\n", + " \n", + " json_data.append(entry)\n", + " \n", + " # Convert to JSON string with proper formatting\n", + " json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python)\n", + " \n", + " # Save to file\n", + " with open(output_file, 'w') as f:\n", + " f.write(json_string)\n", + " \n", + " return json_string" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bad81388-859c-4686-960a-0c058ae3f36e", + "metadata": {}, + "outputs": [], + "source": [ + "model = interpreter.model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c1315bb9-e2db-4f9a-9c2a-7226132dfb4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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conservationScoreDGVgnomadAFgnomadAFgLRT_scoreLRT_OmegagnomadGeneZscoregnomadGenePLIgnomadGeneOELofUpperIMPACTCADD_PHRED...cons_splice_donor_region_variantIMPACT.from.TierTierADTierARTierAR.adjNo.Var.HMNo.Var.HNo.Var.MNo.Var.Lsimple_repeat
10-135085084-C-T1.00.0000190.0000190.0000000.0000000.380492.198300e-140.9263.024.600000...03.02.02.02.02.00.02.00.00
7-102281151-G-GA3.00.0000000.0000000.1263761.3419250.181701.130200e-011.8454.09.240707...04.01.03.03.01.01.00.00.00
6-17602826-G-T3.00.0000000.0000000.0000000.0000000.300171.747100e-020.6874.040.000000...04.01.01.01.04.02.02.00.00
18-42532220-G-A3.00.0000080.0000320.0000000.0000001.096501.000000e+000.1103.029.000000...03.02.03.03.01.00.01.00.00
23-39932684-A-G3.00.0000000.0000000.0000000.0000001.879701.000000e+000.1413.024.400000...03.02.03.03.01.00.01.00.00
..................................................................
6-32485548-AGTG-A1.00.0000280.0000000.1263761.3419250.606373.635900e-071.8491.09.240707...01.04.04.04.07.04.03.00.00
6-32485856-T-C1.00.0009500.0260430.0000000.0000000.606373.635900e-071.8494.031.000000...04.01.01.01.07.04.03.00.00
6-32485889-G-A1.00.0051050.0028470.0000000.0000000.606373.635900e-071.8491.05.533000...01.04.04.04.07.04.03.00.00
6-32487420-T-C1.00.0369300.1205430.3372121.3863300.606373.635900e-071.8493.09.694000...03.02.01.51.57.04.03.00.00
24-21154426-G-A3.00.0000000.0000000.0000000.000000-18.516001.860500e-1641.9951.010.920000...01.04.04.04.00.00.00.00.00
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2511 rows × 41 columns

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" + ], + "text/plain": [ + " conservationScoreDGV gnomadAF gnomadAFg LRT_score \\\n", + "10-135085084-C-T 1.0 0.000019 0.000019 0.000000 \n", + "7-102281151-G-GA 3.0 0.000000 0.000000 0.126376 \n", + "6-17602826-G-T 3.0 0.000000 0.000000 0.000000 \n", + "18-42532220-G-A 3.0 0.000008 0.000032 0.000000 \n", + "23-39932684-A-G 3.0 0.000000 0.000000 0.000000 \n", + "... ... ... ... ... \n", + "6-32485548-AGTG-A 1.0 0.000028 0.000000 0.126376 \n", + "6-32485856-T-C 1.0 0.000950 0.026043 0.000000 \n", + "6-32485889-G-A 1.0 0.005105 0.002847 0.000000 \n", + "6-32487420-T-C 1.0 0.036930 0.120543 0.337212 \n", + "24-21154426-G-A 3.0 0.000000 0.000000 0.000000 \n", + "\n", + " LRT_Omega gnomadGeneZscore gnomadGenePLI \\\n", + "10-135085084-C-T 0.000000 0.38049 2.198300e-14 \n", + "7-102281151-G-GA 1.341925 0.18170 1.130200e-01 \n", + "6-17602826-G-T 0.000000 0.30017 1.747100e-02 \n", + "18-42532220-G-A 0.000000 1.09650 1.000000e+00 \n", + "23-39932684-A-G 0.000000 1.87970 1.000000e+00 \n", + "... ... ... ... \n", + "6-32485548-AGTG-A 1.341925 0.60637 3.635900e-07 \n", + "6-32485856-T-C 0.000000 0.60637 3.635900e-07 \n", + "6-32485889-G-A 0.000000 0.60637 3.635900e-07 \n", + "6-32487420-T-C 1.386330 0.60637 3.635900e-07 \n", + "24-21154426-G-A 0.000000 -18.51600 1.860500e-164 \n", + "\n", + " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", + "10-135085084-C-T 0.926 3.0 24.600000 ... \n", + "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", + "6-17602826-G-T 0.687 4.0 40.000000 ... \n", + "18-42532220-G-A 0.110 3.0 29.000000 ... \n", + "23-39932684-A-G 0.141 3.0 24.400000 ... \n", + "... ... ... ... ... \n", + "6-32485548-AGTG-A 1.849 1.0 9.240707 ... \n", + "6-32485856-T-C 1.849 4.0 31.000000 ... \n", + "6-32485889-G-A 1.849 1.0 5.533000 ... \n", + "6-32487420-T-C 1.849 3.0 9.694000 ... \n", + "24-21154426-G-A 1.995 1.0 10.920000 ... \n", + "\n", + " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", + "10-135085084-C-T 0 3.0 2.0 \n", + "7-102281151-G-GA 0 4.0 1.0 \n", + "6-17602826-G-T 0 4.0 1.0 \n", + "18-42532220-G-A 0 3.0 2.0 \n", + "23-39932684-A-G 0 3.0 2.0 \n", + "... ... ... ... \n", + "6-32485548-AGTG-A 0 1.0 4.0 \n", + "6-32485856-T-C 0 4.0 1.0 \n", + "6-32485889-G-A 0 1.0 4.0 \n", + "6-32487420-T-C 0 3.0 2.0 \n", + "24-21154426-G-A 0 1.0 4.0 \n", + "\n", + " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M \\\n", + "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 \n", + "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 \n", + "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 \n", + "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 \n", + "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 \n", + "... ... ... ... ... ... \n", + "6-32485548-AGTG-A 4.0 4.0 7.0 4.0 3.0 \n", + "6-32485856-T-C 1.0 1.0 7.0 4.0 3.0 \n", + "6-32485889-G-A 4.0 4.0 7.0 4.0 3.0 \n", + "6-32487420-T-C 1.5 1.5 7.0 4.0 3.0 \n", + "24-21154426-G-A 4.0 4.0 0.0 0.0 0.0 \n", + "\n", + " No.Var.L simple_repeat \n", + "10-135085084-C-T 0.0 0 \n", + "7-102281151-G-GA 0.0 0 \n", + "6-17602826-G-T 0.0 0 \n", + "18-42532220-G-A 0.0 0 \n", + "23-39932684-A-G 0.0 0 \n", + "... ... ... \n", + "6-32485548-AGTG-A 0.0 0 \n", + "6-32485856-T-C 0.0 0 \n", + "6-32485889-G-A 0.0 0 \n", + "6-32487420-T-C 0.0 0 \n", + "24-21154426-G-A 0.0 0 \n", + "\n", + "[2511 rows x 41 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processed_df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cdf6763b-b355-418f-8f68-9b1f0587a7e0", + "metadata": {}, + "outputs": [], + "source": [ + "explainer = create_lime_explainer(\n", + " X_train=processed_df,\n", + " feature_names=processed_df.columns,\n", + " mode='classification', # or 'regression' depending on your task\n", + " class_names = ['is_not', 'is_causal']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "161ccab2-fde1-4982-93b9-2c6772b603e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explainer" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fd5a3dbf-cb7d-4609-a5e3-9dcd7a197688", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intercept -1.4518621776516535e-06\n", + "Prediction_local [1.5876037e-05]\n", + "Right: 0.00039676012\n" + ] + } + ], + "source": [ + "explanation = explainer.explain_instance(processed_df.values[0], model.predict_proba, num_features = 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7a9fdab6-0dbe-4a79-b3e9-a7bd67555245", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: -1.4518621776516535e-06}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explanation.intercept" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d70a0175-ef28-4b3e-9e89-7027cdea175f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.5876037e-05])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explanation.local_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b31bc13b-3838-4d78-a420-6cf45ff48cf9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explainer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb3cb60a-e4c4-4a89-9635-db908f8b36d5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0767273-c2a2-4042-9a9d-3e239fed48f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py b/bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py new file mode 100644 index 0000000..c33cbde --- /dev/null +++ b/bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py @@ -0,0 +1 @@ +from .variant_model_interpreter import ModelInterpreter \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf b/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf new file mode 100644 index 0000000..8537876 --- /dev/null +++ b/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf @@ -0,0 +1,36 @@ +// modules/model_interpreter/main.nf +process modelInterpreterShapPy { + cpus '48' + publishDir "./published${context.publishDirSuffix}" + + input: + path modelPath + path inputDataPath + val context + + output: + path 'shapOutputs/shap_values.json', emit: shapValuesPath + + """ + mkdir -p ./shapOutputs + python ${moduleDir}/main.py \ + --model-path ${modelPath} \ + --input-path ${inputDataPath} \ + --output-path ./shapOutputs/shap_values.json + """ +} + +workflow modelInterpreter { + take: + modelPath + inputDataPath + context + main: + modelInterpreterShapPy(modelPath, inputDataPath, context) + emit: + shapValuesPath = modelInterpreterShapPy.out.shapValuesPath +} + +workflow { + // modelInterpreter() +} \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py b/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py new file mode 100644 index 0000000..19cb41b --- /dev/null +++ b/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py @@ -0,0 +1,37 @@ +# modules/model_interpreter/run_shap.py +import argparse +import pandas as pd +from variant_model_interpreter import ModelInterpreter + +def parse_args(): + parser = argparse.ArgumentParser(description='Run SHAP analysis on model predictions') + parser.add_argument('--model-path', type=str, required=True, + help='Path to the trained model .joblib file (XGB preferred, RF is slow)') + parser.add_argument('--input-path', type=str, required=True, + help='Path to the input feature matrix file') + parser.add_argument('--output-path', type=str, required=True, + help='Path where to save the SHAP analysis JSON') + return parser.parse_args() + +def main(): + # Parse command line arguments + args = parse_args() + + # Initialize interpreter + interpreter = ModelInterpreter() + + # Load model + interpreter.load_model(args.model_path) + + # Load and prepare input data + input_df = pd.read_csv(args.input_path, index_col=0) + processed_df = interpreter.prepare_data(input_df) + + # Calculate SHAP values + interpreter.calculate_shap_values(processed_df) + + # Run SHAP analysis and save results + shap_json = interpreter.run_shap_analysis(output_file=args.output_path) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt b/bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt new file mode 100644 index 0000000..59b13e7 --- /dev/null +++ b/bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt @@ -0,0 +1,9 @@ +numpy>=1.19.2 +pandas>=1.2.0 +scikit-learn>=0.24.0 +xgboost>=1.4.0 +shap>=0.40.0 +matplotlib>=3.3.0 +seaborn>=0.11.0 +jinja2>=2.11.0 +weasyprint>=52.5 \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py b/bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py new file mode 100644 index 0000000..14b5c51 --- /dev/null +++ b/bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py @@ -0,0 +1,190 @@ +from bin import create_lime_explainer, create_lime_json, create_shap_json + +import joblib +import pandas as pd +import numpy as np +import shap +from lime import lime_tabular +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +import xgboost as xgb +from typing import Dict +import logging +import json +from typing import List, Optional, Union, Dict, Any + + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +class ModelInterpreter: + def __init__(self): + self.model = None + self.model_type = None + self.shap_explainer = None + self.shap_values = None + self.lime_explainer = None + self.model_features = None + self.is_model_loaded = False + self.variant_ids = None + self.feature_names = None + + def load_model(self, model_path: str) -> None: + """ + Load the model and extract feature names + """ + try: + self.model = joblib.load(model_path) + + # Extract feature names used during training + if isinstance(self.model, (RandomForestClassifier, RandomForestRegressor)): + self.model_type = "random_forest" + self.task_type = "classification" if isinstance(self.model, RandomForestClassifier) else "regression" + self.model_features = list(self.model.feature_names_in_) + elif isinstance(self.model, xgb.XGBModel): + self.model_type = "xgboost" + self.task_type = "classification" if self.model.objective.startswith('binary:') else "regression" + self.model_features = list(self.model.get_booster().feature_names) + else: + raise ValueError(f"Unsupported model type: {type(self.model)}") + + logger.info(f"Loaded model with {len(self.model_features)} features") + logger.info(f"Model features: {self.model_features}") + + self.is_model_loaded = True + + except Exception as e: + logger.error(f"Error loading model: {str(e)}") + raise + + def get_feature_differences(self, df: pd.DataFrame) -> Dict[str, set]: + """ + Utility method to show feature differences between input data and model + """ + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + df_features = set(df.columns) + model_features = set(self.model_features) + + return { + 'missing_features': model_features - df_features, + 'extra_features': df_features - model_features + } + + def prepare_data(self, df: pd.DataFrame) -> pd.DataFrame: + """ + Match feature names between model and feature dataframe + """ + try: + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + # Store variant IDs + self.variant_ids = list(df.index) + + # Get current features + df_features = df.columns + + # Find missing and extra features + missing_features = set(self.model_features) - set(df_features) + extra_features = set(df_features) - set(self.model_features) + + # Log the differences + logger.info(f"Missing features: {missing_features}") + logger.info(f"Extra features: {extra_features}") + + # Subset to model features + subset_df = df[self.model_features] + + # Store feature names + self.feature_names = self.model_features + + return subset_df + + except Exception as e: + logger.error(f"Error preparing data: {str(e)}") + raise + + def calculate_shap_values(self, df: pd.DataFrame) -> None: + """ + Calculate SHAP values with optimization for speed + """ + try: + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + if self.shap_explainer is None: + # Use approximate=True for faster computation + self.shap_explainer = shap.TreeExplainer( + self.model, + feature_perturbation='interventional', + approximate=True + ) + logger.info(f"Created SHAP explainer for {self.model_type} model") + + self.shap_values = self.shap_explainer(df) + logger.info("SHAP values calculated successfully") + + except Exception as e: + logger.error(f"Error calculating SHAP values: {str(e)}") + raise + + def run_shap_analysis(self, output_file: str = './results/shap_values.json') -> str: + """ + Generate SHAP analysis JSON + + Parameters: + ----------- + output_file : str, optional + Path where to save the JSON file (default: './shap_values.json') + """ + if self.shap_values is None: + raise ValueError("SHAP values not calculated. Run calculate_shap_values first.") + + return create_shap_json( + variant_ids=self.variant_ids, + feature_names=self.feature_names, + shap_values=self.shap_values, + output_file=output_file # Pass the output path + ) + + def run_lime_analysis(self, processed_df: pd.DataFrame, num_features = 20, + categorical_features: Optional[List[int]] = None, + output_file: str = './results/lime_values.json') -> str: + """ + Run LIME analysis on processed data + + Parameters: + ----------- + processed_df : pd.DataFrame + Processed feature matrix + categorical_features : List[int], optional + List of categorical feature indices + output_file : str, optional + Path where to save the JSON file + + Returns: + -------- + str + JSON string containing the LIME explanations + """ + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + if self.lime_explainer is None: + self.lime_explainer = create_lime_explainer( + X_train=processed_df.values, + feature_names=self.feature_names, + categorical_features=categorical_features + ) + + return create_lime_json( + variant_ids=self.variant_ids, + feature_names=self.feature_names, + processed_df=processed_df, + num_features = num_features, + model=self.model, + explainer=self.lime_explainer, + output_file=output_file + ) \ No newline at end of file diff --git a/bin/model_interpreter/__init__.py b/bin/model_interpreter/__init__.py new file mode 100644 index 0000000..c33cbde --- /dev/null +++ b/bin/model_interpreter/__init__.py @@ -0,0 +1 @@ +from .variant_model_interpreter import ModelInterpreter \ No newline at end of file diff --git a/bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py b/bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py new file mode 100644 index 0000000..0a9310d --- /dev/null +++ b/bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py @@ -0,0 +1,91 @@ +# Import necessary libraries +from lime import lime_tabular +import numpy as np +import pandas as pd +import json + +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils.numpy_to_python import convert_numpy_to_python + +def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode="classification"): + """ + Create a LIME explainer object + """ + if categorical_features is None: + categorical_features = [] + + explainer = lime_tabular.LimeTabularExplainer( + training_data=np.array(X_train), + feature_names=feature_names, + class_names=class_names, + categorical_features=categorical_features, + mode=mode, + verbose=False, + random_state=42 + ) + + return explainer + +def create_lime_json(variant_ids, feature_names, processed_df, model, explainer, output_file="./results/lime_values.json", num_features=20): + """ + Create JSON File from LIME explanations with preserved exact values + """ + json_data = [] + + # Get explanations for all instances + for idx, instance in enumerate(processed_df.values): + # Get explanation for this instance + explanation = explainer.explain_instance( + instance, + model.predict_proba, + num_features=num_features + ) + + # Get the feature explanations as list of (feature, importance) tuples + exp_list = explanation.as_list() + + # Handle intercept value (it's a dict with class label as key) + intercept_value = explanation.intercept[1] if isinstance(explanation.intercept, dict) else explanation.intercept + + # Handle local prediction (it's a numpy array) + local_pred = explanation.local_pred[0] if isinstance(explanation.local_pred, (list, np.ndarray)) else explanation.local_pred + + # Create entry for this instance + entry = { + "variant_id": variant_ids[idx], + "base_value": float(intercept_value), + "confidence": float(explanation.score), + "prediction_local": float(local_pred), + "model_output_score": {}, + "feature_values": {}, + "feature_explanations": [] # Add this to store the original explanation format + } + + # Store the original explanation format + for feature_text, importance in exp_list: + entry["feature_explanations"].append({ + "feature": feature_text, # This preserves the full text with conditions + "importance": float(importance) + }) + + # Extract just the feature name for the model_output_score + # Assuming format is "feature_name > value" or similar + feature_name = feature_text.split(' >')[0].strip() + entry["model_output_score"][feature_name] = float(importance) + + # Add actual feature values + for feature_name in feature_names: + entry["feature_values"][feature_name] = float(instance[feature_names.index(feature_name)]) + + json_data.append(entry) + + # Convert to JSON string with proper formatting + json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) + + # Save to file + with open(output_file, 'w') as f: + f.write(json_string) + + return json_string \ No newline at end of file diff --git a/bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py b/bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py new file mode 100644 index 0000000..f9ebef7 --- /dev/null +++ b/bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py @@ -0,0 +1,46 @@ +import joblib +import pandas as pd +import numpy as np +import shap +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +import xgboost as xgb +import json + +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils.numpy_to_python import convert_numpy_to_python + +def create_shap_json(variant_ids, feature_names, shap_values, output_file = "./results/shap_values.json"): + """ + Create JSON File from shap values generated from shap.TreeExplainer + Example Usage where processed_df is feature matrix: + variant_ids = list(processed_df.index) + feature_names = list(processed_df.columns) + json_output = create_shap_json(variant_ids, feature_names, shap_values) + """ + json_data = [] + + for idx in range(len(shap_values.values)): + entry = { + "variant_id": variant_ids[idx], + "base_value": float(shap_values.base_values[idx]), + "model_output_score": {}, + "feature_values": {} + } + + # Add feature contributions and values + for feat_idx, feat_name in enumerate(feature_names): + entry["model_output_score"][feat_name] = float(shap_values.values[idx][feat_idx]) + entry["feature_values"][feat_name] = float(shap_values.data[idx][feat_idx]) + + json_data.append(entry) + + # Convert to JSON string with proper formatting + json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) + + # Optionally save to file + with open(output_file, 'w') as f: + f.write(json_string) + + return json_string \ No newline at end of file diff --git a/bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py b/bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py new file mode 100644 index 0000000..8eccd63 --- /dev/null +++ b/bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py @@ -0,0 +1,2 @@ +from .SHAP_to_JSON import create_shap_json +from .LIME_TO_JSON import create_lime_explainer, create_lime_json \ No newline at end of file diff --git a/bin/model_interpreter/bin/LIME_TO_JSON.py b/bin/model_interpreter/bin/LIME_TO_JSON.py new file mode 100644 index 0000000..0a9310d --- /dev/null +++ b/bin/model_interpreter/bin/LIME_TO_JSON.py @@ -0,0 +1,91 @@ +# Import necessary libraries +from lime import lime_tabular +import numpy as np +import pandas as pd +import json + +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils.numpy_to_python import convert_numpy_to_python + +def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode="classification"): + """ + Create a LIME explainer object + """ + if categorical_features is None: + categorical_features = [] + + explainer = lime_tabular.LimeTabularExplainer( + training_data=np.array(X_train), + feature_names=feature_names, + class_names=class_names, + categorical_features=categorical_features, + mode=mode, + verbose=False, + random_state=42 + ) + + return explainer + +def create_lime_json(variant_ids, feature_names, processed_df, model, explainer, output_file="./results/lime_values.json", num_features=20): + """ + Create JSON File from LIME explanations with preserved exact values + """ + json_data = [] + + # Get explanations for all instances + for idx, instance in enumerate(processed_df.values): + # Get explanation for this instance + explanation = explainer.explain_instance( + instance, + model.predict_proba, + num_features=num_features + ) + + # Get the feature explanations as list of (feature, importance) tuples + exp_list = explanation.as_list() + + # Handle intercept value (it's a dict with class label as key) + intercept_value = explanation.intercept[1] if isinstance(explanation.intercept, dict) else explanation.intercept + + # Handle local prediction (it's a numpy array) + local_pred = explanation.local_pred[0] if isinstance(explanation.local_pred, (list, np.ndarray)) else explanation.local_pred + + # Create entry for this instance + entry = { + "variant_id": variant_ids[idx], + "base_value": float(intercept_value), + "confidence": float(explanation.score), + "prediction_local": float(local_pred), + "model_output_score": {}, + "feature_values": {}, + "feature_explanations": [] # Add this to store the original explanation format + } + + # Store the original explanation format + for feature_text, importance in exp_list: + entry["feature_explanations"].append({ + "feature": feature_text, # This preserves the full text with conditions + "importance": float(importance) + }) + + # Extract just the feature name for the model_output_score + # Assuming format is "feature_name > value" or similar + feature_name = feature_text.split(' >')[0].strip() + entry["model_output_score"][feature_name] = float(importance) + + # Add actual feature values + for feature_name in feature_names: + entry["feature_values"][feature_name] = float(instance[feature_names.index(feature_name)]) + + json_data.append(entry) + + # Convert to JSON string with proper formatting + json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) + + # Save to file + with open(output_file, 'w') as f: + f.write(json_string) + + return json_string \ No newline at end of file diff --git a/bin/model_interpreter/bin/SHAP_to_JSON.py b/bin/model_interpreter/bin/SHAP_to_JSON.py new file mode 100644 index 0000000..f9ebef7 --- /dev/null +++ b/bin/model_interpreter/bin/SHAP_to_JSON.py @@ -0,0 +1,46 @@ +import joblib +import pandas as pd +import numpy as np +import shap +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +import xgboost as xgb +import json + +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent.parent)) +from utils.numpy_to_python import convert_numpy_to_python + +def create_shap_json(variant_ids, feature_names, shap_values, output_file = "./results/shap_values.json"): + """ + Create JSON File from shap values generated from shap.TreeExplainer + Example Usage where processed_df is feature matrix: + variant_ids = list(processed_df.index) + feature_names = list(processed_df.columns) + json_output = create_shap_json(variant_ids, feature_names, shap_values) + """ + json_data = [] + + for idx in range(len(shap_values.values)): + entry = { + "variant_id": variant_ids[idx], + "base_value": float(shap_values.base_values[idx]), + "model_output_score": {}, + "feature_values": {} + } + + # Add feature contributions and values + for feat_idx, feat_name in enumerate(feature_names): + entry["model_output_score"][feat_name] = float(shap_values.values[idx][feat_idx]) + entry["feature_values"][feat_name] = float(shap_values.data[idx][feat_idx]) + + json_data.append(entry) + + # Convert to JSON string with proper formatting + json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) + + # Optionally save to file + with open(output_file, 'w') as f: + f.write(json_string) + + return json_string \ No newline at end of file diff --git a/bin/model_interpreter/bin/__init__.py b/bin/model_interpreter/bin/__init__.py new file mode 100644 index 0000000..8eccd63 --- /dev/null +++ b/bin/model_interpreter/bin/__init__.py @@ -0,0 +1,2 @@ +from .SHAP_to_JSON import create_shap_json +from .LIME_TO_JSON import create_lime_explainer, create_lime_json \ No newline at end of file diff --git a/bin/model_interpreter/example.ipynb b/bin/model_interpreter/example.ipynb new file mode 100644 index 0000000..aaea862 --- /dev/null +++ b/bin/model_interpreter/example.ipynb @@ -0,0 +1,1392 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1aa4b5e6-4e6e-451a-8496-3c13d3b8fe75", + "metadata": {}, + "outputs": [], + "source": [ + "from variant_model_interpreter import ModelInterpreter" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba16b3e6-b9a4-4ecb-a471-8b22fa7392f7", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0bf68cf0-92ae-4ca4-a8f0-14a57cce1e23", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:variant_model_interpreter:Loaded model with 41 features\n", + "INFO:variant_model_interpreter:Model features: ['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED', 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg', 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax', 'cons_transcript_ablation', 'cons_splice_acceptor_variant', 'cons_splice_donor_variant', 'cons_stop_gained', 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost', 'cons_transcript_amplification', 'cons_inframe_insertion', 'cons_inframe_deletion', 'cons_missense_variant', 'cons_protein_altering_variant', 'cons_splice_region_variant', 'cons_splice_donor_5th_base_variant', 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD', 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L', 'simple_repeat']\n", + "INFO:variant_model_interpreter:Missing features: set()\n", + "INFO:variant_model_interpreter:Extra features: {'GERPpp_RS', 'predict', 'clinvarNumB', 'isB/LB', 'curationScoreOMIM', 'c_HGMD_Exp_Del_to_Missense', 'CLASS', 'c_ClinVar_Exp_Different_pChange', 'ranking', 'clinvarNumLB', 'confidence', 'REVEL_score', 'FATHMM_score', 'omimSymptomSimScore', 'omimGeneFound', 'hgmdSymptomSimScore', 'SIFT_score', 'c_HGMD_Exp_Start_Loss', 'recessive', 'c_ClinVar_Exp_Del_to_Missense', 'hgmdVarFound', 'identifier', 'curationScoreHGMD', 'gnomadGeneOELof', 'CADD_phred', 'AR.matched', 'curationScoreClinVar', 'clinVarSymMatchFlag', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'isP/LP', 'nc_isBLB', 'hgmdGeneFound', 'dgvVarFound', 'omimVarFound', 'c_HGMD_Exp_Stop_Loss', 'phyloP100way_vertebrate', 'clinVarVarFound', 'clinvarNumLP', 'c_RANKSCORE', 'hgmd_rs', 'AD.matched', 'nc_ClinVar_Exp', 'c_isPLP', 'nc_HGMD_Exp', 'nc_RANKSCORE', 'clinVarGeneFound', 'hgmdSymptomScore', 'hgmdSymMatchFlag', 'fathmm_MKL_coding_score', 'max_ranking', 'c_ClinVar_Exp_Same_pChange', 'c_HGMD_Exp_Same_pChange', 'c_CLNREVSTAT', 'DANN_score', 'diffuse_Phrank_STRING', 'MutationAssessor_score', 'c_isBLB', 'clinvarNumP', 'confidence level', 'M_CAP_score', 'c_HGMD_Exp_Different_pChange', 'phrank', 'omimSymMatchFlag', 'min_ranking', 'nc_isPLP', 'decipherVarFound', 'dominant', 'nc_CLNREVSTAT'}\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2511, 41)\n", + "Index(['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score',\n", + " 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI',\n", + " 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED',\n", + " 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg',\n", + " 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax',\n", + " 'cons_transcript_ablation', 'cons_splice_acceptor_variant',\n", + " 'cons_splice_donor_variant', 'cons_stop_gained',\n", + " 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost',\n", + " 'cons_transcript_amplification', 'cons_inframe_insertion',\n", + " 'cons_inframe_deletion', 'cons_missense_variant',\n", + " 'cons_protein_altering_variant', 'cons_splice_region_variant',\n", + " 'cons_splice_donor_5th_base_variant',\n", + " 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD',\n", + " 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L',\n", + " 'simple_repeat'],\n", + " dtype='object')\n", + " conservationScoreDGV gnomadAF gnomadAFg LRT_score \\\n", + "10-135085084-C-T 1.0 0.000019 0.000019 0.000000 \n", + "7-102281151-G-GA 3.0 0.000000 0.000000 0.126376 \n", + "6-17602826-G-T 3.0 0.000000 0.000000 0.000000 \n", + "18-42532220-G-A 3.0 0.000008 0.000032 0.000000 \n", + "23-39932684-A-G 3.0 0.000000 0.000000 0.000000 \n", + "\n", + " LRT_Omega gnomadGeneZscore gnomadGenePLI \\\n", + "10-135085084-C-T 0.000000 0.38049 2.198300e-14 \n", + "7-102281151-G-GA 1.341925 0.18170 1.130200e-01 \n", + "6-17602826-G-T 0.000000 0.30017 1.747100e-02 \n", + "18-42532220-G-A 0.000000 1.09650 1.000000e+00 \n", + "23-39932684-A-G 0.000000 1.87970 1.000000e+00 \n", + "\n", + " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", + "10-135085084-C-T 0.926 3.0 24.600000 ... \n", + "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", + "6-17602826-G-T 0.687 4.0 40.000000 ... \n", + "18-42532220-G-A 0.110 3.0 29.000000 ... \n", + "23-39932684-A-G 0.141 3.0 24.400000 ... \n", + "\n", + " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", + "10-135085084-C-T 0 3.0 2.0 \n", + "7-102281151-G-GA 0 4.0 1.0 \n", + "6-17602826-G-T 0 4.0 1.0 \n", + "18-42532220-G-A 0 3.0 2.0 \n", + "23-39932684-A-G 0 3.0 2.0 \n", + "\n", + " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M No.Var.L \\\n", + "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 0.0 \n", + "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 0.0 \n", + "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 0.0 \n", + "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 0.0 \n", + "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 0.0 \n", + "\n", + " simple_repeat \n", + "10-135085084-C-T 0 \n", + "7-102281151-G-GA 0 \n", + "6-17602826-G-T 0 \n", + "18-42532220-G-A 0 \n", + "23-39932684-A-G 0 \n", + "\n", + "[5 rows x 41 columns]\n", + "CPU times: user 3.26 s, sys: 254 ms, total: 3.51 s\n", + "Wall time: 335 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "# Initialize interpreter\n", + "interpreter = ModelInterpreter()\n", + "\n", + "# Load model\n", + "interpreter.load_model(\"/home/johnathanj/model_xgb_sep2024_nd.job\")\n", + "\n", + "input_df = pd.read_csv('/home/johnathanj/projects/aim/explainable_AI/filtered_joint_2199195_SAMPLE_NAME_2199195_nd_predictions.csv', index_col = 0)\n", + "\n", + "# Prepare your data\n", + "processed_df = interpreter.prepare_data(input_df)\n", + "print(processed_df.shape)\n", + "print(processed_df.columns)\n", + "print(processed_df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "54416630-d199-4f4c-b792-e58ed7f67310", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:variant_model_interpreter:Created SHAP explainer for xgboost model\n", + "INFO:variant_model_interpreter:SHAP values calculated successfully\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 11.2 s, sys: 290 ms, total: 11.5 s\n", + "Wall time: 796 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "# Calculate SHAP values\n", + "interpreter.calculate_shap_values(processed_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ca6e2bc3-cd07-4e59-97c6-b3419d83172b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.17 s, sys: 169 ms, total: 8.34 s\n", + "Wall time: 320 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "# Save SHAP JSON to custom location\n", + "shap_json = interpreter.run_shap_analysis(output_file='results/shap_analysis.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5682ab7f-bec3-4000-910f-30fb7d706d6a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6h 12min 4s, sys: 7h 21min 39s, total: 13h 33min 43s\n", + "Wall time: 18min 13s\n" + ] + } + ], + "source": [ + "%%time\n", + "# Save LIME JSON to custom location\n", + "lime_json = interpreter.run_lime_analysis(\n", + " processed_df=processed_df,\n", + " num_features = 5,\n", + " output_file='results/lime_analysis.json'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18ee2a07-f8b6-43ae-8c2d-cf4cc2fdb207", + "metadata": {}, + "outputs": [], + "source": [ + "break" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a4120b3e-b226-4385-8e53-7c1aabd05d52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "from bin import SHAP_to_JSON\n", + "importlib.reload(SHAP_to_JSON)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "afce815d-67a0-40f1-a91e-392241a0c2ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from bin import LIME_TO_JSON\n", + "importlib.reload(LIME_TO_JSON)" + ] + }, + { + "cell_type": "markdown", + "id": "4d4c5489-4b4c-464e-a5c8-750e2d39d58d", + "metadata": {}, + "source": [ + "# STOP HERE\n", + "### DEBUGGING/TESTING BELOW" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fe784ff8-65f2-4262-876e-222490cf7a68", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from lime import lime_tabular\n", + "import numpy as np\n", + "import pandas as pd\n", + "import json\n", + "\n", + "def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode=\"classification\"):\n", + " \"\"\"\n", + " Create a LIME explainer object\n", + " \"\"\"\n", + " if categorical_features is None:\n", + " categorical_features = []\n", + " \n", + " explainer = lime_tabular.LimeTabularExplainer(\n", + " training_data=np.array(X_train),\n", + " feature_names=feature_names,\n", + " class_names=class_names,\n", + " categorical_features=categorical_features,\n", + " mode=mode,\n", + " verbose=True,\n", + " random_state=42\n", + " )\n", + " \n", + " return explainer\n", + "\n", + "def create_lime_json(variant_ids, feature_names, processed_df, model, explainer, output_file=\"./lime_values.json\", num_features=20):\n", + " \"\"\"\n", + " Create JSON File from LIME explanations\n", + " \"\"\"\n", + " json_data = []\n", + " \n", + " # Get explanations for all instances\n", + " for idx, instance in enumerate(processed_df.values):\n", + " # Get explanation for this instance\n", + " if hasattr(model, 'predict_proba'):\n", + " explanation = explainer.explain_instance(\n", + " instance, \n", + " model.predict_proba,\n", + " num_features=num_features\n", + " )\n", + " else:\n", + " explanation = explainer.explain_instance(\n", + " instance, \n", + " model.predict,\n", + " num_features=num_features\n", + " )\n", + " \n", + " # Get the feature explanations as list of (feature, importance) tuples\n", + " exp_list = explanation.as_list()\n", + " \n", + " # Create entry for this instance\n", + " entry = {\n", + " \"variant_id\": variant_ids[idx],\n", + " \"base_value\": float(explanation.intercept), # Added intercept\n", + " \"confidence\": float(explanation.score), # Added confidence score\n", + " \"prediction_local\": float(explanation.local_pred), # Added local prediction\n", + " \"model_output_score\": {},\n", + " \"feature_values\": {}\n", + " }\n", + " \n", + " # Add feature contributions and values\n", + " for feature_name in feature_names:\n", + " # Find matching feature in explanation\n", + " feature_exp = next((x for x in exp_list if feature_name in x[0]), None)\n", + " \n", + " # Add contribution (0 if feature not in top features)\n", + " entry[\"model_output_score\"][feature_name] = float(feature_exp[1]) if feature_exp else 0.0\n", + " \n", + " # Add actual feature value\n", + " entry[\"feature_values\"][feature_name] = float(instance[feature_names.index(feature_name)])\n", + " \n", + " json_data.append(entry)\n", + " \n", + " # Convert to JSON string with proper formatting\n", + " json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python)\n", + " \n", + " # Save to file\n", + " with open(output_file, 'w') as f:\n", + " f.write(json_string)\n", + " \n", + " return json_string" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bad81388-859c-4686-960a-0c058ae3f36e", + "metadata": {}, + "outputs": [], + "source": [ + "model = interpreter.model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c1315bb9-e2db-4f9a-9c2a-7226132dfb4d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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23-39932684-A-G3.00.0000000.0000000.0000000.0000001.879701.000000e+000.1413.024.400000...03.02.03.03.01.00.01.00.00
..................................................................
6-32485548-AGTG-A1.00.0000280.0000000.1263761.3419250.606373.635900e-071.8491.09.240707...01.04.04.04.07.04.03.00.00
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2511 rows × 41 columns

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"... ... ... ... \n", + "6-32485548-AGTG-A 1.341925 0.60637 3.635900e-07 \n", + "6-32485856-T-C 0.000000 0.60637 3.635900e-07 \n", + "6-32485889-G-A 0.000000 0.60637 3.635900e-07 \n", + "6-32487420-T-C 1.386330 0.60637 3.635900e-07 \n", + "24-21154426-G-A 0.000000 -18.51600 1.860500e-164 \n", + "\n", + " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", + "10-135085084-C-T 0.926 3.0 24.600000 ... \n", + "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", + "6-17602826-G-T 0.687 4.0 40.000000 ... \n", + "18-42532220-G-A 0.110 3.0 29.000000 ... \n", + "23-39932684-A-G 0.141 3.0 24.400000 ... \n", + "... ... ... ... ... \n", + "6-32485548-AGTG-A 1.849 1.0 9.240707 ... \n", + "6-32485856-T-C 1.849 4.0 31.000000 ... \n", + "6-32485889-G-A 1.849 1.0 5.533000 ... \n", + "6-32487420-T-C 1.849 3.0 9.694000 ... \n", + "24-21154426-G-A 1.995 1.0 10.920000 ... \n", + "\n", + " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", + "10-135085084-C-T 0 3.0 2.0 \n", + "7-102281151-G-GA 0 4.0 1.0 \n", + "6-17602826-G-T 0 4.0 1.0 \n", + "18-42532220-G-A 0 3.0 2.0 \n", + "23-39932684-A-G 0 3.0 2.0 \n", + "... ... ... ... \n", + "6-32485548-AGTG-A 0 1.0 4.0 \n", + "6-32485856-T-C 0 4.0 1.0 \n", + "6-32485889-G-A 0 1.0 4.0 \n", + "6-32487420-T-C 0 3.0 2.0 \n", + "24-21154426-G-A 0 1.0 4.0 \n", + "\n", + " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M \\\n", + "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 \n", + "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 \n", + "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 \n", + "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 \n", + "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 \n", + "... ... ... ... ... ... \n", + "6-32485548-AGTG-A 4.0 4.0 7.0 4.0 3.0 \n", + "6-32485856-T-C 1.0 1.0 7.0 4.0 3.0 \n", + "6-32485889-G-A 4.0 4.0 7.0 4.0 3.0 \n", + "6-32487420-T-C 1.5 1.5 7.0 4.0 3.0 \n", + "24-21154426-G-A 4.0 4.0 0.0 0.0 0.0 \n", + "\n", + " No.Var.L simple_repeat \n", + "10-135085084-C-T 0.0 0 \n", + "7-102281151-G-GA 0.0 0 \n", + "6-17602826-G-T 0.0 0 \n", + "18-42532220-G-A 0.0 0 \n", + "23-39932684-A-G 0.0 0 \n", + "... ... ... \n", + "6-32485548-AGTG-A 0.0 0 \n", + "6-32485856-T-C 0.0 0 \n", + "6-32485889-G-A 0.0 0 \n", + "6-32487420-T-C 0.0 0 \n", + "24-21154426-G-A 0.0 0 \n", + "\n", + "[2511 rows x 41 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "processed_df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cdf6763b-b355-418f-8f68-9b1f0587a7e0", + "metadata": {}, + "outputs": [], + "source": [ + "explainer = create_lime_explainer(\n", + " X_train=processed_df,\n", + " feature_names=processed_df.columns,\n", + " mode='classification', # or 'regression' depending on your task\n", + " class_names = ['is_not', 'is_causal']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "161ccab2-fde1-4982-93b9-2c6772b603e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explainer" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fd5a3dbf-cb7d-4609-a5e3-9dcd7a197688", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intercept -1.4518621776516535e-06\n", + "Prediction_local [1.5876037e-05]\n", + "Right: 0.00039676012\n" + ] + } + ], + "source": [ + "explanation = explainer.explain_instance(processed_df.values[0], model.predict_proba, num_features = 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7a9fdab6-0dbe-4a79-b3e9-a7bd67555245", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: -1.4518621776516535e-06}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explanation.intercept" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d70a0175-ef28-4b3e-9e89-7027cdea175f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.5876037e-05])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explanation.local_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b31bc13b-3838-4d78-a420-6cf45ff48cf9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "explainer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb3cb60a-e4c4-4a89-9635-db908f8b36d5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0767273-c2a2-4042-9a9d-3e239fed48f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bin/model_interpreter/main.nf b/bin/model_interpreter/main.nf new file mode 100644 index 0000000..64a6a4f --- /dev/null +++ b/bin/model_interpreter/main.nf @@ -0,0 +1,32 @@ +// modules/model_interpreter/main.nf +process modelInterpreterShapPy { + cpus '48' + publishDir "./published${context.publishDirSuffix}" + + input: + path modelPath + path inputDataPath + val context + + output: + path 'shapOutputs/shap_values.json', emit: shapValuesPath + + """ + mkdir -p ./shapOutputs + python ${moduleDir}/main.py \ + --model-path ${modelPath} \ + --input-path ${inputDataPath} \ + --output-path ./shapOutputs/shap_values.json + """ +} + +workflow modelInterpreter { + take: + modelPath + inputDataPath + context + main: + modelInterpreterShapPy(modelPath, inputDataPath, context) + emit: + shapValuesPath = modelInterpreterShapPy.out.shapValuesPath +} \ No newline at end of file diff --git a/bin/model_interpreter/main.py b/bin/model_interpreter/main.py new file mode 100644 index 0000000..a368b9b --- /dev/null +++ b/bin/model_interpreter/main.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 +# bin/model_interpreter/main.py + +import argparse +from variant_model_interpreter import ModelInterpreter +import pandas as pd + +def parse_args(): + parser = argparse.ArgumentParser(description='Run model interpretation (SHAP/LIME) analysis') + parser.add_argument('--analysis-type', type=str, default='both', # Set default to 'both' + choices=['shap', 'lime', 'both'], + help='Type of analysis to run (shap, lime, or both)') + parser.add_argument('--model-path', type=str, required=True, + help='Path to the trained model .job file') + parser.add_argument('--input-path', type=str, required=True, + help='Path to the input CSV file') + parser.add_argument('--output-path', type=str, required=True, + help='Path where to save the analysis JSON') + return parser.parse_args() + +def main(): + args = parse_args() + + # Initialize interpreter + interpreter = ModelInterpreter() + + # Load model + interpreter.load_model(args.model_path) + + # Load and prepare data + input_df = pd.read_csv(args.input_path, index_col=0) + processed_df = interpreter.prepare_data(input_df) + + # Run specified analysis + if args.analysis_type in ['shap', 'both']: + interpreter.calculate_shap_values(processed_df) + shap_output = args.output_path if args.analysis_type == 'shap' else args.output_path.replace('.json', '_shap.json') + interpreter.run_shap_analysis(output_file=shap_output) + + if args.analysis_type in ['lime', 'both']: + lime_output = args.output_path if args.analysis_type == 'lime' else args.output_path.replace('.json', '_lime.json') + interpreter.run_lime_analysis(processed_df, output_file=lime_output) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/bin/model_interpreter/results/lime_analysis.json b/bin/model_interpreter/results/lime_analysis.json new file mode 100644 index 0000000..e7a10b8 --- /dev/null +++ b/bin/model_interpreter/results/lime_analysis.json @@ -0,0 +1,195860 @@ +[ + { + "variant_id": "10-135085084-C-T", + "base_value": 0.001294243269921963, + "confidence": 0.006173459274502857, + "prediction_local": 0.0002548341544847479, + "model_output_score": { + "No.Var.L <= 0.00": -0.0007777347546341007, + "zyg <= 1.00": -0.0004272535032511538, + "simple_repeat <= 0.00": -0.0003072199055137973, + "spliceAImax": 0.0002474872715900633, + "CADD_PHRED": 0.00022531177637177355 + }, + "feature_values": { + "conservationScoreDGV": 1.0, + "gnomadAF": 1.864e-05, + "gnomadAFg": 1.864e-05, + "LRT_score": 0.0, + "LRT_Omega": 0.0, + "gnomadGeneZscore": 0.38049, + "gnomadGenePLI": 2.1983e-14, + "gnomadGeneOELofUpper": 0.926, + "IMPACT": 3.0, + "CADD_PHRED": 24.6, + 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0.00462119746953249, + "simple_repeat": 0.016668708994984627 + }, + "feature_values": { + "conservationScoreDGV": 1.0, + "gnomadAF": 0.005105, + "gnomadAFg": 0.00284707, + "LRT_score": 0.0, + "LRT_Omega": 0.0, + "gnomadGeneZscore": 0.60637, + "gnomadGenePLI": 3.6359e-07, + "gnomadGeneOELofUpper": 1.849, + "IMPACT": 1.0, + "CADD_PHRED": 5.533, + "conservationScoreGnomad": 2.0, + "conservationScoreOELof": 1.0, + "zyg": 2.0, + "ESP6500_AA_AF": 0.0, + "ESP6500_EA_AF": 0.0, + "hom": 0.0, + "spliceAImax": 0.0, + "cons_transcript_ablation": 0.0, + "cons_splice_acceptor_variant": 0.0, + "cons_splice_donor_variant": 0.0, + "cons_stop_gained": 0.0, + "cons_frameshift_variant": 0.0, + "cons_stop_lost": 0.0, + "cons_start_lost": 0.0, + "cons_transcript_amplification": 0.0, + "cons_inframe_insertion": 0.0, + "cons_inframe_deletion": 0.0, + "cons_missense_variant": 0.0, + "cons_protein_altering_variant": 0.0, + "cons_splice_region_variant": 0.0, + "cons_splice_donor_5th_base_variant": 0.0, + "cons_splice_donor_region_variant": 0.0, + "IMPACT.from.Tier": 1.0, + "TierAD": 4.0, + "TierAR": 4.0, + "TierAR.adj": 4.0, + "No.Var.HM": 7.0, + "No.Var.H": 4.0, + "No.Var.M": 3.0, + "No.Var.L": 0.0, + "simple_repeat": 0.0 + } + }, + { + "variant_id": "6-32487420-T-C", + "base_value": -10.256918907165527, + "model_output_score": { + "conservationScoreDGV": -0.06033186614513397, + "gnomadAF": -2.318244457244873, + "gnomadAFg": -1.721205472946167, + "LRT_score": -0.03588201478123665, + "LRT_Omega": -0.6578031182289124, + "gnomadGeneZscore": -0.23088906705379486, + "gnomadGenePLI": 0.22050103545188904, + "gnomadGeneOELofUpper": 0.19196084141731262, + "IMPACT": 0.2995164394378662, + "CADD_PHRED": -0.794739305973053, + "conservationScoreGnomad": 0.0, + "conservationScoreOELof": 0.0, + "zyg": 0.3303452730178833, + "ESP6500_AA_AF": 0.0, + "ESP6500_EA_AF": -0.0005441090906970203, + "hom": -2.7936060428619385, + "spliceAImax": 0.050390202552080154, + "cons_transcript_ablation": 0.0, + "cons_splice_acceptor_variant": 0.004777200985699892, + "cons_splice_donor_variant": 0.0015439316630363464, + "cons_stop_gained": 0.010039666667580605, + "cons_frameshift_variant": -0.01769348792731762, + "cons_stop_lost": 0.0, + "cons_start_lost": 0.0, + "cons_transcript_amplification": 0.0, + "cons_inframe_insertion": 0.0, + "cons_inframe_deletion": -0.0001818835735321045, + "cons_missense_variant": -0.006185370497405529, + "cons_protein_altering_variant": 0.0, + "cons_splice_region_variant": 0.008251354098320007, + "cons_splice_donor_5th_base_variant": 0.0, + "cons_splice_donor_region_variant": 0.0, + "IMPACT.from.Tier": 2.363917350769043, + "TierAD": 0.0, + "TierAR": 0.1267298460006714, + "TierAR.adj": 0.0, + "No.Var.HM": -0.04336792230606079, + "No.Var.H": -0.05897371098399162, + "No.Var.M": 0.012213566340506077, + "No.Var.L": 0.013540907762944698, + "simple_repeat": 0.0011542135616764426 + }, + "feature_values": { + "conservationScoreDGV": 1.0, + "gnomadAF": 0.03693, + "gnomadAFg": 0.120543, + "LRT_score": 0.337212, + "LRT_Omega": 1.38633, + "gnomadGeneZscore": 0.60637, + "gnomadGenePLI": 3.6359e-07, + "gnomadGeneOELofUpper": 1.849, + "IMPACT": 3.0, + "CADD_PHRED": 9.694, + "conservationScoreGnomad": 1.0, + "conservationScoreOELof": 1.0, + "zyg": 2.0, + "ESP6500_AA_AF": 0.0, + "ESP6500_EA_AF": 0.0, + "hom": 77.0, + "spliceAImax": 0.0, + "cons_transcript_ablation": 0.0, + "cons_splice_acceptor_variant": 0.0, + "cons_splice_donor_variant": 0.0, + "cons_stop_gained": 0.0, + "cons_frameshift_variant": 0.0, + "cons_stop_lost": 0.0, + "cons_start_lost": 0.0, + "cons_transcript_amplification": 0.0, + "cons_inframe_insertion": 0.0, + "cons_inframe_deletion": 0.0, + "cons_missense_variant": 1.0, + "cons_protein_altering_variant": 0.0, + "cons_splice_region_variant": 0.0, + "cons_splice_donor_5th_base_variant": 0.0, + "cons_splice_donor_region_variant": 0.0, + "IMPACT.from.Tier": 3.0, + "TierAD": 2.0, + "TierAR": 1.5, + "TierAR.adj": 1.5, + "No.Var.HM": 7.0, + "No.Var.H": 4.0, + "No.Var.M": 3.0, + "No.Var.L": 0.0, + "simple_repeat": 0.0 + } + }, + { + "variant_id": "24-21154426-G-A", + "base_value": -10.256918907165527, + "model_output_score": { + "conservationScoreDGV": 0.004759486764669418, + "gnomadAF": -0.40886542201042175, + "gnomadAFg": -0.10506249219179153, + "LRT_score": -0.19558551907539368, + "LRT_Omega": -0.18366000056266785, + "gnomadGeneZscore": -0.8151912093162537, + "gnomadGenePLI": -0.5185702443122864, + "gnomadGeneOELofUpper": -0.9614138007164001, + "IMPACT": -0.34536826610565186, + "CADD_PHRED": -2.3626279830932617, + "conservationScoreGnomad": 0.0, + "conservationScoreOELof": 0.0, + "zyg": -0.15922428667545319, + "ESP6500_AA_AF": 0.0, + "ESP6500_EA_AF": -7.89832483860664e-06, + "hom": 0.010412431322038174, + "spliceAImax": -1.5045604705810547, + "cons_transcript_ablation": 0.0, + "cons_splice_acceptor_variant": 0.005138596054166555, + "cons_splice_donor_variant": -0.00945999938994646, + "cons_stop_gained": 0.006892947014421225, + "cons_frameshift_variant": -0.015127758495509624, + "cons_stop_lost": 0.0, + "cons_start_lost": 0.0, + "cons_transcript_amplification": 0.0, + "cons_inframe_insertion": 0.0, + "cons_inframe_deletion": -0.0006365925073623657, + "cons_missense_variant": -0.059125833213329315, + "cons_protein_altering_variant": 0.0, + "cons_splice_region_variant": -0.11330326646566391, + "cons_splice_donor_5th_base_variant": 0.0, + "cons_splice_donor_region_variant": 0.0, + "IMPACT.from.Tier": -3.4541122913360596, + "TierAD": 0.0, + "TierAR": -0.03708724305033684, + "TierAR.adj": 0.0, + "No.Var.HM": -0.6974364519119263, + "No.Var.H": -0.03599334880709648, + "No.Var.M": -0.2082396000623703, + "No.Var.L": -0.0194710586220026, + "simple_repeat": 0.008274316787719727 + }, + "feature_values": { + "conservationScoreDGV": 3.0, + "gnomadAF": 0.0, + "gnomadAFg": 0.0, + "LRT_score": 0.0, + "LRT_Omega": 0.0, + "gnomadGeneZscore": -18.516, + "gnomadGenePLI": 1.8605e-164, + "gnomadGeneOELofUpper": 1.995, + "IMPACT": 1.0, + "CADD_PHRED": 10.92, + "conservationScoreGnomad": 2.0, + "conservationScoreOELof": 1.0, + "zyg": 1.0, + "ESP6500_AA_AF": 0.0, + "ESP6500_EA_AF": 0.0, + "hom": 0.0, + "spliceAImax": 0.0, + "cons_transcript_ablation": 0.0, + "cons_splice_acceptor_variant": 0.0, + "cons_splice_donor_variant": 0.0, + "cons_stop_gained": 0.0, + "cons_frameshift_variant": 0.0, + "cons_stop_lost": 0.0, + "cons_start_lost": 0.0, + "cons_transcript_amplification": 0.0, + "cons_inframe_insertion": 0.0, + "cons_inframe_deletion": 0.0, + "cons_missense_variant": 0.0, + "cons_protein_altering_variant": 0.0, + "cons_splice_region_variant": 0.0, + "cons_splice_donor_5th_base_variant": 0.0, + "cons_splice_donor_region_variant": 0.0, + "IMPACT.from.Tier": 1.0, + "TierAD": 4.0, + "TierAR": 4.0, + "TierAR.adj": 4.0, + "No.Var.HM": 0.0, + "No.Var.H": 0.0, + "No.Var.M": 0.0, + "No.Var.L": 0.0, + "simple_repeat": 0.0 + } + } +] \ No newline at end of file diff --git a/bin/model_interpreter/templates/genomic_variant_report_template.html b/bin/model_interpreter/templates/genomic_variant_report_template.html new file mode 100644 index 0000000..9a04fff --- /dev/null +++ b/bin/model_interpreter/templates/genomic_variant_report_template.html @@ -0,0 +1,163 @@ + + + + + Genomic Variant Analysis Report + + + +
+

Genomic Variant Analysis Report

+

Generated on: {{ timestamp }}

+
+ +
+
+

Analysis Overview

+

Model Type: {{ model_type }}

+

Total Variants Analyzed: {{ total_variants }}

+

Features Considered: {{ feature_count }}

+
+
+ +
+

Global Feature Importance

+ Feature Importance Plot +
+ +
+

SHAP Summary Plot

+ SHAP Summary Plot +
+ +
+

Individual Variant Analysis

+ {% for variant in variants %} +
+
+ Variant ID: {{ variant.variant_id }} +
+ +

Prediction: + + {{ "Pathogenic" if variant.prediction == 1 else "Benign" }} + + {% if variant.probability is not none %} + (Confidence: {{ "%.1f"|format(variant.probability * 100) }}%) + {% endif %} +

+ +

Feature Impacts:

+
+ Feature Impact Plot +
+ + + + + + + + {% for impact in variant.feature_impacts[:5] %} + + + + + + {% endfor %} +
FeatureImpactMagnitude
{{ impact.feature }} +
+
+
+
+ 		{{ "%.3f"|format(impact.magnitude) }}
+
+ {% endfor %} +
+ + \ No newline at end of file diff --git a/bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py b/bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py new file mode 100644 index 0000000..5f79acf --- /dev/null +++ b/bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py @@ -0,0 +1 @@ +from .numpy_to_python import convert_numpy_to_python \ No newline at end of file diff --git a/bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py b/bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py new file mode 100644 index 0000000..401fa8e --- /dev/null +++ b/bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py @@ -0,0 +1,16 @@ +import numpy as np + +def convert_numpy_to_python(obj): + """ + Convert Numpy to Python object for JSON storage + + Usage: for json.dumps + json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) + + """ + + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, np.float32): + return float(obj) + return obj diff --git a/bin/model_interpreter/utils/__init__.py b/bin/model_interpreter/utils/__init__.py new file mode 100644 index 0000000..5f79acf --- /dev/null +++ b/bin/model_interpreter/utils/__init__.py @@ -0,0 +1 @@ +from .numpy_to_python import convert_numpy_to_python \ No newline at end of file diff --git a/bin/model_interpreter/utils/numpy_to_python.py b/bin/model_interpreter/utils/numpy_to_python.py new file mode 100644 index 0000000..401fa8e --- /dev/null +++ b/bin/model_interpreter/utils/numpy_to_python.py @@ -0,0 +1,16 @@ +import numpy as np + +def convert_numpy_to_python(obj): + """ + Convert Numpy to Python object for JSON storage + + Usage: for json.dumps + json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) + + """ + + if isinstance(obj, np.ndarray): + return obj.tolist() + elif isinstance(obj, np.float32): + return float(obj) + return obj diff --git a/bin/model_interpreter/variant_model_interpreter.py b/bin/model_interpreter/variant_model_interpreter.py new file mode 100644 index 0000000..14b5c51 --- /dev/null +++ b/bin/model_interpreter/variant_model_interpreter.py @@ -0,0 +1,190 @@ +from bin import create_lime_explainer, create_lime_json, create_shap_json + +import joblib +import pandas as pd +import numpy as np +import shap +from lime import lime_tabular +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +import xgboost as xgb +from typing import Dict +import logging +import json +from typing import List, Optional, Union, Dict, Any + + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +class ModelInterpreter: + def __init__(self): + self.model = None + self.model_type = None + self.shap_explainer = None + self.shap_values = None + self.lime_explainer = None + self.model_features = None + self.is_model_loaded = False + self.variant_ids = None + self.feature_names = None + + def load_model(self, model_path: str) -> None: + """ + Load the model and extract feature names + """ + try: + self.model = joblib.load(model_path) + + # Extract feature names used during training + if isinstance(self.model, (RandomForestClassifier, RandomForestRegressor)): + self.model_type = "random_forest" + self.task_type = "classification" if isinstance(self.model, RandomForestClassifier) else "regression" + self.model_features = list(self.model.feature_names_in_) + elif isinstance(self.model, xgb.XGBModel): + self.model_type = "xgboost" + self.task_type = "classification" if self.model.objective.startswith('binary:') else "regression" + self.model_features = list(self.model.get_booster().feature_names) + else: + raise ValueError(f"Unsupported model type: {type(self.model)}") + + logger.info(f"Loaded model with {len(self.model_features)} features") + logger.info(f"Model features: {self.model_features}") + + self.is_model_loaded = True + + except Exception as e: + logger.error(f"Error loading model: {str(e)}") + raise + + def get_feature_differences(self, df: pd.DataFrame) -> Dict[str, set]: + """ + Utility method to show feature differences between input data and model + """ + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + df_features = set(df.columns) + model_features = set(self.model_features) + + return { + 'missing_features': model_features - df_features, + 'extra_features': df_features - model_features + } + + def prepare_data(self, df: pd.DataFrame) -> pd.DataFrame: + """ + Match feature names between model and feature dataframe + """ + try: + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + # Store variant IDs + self.variant_ids = list(df.index) + + # Get current features + df_features = df.columns + + # Find missing and extra features + missing_features = set(self.model_features) - set(df_features) + extra_features = set(df_features) - set(self.model_features) + + # Log the differences + logger.info(f"Missing features: {missing_features}") + logger.info(f"Extra features: {extra_features}") + + # Subset to model features + subset_df = df[self.model_features] + + # Store feature names + self.feature_names = self.model_features + + return subset_df + + except Exception as e: + logger.error(f"Error preparing data: {str(e)}") + raise + + def calculate_shap_values(self, df: pd.DataFrame) -> None: + """ + Calculate SHAP values with optimization for speed + """ + try: + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + if self.shap_explainer is None: + # Use approximate=True for faster computation + self.shap_explainer = shap.TreeExplainer( + self.model, + feature_perturbation='interventional', + approximate=True + ) + logger.info(f"Created SHAP explainer for {self.model_type} model") + + self.shap_values = self.shap_explainer(df) + logger.info("SHAP values calculated successfully") + + except Exception as e: + logger.error(f"Error calculating SHAP values: {str(e)}") + raise + + def run_shap_analysis(self, output_file: str = './results/shap_values.json') -> str: + """ + Generate SHAP analysis JSON + + Parameters: + ----------- + output_file : str, optional + Path where to save the JSON file (default: './shap_values.json') + """ + if self.shap_values is None: + raise ValueError("SHAP values not calculated. Run calculate_shap_values first.") + + return create_shap_json( + variant_ids=self.variant_ids, + feature_names=self.feature_names, + shap_values=self.shap_values, + output_file=output_file # Pass the output path + ) + + def run_lime_analysis(self, processed_df: pd.DataFrame, num_features = 20, + categorical_features: Optional[List[int]] = None, + output_file: str = './results/lime_values.json') -> str: + """ + Run LIME analysis on processed data + + Parameters: + ----------- + processed_df : pd.DataFrame + Processed feature matrix + categorical_features : List[int], optional + List of categorical feature indices + output_file : str, optional + Path where to save the JSON file + + Returns: + -------- + str + JSON string containing the LIME explanations + """ + if not self.is_model_loaded: + raise ValueError("Model not loaded. Please load model first.") + + if self.lime_explainer is None: + self.lime_explainer = create_lime_explainer( + X_train=processed_df.values, + feature_names=self.feature_names, + categorical_features=categorical_features + ) + + return create_lime_json( + variant_ids=self.variant_ids, + feature_names=self.feature_names, + processed_df=processed_df, + num_features = num_features, + model=self.model, + explainer=self.lime_explainer, + output_file=output_file + ) \ No newline at end of file diff --git a/main.nf b/main.nf index 6b10486..2aa9426 100644 --- a/main.nf +++ b/main.nf @@ -590,6 +590,35 @@ process PREDICTION { """ } +process MODEL_INTERPRETABILITY { + publishDir "${params.outdir}/${params.run_id}/model_interpretability/", mode: "copy" + + input: + path feature_matrix // Now this will be each CSV file that needs interpretation + path model_file // The model file + + output: + path "${feature_matrix.baseName}/shap_outputs/shap_values.json", emit: shap_results + path "${feature_matrix.baseName}/lime_outputs/lime_values.json", emit: lime_results + + script: + """ + mkdir -p "${feature_matrix.baseName}/shap_outputs" "${feature_matrix.baseName}/lime_outputs" "${feature_matrix.baseName}/outputs" + + echo "Analyzing predictions using feature matrix: ${feature_matrix}" + echo "Using model: ${model_file}" + + python ${projectDir}/bin/model_interpreter/main.py \ + --model-path ${model_file} \ + --input-path ${feature_matrix} \ + --output-path ${feature_matrix.baseName}/outputs/values.json + + mv ${feature_matrix.baseName}/outputs/*_shap.json ${feature_matrix.baseName}/shap_outputs/ + mv ${feature_matrix.baseName}/outputs/*_lime.json ${feature_matrix.baseName}/lime_outputs/ + """ +} + + workflow VCF_PRE_PROCESS { take: vcf @@ -723,4 +752,11 @@ workflow { file(params.ref_predict_new_dir), file(params.ref_model_inputs_dir) ) + + // Add Model Interpretability step - now with file paths from original + MODEL_INTERPRETABILITY( + PREDICTION.out.flatten().filter(~/.*\.csv$/), // Get ALL CSV files + file("${params.ref_predict_new_dir}/hg19/final_model_wo_bg_val.job") + ) + } From e21b5385ce474ce5e3aaddfd4b84db7c2fbe1fb3 Mon Sep 17 00:00:00 2001 From: jjia1 Date: Wed, 13 Nov 2024 02:41:53 -0600 Subject: [PATCH 02/80] model interpretability --- .github/workflows/docker.yml | 31 + .gitignore | 5 + README.md | 2 +- .../Untitled-checkpoint.ipynb | 1383 ----------------- .../.ipynb_checkpoints/__init__-checkpoint.py | 1 - .../.ipynb_checkpoints/main-checkpoint.nf | 36 - .../.ipynb_checkpoints/main-checkpoint.py | 37 - .../requirements-checkpoint.txt | 9 - .../variant_model_interpreter-checkpoint.py | 190 --- requirements.txt | 2 + utils/GRI_bed_filter/README.md | 13 + .../version1_gene-only/gri_v1.R | 252 +++ utils/hpo_update/hpo_update.r | 83 + utils/hpo_update/parseGeneMap2_output.py | 138 ++ 14 files changed, 525 insertions(+), 1657 deletions(-) create mode 100644 .github/workflows/docker.yml delete mode 100644 bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb delete mode 100644 bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py delete mode 100644 bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf delete mode 100644 bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py delete mode 100644 bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt delete mode 100644 bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py create mode 100644 utils/GRI_bed_filter/README.md create mode 100644 utils/GRI_bed_filter/version1_gene-only/gri_v1.R create mode 100644 utils/hpo_update/hpo_update.r create mode 100755 utils/hpo_update/parseGeneMap2_output.py diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml new file mode 100644 index 0000000..660b673 --- /dev/null +++ b/.github/workflows/docker.yml @@ -0,0 +1,31 @@ +name: Build Docker Image aim-lite and Push + +on: + push: + branches: + - main + - ci + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v1 + + - name: Log in to Docker Hub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKER_USERNAME }} + password: ${{ secrets.DOCKER_PASSWORD }} + + - name: Build and push Docker image + uses: docker/build-push-action@v2 + with: + context: . + push: true + tags: ${{ secrets.DOCKER_USERNAME }}/aim-lite:latest diff --git a/.gitignore b/.gitignore index 055eff2..9897a4e 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,7 @@ run/bedtools test_data/ test_ref/ test_output/ +*.ipynb_checkpoints/ # Nextflow Related .nextflow* @@ -17,3 +18,7 @@ out/ trace-*txt report-*html params.yaml + +# Ignore R history and session files +.Rhistory +.RData diff --git a/README.md b/README.md index 19c7f6a..110578e 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ In making prediction, it takes variant annotation from [MARRVEL](https://marrvel and generates **prediction score** + **confidence score** as output. -You can use AI-MARRVEL from our [website](https://ai.marrvel.org/) or follow the [documentation](https://ai-marrvel.readthedocs.io/en/latest/) to run locally. +You can use AI-MARRVEL from our [website](https://ai.marrvel.org/) or follow the [documentation](https://aimarrvel.readthedocs.io/en/main/) to run locally. :new: Our paper is now published in [NEJM AI](https://ai.nejm.org/doi/full/10.1056/AIoa2300009)! diff --git a/bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb deleted file mode 100644 index dd5b3af..0000000 --- a/bin/model_interpreter/.ipynb_checkpoints/Untitled-checkpoint.ipynb +++ /dev/null @@ -1,1383 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1aa4b5e6-4e6e-451a-8496-3c13d3b8fe75", - "metadata": {}, - "outputs": [], - "source": [ - "from variant_model_interpreter import ModelInterpreter" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ba16b3e6-b9a4-4ecb-a471-8b22fa7392f7", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0bf68cf0-92ae-4ca4-a8f0-14a57cce1e23", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:variant_model_interpreter:Loaded model with 41 features\n", - "INFO:variant_model_interpreter:Model features: ['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED', 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg', 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax', 'cons_transcript_ablation', 'cons_splice_acceptor_variant', 'cons_splice_donor_variant', 'cons_stop_gained', 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost', 'cons_transcript_amplification', 'cons_inframe_insertion', 'cons_inframe_deletion', 'cons_missense_variant', 'cons_protein_altering_variant', 'cons_splice_region_variant', 'cons_splice_donor_5th_base_variant', 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD', 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L', 'simple_repeat']\n", - "INFO:variant_model_interpreter:Missing features: set()\n", - "INFO:variant_model_interpreter:Extra features: {'identifier', 'confidence level', 'curationScoreHGMD', 'c_HGMD_Exp_Different_pChange', 'omimVarFound', 'nc_HGMD_Exp', 'M_CAP_score', 'isB/LB', 'max_ranking', 'c_CLNREVSTAT', 'c_isPLP', 'GERPpp_RS', 'nc_ClinVar_Exp', 'c_isBLB', 'Polyphen2_HDIV_score', 'phyloP100way_vertebrate', 'Polyphen2_HVAR_score', 'SIFT_score', 'clinvarNumB', 'hgmd_rs', 'REVEL_score', 'c_HGMD_Exp_Same_pChange', 'c_HGMD_Exp_Stop_Loss', 'recessive', 'ranking', 'hgmdVarFound', 'omimGeneFound', 'omimSymMatchFlag', 'nc_isBLB', 'c_ClinVar_Exp_Different_pChange', 'AR.matched', 'diffuse_Phrank_STRING', 'clinVarVarFound', 'hgmdSymMatchFlag', 'decipherVarFound', 'clinvarNumLB', 'nc_CLNREVSTAT', 'CLASS', 'DANN_score', 'c_HGMD_Exp_Del_to_Missense', 'CADD_phred', 'min_ranking', 'curationScoreOMIM', 'nc_isPLP', 'c_ClinVar_Exp_Same_pChange', 'hgmdSymptomScore', 'nc_RANKSCORE', 'isP/LP', 'predict', 'c_RANKSCORE', 'dgvVarFound', 'hgmdSymptomSimScore', 'clinVarSymMatchFlag', 'omimSymptomSimScore', 'c_ClinVar_Exp_Del_to_Missense', 'curationScoreClinVar', 'phrank', 'hgmdGeneFound', 'AD.matched', 'gnomadGeneOELof', 'clinvarNumLP', 'dominant', 'MutationAssessor_score', 'clinVarGeneFound', 'clinvarNumP', 'c_HGMD_Exp_Start_Loss', 'fathmm_MKL_coding_score', 'FATHMM_score', 'confidence'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2511, 41)\n", - "Index(['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score',\n", - " 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI',\n", - " 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED',\n", - " 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg',\n", - " 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax',\n", - " 'cons_transcript_ablation', 'cons_splice_acceptor_variant',\n", - " 'cons_splice_donor_variant', 'cons_stop_gained',\n", - " 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost',\n", - " 'cons_transcript_amplification', 'cons_inframe_insertion',\n", - " 'cons_inframe_deletion', 'cons_missense_variant',\n", - " 'cons_protein_altering_variant', 'cons_splice_region_variant',\n", - " 'cons_splice_donor_5th_base_variant',\n", - " 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD',\n", - " 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L',\n", - " 'simple_repeat'],\n", - " dtype='object')\n", - " conservationScoreDGV gnomadAF gnomadAFg LRT_score \\\n", - "10-135085084-C-T 1.0 0.000019 0.000019 0.000000 \n", - "7-102281151-G-GA 3.0 0.000000 0.000000 0.126376 \n", - "6-17602826-G-T 3.0 0.000000 0.000000 0.000000 \n", - "18-42532220-G-A 3.0 0.000008 0.000032 0.000000 \n", - "23-39932684-A-G 3.0 0.000000 0.000000 0.000000 \n", - "\n", - " LRT_Omega gnomadGeneZscore gnomadGenePLI \\\n", - "10-135085084-C-T 0.000000 0.38049 2.198300e-14 \n", - "7-102281151-G-GA 1.341925 0.18170 1.130200e-01 \n", - "6-17602826-G-T 0.000000 0.30017 1.747100e-02 \n", - "18-42532220-G-A 0.000000 1.09650 1.000000e+00 \n", - "23-39932684-A-G 0.000000 1.87970 1.000000e+00 \n", - "\n", - " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", - "10-135085084-C-T 0.926 3.0 24.600000 ... \n", - "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", - "6-17602826-G-T 0.687 4.0 40.000000 ... \n", - "18-42532220-G-A 0.110 3.0 29.000000 ... \n", - "23-39932684-A-G 0.141 3.0 24.400000 ... \n", - "\n", - " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", - "10-135085084-C-T 0 3.0 2.0 \n", - "7-102281151-G-GA 0 4.0 1.0 \n", - "6-17602826-G-T 0 4.0 1.0 \n", - "18-42532220-G-A 0 3.0 2.0 \n", - "23-39932684-A-G 0 3.0 2.0 \n", - "\n", - " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M No.Var.L \\\n", - "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 0.0 \n", - "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 0.0 \n", - "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 0.0 \n", - "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 0.0 \n", - "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 0.0 \n", - "\n", - " simple_repeat \n", - "10-135085084-C-T 0 \n", - "7-102281151-G-GA 0 \n", - "6-17602826-G-T 0 \n", - "18-42532220-G-A 0 \n", - "23-39932684-A-G 0 \n", - "\n", - "[5 rows x 41 columns]\n", - "CPU times: user 3.1 s, sys: 228 ms, total: 3.33 s\n", - "Wall time: 303 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Initialize interpreter\n", - "interpreter = ModelInterpreter()\n", - "\n", - "# Load model\n", - "interpreter.load_model(\"/home/johnathanj/model_xgb_sep2024_nd.job\")\n", - "\n", - "input_df = pd.read_csv('/home/johnathanj/projects/aim/explainable_AI/filtered_joint_2199195_SAMPLE_NAME_2199195_nd_predictions.csv', index_col = 0)\n", - "\n", - "# Prepare your data\n", - "processed_df = interpreter.prepare_data(input_df)\n", - "print(processed_df.shape)\n", - "print(processed_df.columns)\n", - "print(processed_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "54416630-d199-4f4c-b792-e58ed7f67310", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:variant_model_interpreter:Created SHAP explainer for xgboost model\n", - "INFO:variant_model_interpreter:SHAP values calculated successfully\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 10.2 s, sys: 294 ms, total: 10.5 s\n", - "Wall time: 812 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Calculate SHAP values\n", - "interpreter.calculate_shap_values(processed_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ca6e2bc3-cd07-4e59-97c6-b3419d83172b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.97 s, sys: 205 ms, total: 8.18 s\n", - "Wall time: 314 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Save SHAP JSON to custom location\n", - "shap_json = interpreter.run_shap_analysis(output_file='results/shap_analysis.json')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5682ab7f-bec3-4000-910f-30fb7d706d6a", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "%%time\n", - "# Save LIME JSON to custom location\n", - "lime_json = interpreter.run_lime_analysis(\n", - " processed_df=processed_df,\n", - " num_features = 5,\n", - " output_file='results/lime_analysis.json'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18ee2a07-f8b6-43ae-8c2d-cf4cc2fdb207", - "metadata": {}, - "outputs": [], - "source": [ - "break" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a4120b3e-b226-4385-8e53-7c1aabd05d52", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import importlib\n", - "from bin import SHAP_to_JSON\n", - "importlib.reload(SHAP_to_JSON)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "afce815d-67a0-40f1-a91e-392241a0c2ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bin import LIME_TO_JSON\n", - "importlib.reload(LIME_TO_JSON)" - ] - }, - { - "cell_type": "markdown", - "id": "4d4c5489-4b4c-464e-a5c8-750e2d39d58d", - "metadata": {}, - "source": [ - "# STOP HERE\n", - "### DEBUGGING/TESTING BELOW" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "fe784ff8-65f2-4262-876e-222490cf7a68", - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary libraries\n", - "from lime import lime_tabular\n", - "import numpy as np\n", - "import pandas as pd\n", - "import json\n", - "\n", - "def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode=\"classification\"):\n", - " \"\"\"\n", - " Create a LIME explainer object\n", - " \"\"\"\n", - " if categorical_features is None:\n", - " categorical_features = []\n", - " \n", - " explainer = lime_tabular.LimeTabularExplainer(\n", - " training_data=np.array(X_train),\n", - " feature_names=feature_names,\n", - " class_names=class_names,\n", - " categorical_features=categorical_features,\n", - " mode=mode,\n", - " verbose=True,\n", - " random_state=42\n", - " )\n", - " \n", - " return explainer\n", - "\n", - "def create_lime_json(variant_ids, feature_names, processed_df, model, explainer, output_file=\"./lime_values.json\", num_features=20):\n", - " \"\"\"\n", - " Create JSON File from LIME explanations\n", - " \"\"\"\n", - " json_data = []\n", - " \n", - " # Get explanations for all instances\n", - " for idx, instance in enumerate(processed_df.values):\n", - " # Get explanation for this instance\n", - " if hasattr(model, 'predict_proba'):\n", - " explanation = explainer.explain_instance(\n", - " instance, \n", - " model.predict_proba,\n", - " num_features=num_features\n", - " )\n", - " else:\n", - " explanation = explainer.explain_instance(\n", - " instance, \n", - " model.predict,\n", - " num_features=num_features\n", - " )\n", - " \n", - " # Get the feature explanations as list of (feature, importance) tuples\n", - " exp_list = explanation.as_list()\n", - " \n", - " # Create entry for this instance\n", - " entry = {\n", - " \"variant_id\": variant_ids[idx],\n", - " \"base_value\": float(explanation.intercept), # Added intercept\n", - " \"confidence\": float(explanation.score), # Added confidence score\n", - " \"prediction_local\": float(explanation.local_pred), # Added local prediction\n", - " \"model_output_score\": {},\n", - " \"feature_values\": {}\n", - " }\n", - " \n", - " # Add feature contributions and values\n", - " for feature_name in feature_names:\n", - " # Find matching feature in explanation\n", - " feature_exp = next((x for x in exp_list if feature_name in x[0]), None)\n", - " \n", - " # Add contribution (0 if feature not in top features)\n", - " entry[\"model_output_score\"][feature_name] = float(feature_exp[1]) if feature_exp else 0.0\n", - " \n", - " # Add actual feature value\n", - " entry[\"feature_values\"][feature_name] = float(instance[feature_names.index(feature_name)])\n", - " \n", - " json_data.append(entry)\n", - " \n", - " # Convert to JSON string with proper formatting\n", - " json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python)\n", - " \n", - " # Save to file\n", - " with open(output_file, 'w') as f:\n", - " f.write(json_string)\n", - " \n", - " return json_string" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bad81388-859c-4686-960a-0c058ae3f36e", - "metadata": {}, - "outputs": [], - "source": [ - "model = interpreter.model" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c1315bb9-e2db-4f9a-9c2a-7226132dfb4d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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conservationScoreDGVgnomadAFgnomadAFgLRT_scoreLRT_OmegagnomadGeneZscoregnomadGenePLIgnomadGeneOELofUpperIMPACTCADD_PHRED...cons_splice_donor_region_variantIMPACT.from.TierTierADTierARTierAR.adjNo.Var.HMNo.Var.HNo.Var.MNo.Var.Lsimple_repeat
10-135085084-C-T1.00.0000190.0000190.0000000.0000000.380492.198300e-140.9263.024.600000...03.02.02.02.02.00.02.00.00
7-102281151-G-GA3.00.0000000.0000000.1263761.3419250.181701.130200e-011.8454.09.240707...04.01.03.03.01.01.00.00.00
6-17602826-G-T3.00.0000000.0000000.0000000.0000000.300171.747100e-020.6874.040.000000...04.01.01.01.04.02.02.00.00
18-42532220-G-A3.00.0000080.0000320.0000000.0000001.096501.000000e+000.1103.029.000000...03.02.03.03.01.00.01.00.00
23-39932684-A-G3.00.0000000.0000000.0000000.0000001.879701.000000e+000.1413.024.400000...03.02.03.03.01.00.01.00.00
..................................................................
6-32485548-AGTG-A1.00.0000280.0000000.1263761.3419250.606373.635900e-071.8491.09.240707...01.04.04.04.07.04.03.00.00
6-32485856-T-C1.00.0009500.0260430.0000000.0000000.606373.635900e-071.8494.031.000000...04.01.01.01.07.04.03.00.00
6-32485889-G-A1.00.0051050.0028470.0000000.0000000.606373.635900e-071.8491.05.533000...01.04.04.04.07.04.03.00.00
6-32487420-T-C1.00.0369300.1205430.3372121.3863300.606373.635900e-071.8493.09.694000...03.02.01.51.57.04.03.00.00
24-21154426-G-A3.00.0000000.0000000.0000000.000000-18.516001.860500e-1641.9951.010.920000...01.04.04.04.00.00.00.00.00
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2511 rows × 41 columns

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" - ], - "text/plain": [ - " conservationScoreDGV gnomadAF gnomadAFg LRT_score \\\n", - "10-135085084-C-T 1.0 0.000019 0.000019 0.000000 \n", - "7-102281151-G-GA 3.0 0.000000 0.000000 0.126376 \n", - "6-17602826-G-T 3.0 0.000000 0.000000 0.000000 \n", - "18-42532220-G-A 3.0 0.000008 0.000032 0.000000 \n", - "23-39932684-A-G 3.0 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... \n", - "6-32485548-AGTG-A 1.0 0.000028 0.000000 0.126376 \n", - "6-32485856-T-C 1.0 0.000950 0.026043 0.000000 \n", - "6-32485889-G-A 1.0 0.005105 0.002847 0.000000 \n", - "6-32487420-T-C 1.0 0.036930 0.120543 0.337212 \n", - "24-21154426-G-A 3.0 0.000000 0.000000 0.000000 \n", - "\n", - " LRT_Omega gnomadGeneZscore gnomadGenePLI \\\n", - "10-135085084-C-T 0.000000 0.38049 2.198300e-14 \n", - "7-102281151-G-GA 1.341925 0.18170 1.130200e-01 \n", - "6-17602826-G-T 0.000000 0.30017 1.747100e-02 \n", - "18-42532220-G-A 0.000000 1.09650 1.000000e+00 \n", - "23-39932684-A-G 0.000000 1.87970 1.000000e+00 \n", - "... ... ... ... \n", - "6-32485548-AGTG-A 1.341925 0.60637 3.635900e-07 \n", - "6-32485856-T-C 0.000000 0.60637 3.635900e-07 \n", - "6-32485889-G-A 0.000000 0.60637 3.635900e-07 \n", - "6-32487420-T-C 1.386330 0.60637 3.635900e-07 \n", - "24-21154426-G-A 0.000000 -18.51600 1.860500e-164 \n", - "\n", - " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", - "10-135085084-C-T 0.926 3.0 24.600000 ... \n", - "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", - "6-17602826-G-T 0.687 4.0 40.000000 ... \n", - "18-42532220-G-A 0.110 3.0 29.000000 ... \n", - "23-39932684-A-G 0.141 3.0 24.400000 ... \n", - "... ... ... ... ... \n", - "6-32485548-AGTG-A 1.849 1.0 9.240707 ... \n", - "6-32485856-T-C 1.849 4.0 31.000000 ... \n", - "6-32485889-G-A 1.849 1.0 5.533000 ... \n", - "6-32487420-T-C 1.849 3.0 9.694000 ... \n", - "24-21154426-G-A 1.995 1.0 10.920000 ... \n", - "\n", - " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", - "10-135085084-C-T 0 3.0 2.0 \n", - "7-102281151-G-GA 0 4.0 1.0 \n", - "6-17602826-G-T 0 4.0 1.0 \n", - "18-42532220-G-A 0 3.0 2.0 \n", - "23-39932684-A-G 0 3.0 2.0 \n", - "... ... ... ... \n", - "6-32485548-AGTG-A 0 1.0 4.0 \n", - "6-32485856-T-C 0 4.0 1.0 \n", - "6-32485889-G-A 0 1.0 4.0 \n", - "6-32487420-T-C 0 3.0 2.0 \n", - "24-21154426-G-A 0 1.0 4.0 \n", - "\n", - " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M \\\n", - "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 \n", - "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 \n", - "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 \n", - "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 \n", - "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 \n", - "... ... ... ... ... ... \n", - "6-32485548-AGTG-A 4.0 4.0 7.0 4.0 3.0 \n", - "6-32485856-T-C 1.0 1.0 7.0 4.0 3.0 \n", - "6-32485889-G-A 4.0 4.0 7.0 4.0 3.0 \n", - "6-32487420-T-C 1.5 1.5 7.0 4.0 3.0 \n", - "24-21154426-G-A 4.0 4.0 0.0 0.0 0.0 \n", - "\n", - " No.Var.L simple_repeat \n", - "10-135085084-C-T 0.0 0 \n", - "7-102281151-G-GA 0.0 0 \n", - "6-17602826-G-T 0.0 0 \n", - "18-42532220-G-A 0.0 0 \n", - "23-39932684-A-G 0.0 0 \n", - "... ... ... \n", - "6-32485548-AGTG-A 0.0 0 \n", - "6-32485856-T-C 0.0 0 \n", - "6-32485889-G-A 0.0 0 \n", - "6-32487420-T-C 0.0 0 \n", - "24-21154426-G-A 0.0 0 \n", - "\n", - "[2511 rows x 41 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "processed_df" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "cdf6763b-b355-418f-8f68-9b1f0587a7e0", - "metadata": {}, - "outputs": [], - "source": [ - "explainer = create_lime_explainer(\n", - " X_train=processed_df,\n", - " feature_names=processed_df.columns,\n", - " mode='classification', # or 'regression' depending on your task\n", - " class_names = ['is_not', 'is_causal']\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "161ccab2-fde1-4982-93b9-2c6772b603e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explainer" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "fd5a3dbf-cb7d-4609-a5e3-9dcd7a197688", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intercept -1.4518621776516535e-06\n", - "Prediction_local [1.5876037e-05]\n", - "Right: 0.00039676012\n" - ] - } - ], - "source": [ - "explanation = explainer.explain_instance(processed_df.values[0], model.predict_proba, num_features = 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7a9fdab6-0dbe-4a79-b3e9-a7bd67555245", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: -1.4518621776516535e-06}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explanation.intercept" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d70a0175-ef28-4b3e-9e89-7027cdea175f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.5876037e-05])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explanation.local_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b31bc13b-3838-4d78-a420-6cf45ff48cf9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explainer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb3cb60a-e4c4-4a89-9635-db908f8b36d5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0767273-c2a2-4042-9a9d-3e239fed48f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py b/bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py deleted file mode 100644 index c33cbde..0000000 --- a/bin/model_interpreter/.ipynb_checkpoints/__init__-checkpoint.py +++ /dev/null @@ -1 +0,0 @@ -from .variant_model_interpreter import ModelInterpreter \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf b/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf deleted file mode 100644 index 8537876..0000000 --- a/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.nf +++ /dev/null @@ -1,36 +0,0 @@ -// modules/model_interpreter/main.nf -process modelInterpreterShapPy { - cpus '48' - publishDir "./published${context.publishDirSuffix}" - - input: - path modelPath - path inputDataPath - val context - - output: - path 'shapOutputs/shap_values.json', emit: shapValuesPath - - """ - mkdir -p ./shapOutputs - python ${moduleDir}/main.py \ - --model-path ${modelPath} \ - --input-path ${inputDataPath} \ - --output-path ./shapOutputs/shap_values.json - """ -} - -workflow modelInterpreter { - take: - modelPath - inputDataPath - context - main: - modelInterpreterShapPy(modelPath, inputDataPath, context) - emit: - shapValuesPath = modelInterpreterShapPy.out.shapValuesPath -} - -workflow { - // modelInterpreter() -} \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py b/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py deleted file mode 100644 index 19cb41b..0000000 --- a/bin/model_interpreter/.ipynb_checkpoints/main-checkpoint.py +++ /dev/null @@ -1,37 +0,0 @@ -# modules/model_interpreter/run_shap.py -import argparse -import pandas as pd -from variant_model_interpreter import ModelInterpreter - -def parse_args(): - parser = argparse.ArgumentParser(description='Run SHAP analysis on model predictions') - parser.add_argument('--model-path', type=str, required=True, - help='Path to the trained model .joblib file (XGB preferred, RF is slow)') - parser.add_argument('--input-path', type=str, required=True, - help='Path to the input feature matrix file') - parser.add_argument('--output-path', type=str, required=True, - help='Path where to save the SHAP analysis JSON') - return parser.parse_args() - -def main(): - # Parse command line arguments - args = parse_args() - - # Initialize interpreter - interpreter = ModelInterpreter() - - # Load model - interpreter.load_model(args.model_path) - - # Load and prepare input data - input_df = pd.read_csv(args.input_path, index_col=0) - processed_df = interpreter.prepare_data(input_df) - - # Calculate SHAP values - interpreter.calculate_shap_values(processed_df) - - # Run SHAP analysis and save results - shap_json = interpreter.run_shap_analysis(output_file=args.output_path) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt b/bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt deleted file mode 100644 index 59b13e7..0000000 --- a/bin/model_interpreter/.ipynb_checkpoints/requirements-checkpoint.txt +++ /dev/null @@ -1,9 +0,0 @@ -numpy>=1.19.2 -pandas>=1.2.0 -scikit-learn>=0.24.0 -xgboost>=1.4.0 -shap>=0.40.0 -matplotlib>=3.3.0 -seaborn>=0.11.0 -jinja2>=2.11.0 -weasyprint>=52.5 \ No newline at end of file diff --git a/bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py b/bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py deleted file mode 100644 index 14b5c51..0000000 --- a/bin/model_interpreter/.ipynb_checkpoints/variant_model_interpreter-checkpoint.py +++ /dev/null @@ -1,190 +0,0 @@ -from bin import create_lime_explainer, create_lime_json, create_shap_json - -import joblib -import pandas as pd -import numpy as np -import shap -from lime import lime_tabular -from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor -import xgboost as xgb -from typing import Dict -import logging -import json -from typing import List, Optional, Union, Dict, Any - - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -class ModelInterpreter: - def __init__(self): - self.model = None - self.model_type = None - self.shap_explainer = None - self.shap_values = None - self.lime_explainer = None - self.model_features = None - self.is_model_loaded = False - self.variant_ids = None - self.feature_names = None - - def load_model(self, model_path: str) -> None: - """ - Load the model and extract feature names - """ - try: - self.model = joblib.load(model_path) - - # Extract feature names used during training - if isinstance(self.model, (RandomForestClassifier, RandomForestRegressor)): - self.model_type = "random_forest" - self.task_type = "classification" if isinstance(self.model, RandomForestClassifier) else "regression" - self.model_features = list(self.model.feature_names_in_) - elif isinstance(self.model, xgb.XGBModel): - self.model_type = "xgboost" - self.task_type = "classification" if self.model.objective.startswith('binary:') else "regression" - self.model_features = list(self.model.get_booster().feature_names) - else: - raise ValueError(f"Unsupported model type: {type(self.model)}") - - logger.info(f"Loaded model with {len(self.model_features)} features") - logger.info(f"Model features: {self.model_features}") - - self.is_model_loaded = True - - except Exception as e: - logger.error(f"Error loading model: {str(e)}") - raise - - def get_feature_differences(self, df: pd.DataFrame) -> Dict[str, set]: - """ - Utility method to show feature differences between input data and model - """ - if not self.is_model_loaded: - raise ValueError("Model not loaded. Please load model first.") - - df_features = set(df.columns) - model_features = set(self.model_features) - - return { - 'missing_features': model_features - df_features, - 'extra_features': df_features - model_features - } - - def prepare_data(self, df: pd.DataFrame) -> pd.DataFrame: - """ - Match feature names between model and feature dataframe - """ - try: - if not self.is_model_loaded: - raise ValueError("Model not loaded. Please load model first.") - - # Store variant IDs - self.variant_ids = list(df.index) - - # Get current features - df_features = df.columns - - # Find missing and extra features - missing_features = set(self.model_features) - set(df_features) - extra_features = set(df_features) - set(self.model_features) - - # Log the differences - logger.info(f"Missing features: {missing_features}") - logger.info(f"Extra features: {extra_features}") - - # Subset to model features - subset_df = df[self.model_features] - - # Store feature names - self.feature_names = self.model_features - - return subset_df - - except Exception as e: - logger.error(f"Error preparing data: {str(e)}") - raise - - def calculate_shap_values(self, df: pd.DataFrame) -> None: - """ - Calculate SHAP values with optimization for speed - """ - try: - if not self.is_model_loaded: - raise ValueError("Model not loaded. Please load model first.") - - if self.shap_explainer is None: - # Use approximate=True for faster computation - self.shap_explainer = shap.TreeExplainer( - self.model, - feature_perturbation='interventional', - approximate=True - ) - logger.info(f"Created SHAP explainer for {self.model_type} model") - - self.shap_values = self.shap_explainer(df) - logger.info("SHAP values calculated successfully") - - except Exception as e: - logger.error(f"Error calculating SHAP values: {str(e)}") - raise - - def run_shap_analysis(self, output_file: str = './results/shap_values.json') -> str: - """ - Generate SHAP analysis JSON - - Parameters: - ----------- - output_file : str, optional - Path where to save the JSON file (default: './shap_values.json') - """ - if self.shap_values is None: - raise ValueError("SHAP values not calculated. Run calculate_shap_values first.") - - return create_shap_json( - variant_ids=self.variant_ids, - feature_names=self.feature_names, - shap_values=self.shap_values, - output_file=output_file # Pass the output path - ) - - def run_lime_analysis(self, processed_df: pd.DataFrame, num_features = 20, - categorical_features: Optional[List[int]] = None, - output_file: str = './results/lime_values.json') -> str: - """ - Run LIME analysis on processed data - - Parameters: - ----------- - processed_df : pd.DataFrame - Processed feature matrix - categorical_features : List[int], optional - List of categorical feature indices - output_file : str, optional - Path where to save the JSON file - - Returns: - -------- - str - JSON string containing the LIME explanations - """ - if not self.is_model_loaded: - raise ValueError("Model not loaded. Please load model first.") - - if self.lime_explainer is None: - self.lime_explainer = create_lime_explainer( - X_train=processed_df.values, - feature_names=self.feature_names, - categorical_features=categorical_features - ) - - return create_lime_json( - variant_ids=self.variant_ids, - feature_names=self.feature_names, - processed_df=processed_df, - num_features = num_features, - model=self.model, - explainer=self.lime_explainer, - output_file=output_file - ) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 4db1b60..9af4ba4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,3 +7,5 @@ matplotlib requests tables scikit-learn==1.1.2 +shap +lime diff --git a/utils/GRI_bed_filter/README.md b/utils/GRI_bed_filter/README.md new file mode 100644 index 0000000..7038380 --- /dev/null +++ b/utils/GRI_bed_filter/README.md @@ -0,0 +1,13 @@ +# Genomic Region of Interest filter +\ +\ + +##### Data files for generation: \ +1. Gencode v46 gene annotation \ +2. HGMD 2022 database \ +3. Clinvar 8/6/2024 database \ + +##### BED file description: \ +1. Version 1: BED \<--- protein_coding gene + HGMD mutations (50 flanking bp) + Cinvar mutations (50 flanking bp) \ +(Note: Promoter upstream 1kb is dropped as requested by Dr.Liu) + diff --git a/utils/GRI_bed_filter/version1_gene-only/gri_v1.R b/utils/GRI_bed_filter/version1_gene-only/gri_v1.R new file mode 100644 index 0000000..82c4625 --- /dev/null +++ b/utils/GRI_bed_filter/version1_gene-only/gri_v1.R @@ -0,0 +1,252 @@ +# BED region = protein_coding genes +# + HGMD mutations (50 flanking bp) +# + Cinvar mutations (50 flanking bp) +# +# gencode data: +# https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/gencode.v46.basic.annotation.gtf.gz +# https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/GRCh37_mapping/gencode.v46lift37.basic.annotation.gtf.gz +# +# HGMD data: +# HGMD_Pro_2022.2_hg38.vcf.gz HGMD_Pro_2022.2_hg19.vcf.gz from Hwan +# +# Clinvar data: +# https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/ +# https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/ + +library(VariantAnnotation) +library(BSgenome.Hsapiens.UCSC.hg38) +library(BSgenome.Hsapiens.UCSC.hg19) +library(httr) +library(dplyr) +library(readr) +library(data.table) + +# Function to generate GRI region based on genome version +generate_gri_geneonly <- function(genome_version = "hg38") { + # data preparation -------------------------------------------------------- + + ## hgmd local file name + hgmd_file = paste0("HGMD_Pro_2022.2_", genome_version, ".vcf.gz") + + ## use different data based on genome version + if (genome_version == "hg38") { + ##load chr size + hg38 <- BSgenome.Hsapiens.UCSC.hg38 + size <- seqlengths(hg38) + chr_size <- + data.frame(seqnames = names(size), chr_end = as.integer(size))[c(1:24),] + + ##load gencode data + url <- + "https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/gencode.v46.basic.annotation.gtf.gz" + destfile <- "gencode.gtf.gz" + GET(url, write_disk(destfile, overwrite = TRUE)) + gtf_data <- rtracklayer::import(destfile) + + ##load hgmd data + hgmd_data <- readVcf(hgmd_file, genome = "hg38") + + ##load clinvar data + url <- + "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz" + destfile <- "clinvar.vcf.gz" + GET(url, write_disk(destfile, overwrite = TRUE)) + clinvar_data <- readVcf(destfile, genome = "hg38") + + } else if (genome_version == "hg19") { + ##load chr size + hg19 <- BSgenome.Hsapiens.UCSC.hg19 + size <- seqlengths(hg19) + chr_size <- + data.frame(seqnames = names(size), chr_end = as.integer(size))[c(1:24),] + + ##load gencode data + url <- + "https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/GRCh37_mapping/gencode.v46lift37.basic.annotation.gtf.gz" + destfile <- "gencode.gtf.gz" + GET(url, write_disk(destfile, overwrite = TRUE)) + gtf_data <- rtracklayer::import(destfile) + + ##load hgmd data + hgmd_data <- readVcf(hgmd_file, genome = "hg19") + + ##load clinvar data + url <- + "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/clinvar.vcf.gz" + destfile <- "clinvar.vcf.gz" + GET(url, write_disk(destfile, overwrite = TRUE)) + clinvar_data <- readVcf(destfile, genome = "hg19") + } + + + ## function to check bed file coverage + coverage_bed <- function(bed_file, chromosome_size = chr_size) { + ## calculate hg chr bp length + seq.size <- sum(chromosome_size[, 2]) + ## calculate bed size + bed.size <- sum(bed_file[, 4]) + ## get percentage + size.per <- bed.size / seq.size + print(paste( + paste0("bed file coverage is: ", size.per * 100, "% ;"), + bed.size, + "bp out of", + seq.size, + "bp" + )) + } + + # 1. include gencode v46 annotation ---------------------------- + + ## convert gtf data to data.table and add chr size + gtf_dt <- as.data.table(gtf_data) + gtf_dt_size <- + merge(gtf_dt, chr_size, by = "seqnames") # no MT chr + + ## select protein coding genes + genes <- + gtf_dt_size[type == "gene" & gene_type == "protein_coding"] + + ## extract gene location info and save into bed format + ## add source column + bed_coding <- genes |> + dplyr::select( + chr = seqnames, + start = start, + end = end, + name = gene_name, + strand = strand, + ) |> + mutate(source = "GencodeV46.protein_coding") + + ## merge overlapped region + gr <- GRanges( + seqnames = bed_coding$chr, + ranges = IRanges(start = bed_coding$start, end = bed_coding$end) + ) + + merged_gr <- reduce(gr) + bed_coding.dt <- as.data.table(merged_gr) + coverage_bed(bed_coding.dt) + + # 2. include hgmd 2022 variants ----------------------------------------- + + ## extract fields and convert to data.table + info_dt <- as.data.table(info(hgmd_data)) + range_dt <- as.data.table(rowRanges(hgmd_data)) + + ## merged useful columns + hgmd <- cbind(range_dt, info_dt) |> + dplyr::select(seqnames, start, end, GENE, STRAND, CLASS, MUT) |> + mutate(source = "HGMD_2022") + + ## exclude "rejected variants" in HGMD database + hgmd_noR <- hgmd[-which(hgmd$CLASS == "R"),] + hgmd_noR <- hgmd_noR |> + dplyr::mutate(seqnames = paste0("chr", seqnames)) + + ## add 50bp flanking region + hgmd_noR <- merge(hgmd_noR, chr_size, by = "seqnames") + hgmd_noR_flank <- hgmd_noR |> + dplyr::mutate( + final_start = ifelse(start > 50, start - 50, 0), + final_end = ifelse((end + 50) > chr_end, chr_end, end + 50), + .after = seqnames + ) + + ## generate bed + bed_hgmd <- hgmd_noR_flank |> + dplyr::select( + chr = seqnames, + start = final_start, + end = final_end, + name = GENE, + strand = STRAND, + source + ) + + ## merge bed files from step1 and step2, and reduce them into non-overlapping regions + bed_gencode_hgmd <- rbind(bed_coding, bed_hgmd, fill = TRUE) |> + arrange(chr, start, end) + + gr <- GRanges( + seqnames = bed_gencode_hgmd$chr, + ranges = IRanges(start = bed_gencode_hgmd$start, end = bed_gencode_hgmd$end) + ) + + merged_gr <- reduce(gr) + bed_gencode_hgmd.dt <- as.data.table(merged_gr) + coverage_bed(bed_gencode_hgmd.dt) + + # 3. Include clinvar variants---------------------------------------- + + ## obtain pathogenic variants + info_df <- + as.data.frame(clinvar_data@info) #faster to load as dataframe than datatable + range_df <- as.data.frame(clinvar_data@rowRanges) + clinvar <- cbind(range_df, info_df) + clinvar_patho <- clinvar |> + filter(grepl("pathogenic", CLNSIG)) + + ## remove MT chromosome + ## rename seqnames + clinvar_patho <- clinvar_patho |> + filter(seqnames != "MT") + clinvar_patho <- clinvar_patho |> + mutate(seqnames = paste0("chr", seqnames)) + + ## add chromosome size column + clinvar_patho <- merge(clinvar_patho, chr_size, by = "seqnames") + + ## add flanking region + clinvar_patho_flank <- clinvar_patho |> + mutate( + final_start = ifelse(start > 50, start - 50, 0), + final_end = ifelse((end + 50) > chr_end, chr_end, end + 50), + .after = seqnames + ) + + ## generate bed + bed_clinvar <- clinvar_patho_flank |> + dplyr::select( + chr = seqnames, + start = final_start, + end = final_end, + name = GENEINFO, + strand = strand + ) |> + mutate(source = "Clinvar_latest") #the Clinvar link provides latest vcf file + + ## merge and reduce + bed_gencode_hgmd_clinvar <- + rbind(bed_gencode_hgmd, bed_clinvar) |> + arrange(chr, start, end) + + gr <- GRanges( + seqnames = bed_gencode_hgmd_clinvar$chr, + ranges = IRanges(start = bed_gencode_hgmd_clinvar$start, end = bed_gencode_hgmd_clinvar$end) + ) + + merged_gr <- reduce(gr) + bed_gencode_hgmd_clinvar.dt <- as.data.table(merged_gr) + + ## check bed coverage + coverage_bed(bed_gencode_hgmd_clinvar.dt) + + # write the bed file + file_name <- paste0("gene_only.", genome_version, ".bed") + write_tsv(bed_gencode_hgmd_clinvar.dt[, c(1:3)], + file = file_name, + col_names = FALSE) + + # save the initial bed files which are not reduced, which contains the source column to indicate where that region is from + file_name <- paste0("gene_only_source.", genome_version, ".bed") + write_tsv(bed_gencode_hgmd_clinvar, + file = file_name, + col_names = FALSE) + +} + +# Apply generate_gri_geneonly function to generate bed files -------------- +generate_gri_geneonly("hg19") +generate_gri_geneonly("hg38") diff --git a/utils/hpo_update/hpo_update.r b/utils/hpo_update/hpo_update.r new file mode 100644 index 0000000..e17f6b8 --- /dev/null +++ b/utils/hpo_update/hpo_update.r @@ -0,0 +1,83 @@ +#NOTE: Replace the download links with newest ones and then parse the files + +library(tidyverse) +library(httr) + +#download new hpo-related data files +hp_obo_file <- "https://github.com/obophenotype/human-phenotype-ontology/releases/download/v2024-08-13/hp.obo" +hpo_omim_file <- "https://github.com/obophenotype/human-phenotype-ontology/releases/download/v2024-08-13/phenotype.hpoa" +genemap2_file <- "https://data.omim.org/downloads/E8eFWaP3SOu67gXVTVKiGA/genemap2.txt" + +GET(hp_obo_file, write_disk("hp.obo", overwrite = TRUE)) +GET(hpo_omim_file, write_disk("phenotype.hpoa", overwrite = TRUE)) +GET(genemap2_file, write_disk("genemap2.txt", overwrite = TRUE)) + + +#read the new data file +df_new <- read_delim("phenotype.hpoa", delim = '\t', skip = 4) + +old_colnames <- c("OMIM_ID", + "HPO_ID", + "DiseaseName", + "Onset", + "Frequency", + "Sex", + "Aspect") + +#make the format of new data file same as the old one +df_new_omim <- df_new %>% + separate( + database_id, + into = c("database", "omim"), + sep = ":", + remove = FALSE + ) %>% + filter(database == "OMIM") + +df_new_omim_clean <- df_new_omim %>% + select(omim, hpo_id, disease_name, onset, frequency, sex, aspect) + +colnames(df_new_omim_clean) <- old_colnames +df_new_omim_clean$OMIM_ID <- as.numeric(df_new_omim_clean$OMIM_ID) + +#save the new HPO_OMIM.tsv +write_tsv(df_new_omim_clean, 'HPO_OMIM.tsv') + +#parse the genemap2 table +system("cat genemap2.txt | ./parseGeneMap2_output.py > genemap2_parsed.txt", + intern = FALSE) + +#organize the table +old_colnames <- c( + "MIM_Number", + "Pheno_ID", + "Phenotypes", + "Approved_Gene_Symbol", + "Gene_Symbols", + "Ensembl_Gene_ID", + "Entrez_Gene_ID", + "inheritance", + "Pheno_mapKey" +) + +gm2_new_parsed <- read_delim('genemap2_parsed.txt') +gm2_new_parsed_selected <- gm2_new_parsed |> + select( + MIM_Number, + Phenotype_MIM_Number, + Phenotype_Name, + Approved_Gene_Symbol, + Gene_Symbols, + Ensembl_Gene_ID, + Entrez_Gene_ID, + Inheritance, + Mapping_Key + ) +colnames(gm2_new_parsed_selected) <- old_colnames + +#save genemap2 data as rds +#the file name here is not changed, even though the newest data is from 2024 +saveRDS(gm2_new_parsed_selected, file = "genemap2_v2022.rds") + +#remove unnecessary file +file.remove("phenotype.hpoa", "genemap2.txt", "genemap2_parsed.txt") diff --git a/utils/hpo_update/parseGeneMap2_output.py b/utils/hpo_update/parseGeneMap2_output.py new file mode 100755 index 0000000..49d9e33 --- /dev/null +++ b/utils/hpo_update/parseGeneMap2_output.py @@ -0,0 +1,138 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# +# This script parses the genemap2.txt file from OMIM and outputs +# all fields along with processed phenotype information. +# Usage: cat genemap2.txt | ./parseGeneMap2.py > parsed.txt +# + +#originially from: https://github.com/OMIM-org/genemap2-parser/tree/master + +# Imports +import sys +import re + +# Define the expected number of fields in genemap2.txt +EXPECTED_NUM_FIELDS = 14 + +# Print header for the output file (TSV format) +header = [ + "Chromosome", + "Genomic_Position_Start", + "Genomic_Position_End", + "Cyto_Location", + "Computed_Cyto_Location", + "MIM_Number", + "Gene_Symbols", + "Gene_Name", + "Approved_Gene_Symbol", + "Entrez_Gene_ID", + "Ensembl_Gene_ID", + "Comments", + "Mouse", + "Phenotype_Name", + "Phenotype_MIM_Number", + "Mapping_Key", + "Inheritance" +] +print("\t".join(header)) + +# Read from stdin +for line_number, line in enumerate(sys.stdin, 1): + # Skip comments + if line.startswith('#'): + continue + + # Strip trailing new line and carriage return + line = line.strip('\n').strip('\r') + + # Split the line into fields based on tab + valueList = line.split('\t') + + # Validate the number of fields + if len(valueList) < EXPECTED_NUM_FIELDS: + sys.stderr.write(f"Warning: Line {line_number} has fewer fields ({len(valueList)}) than expected ({EXPECTED_NUM_FIELDS}). Skipping.\n") + continue # Skip malformed lines + + # Assign fields to variables + chromosome = valueList[0] + genomicPositionStart = valueList[1] + genomicPositionEnd = valueList[2] + cytoLocation = valueList[3] + computedCytoLocation = valueList[4] + mimNumber = valueList[5] + geneSymbols = valueList[6] + geneName = valueList[7] + approvedGeneSymbol = valueList[8] + entrezGeneID = valueList[9] + ensemblGeneID = valueList[10] + comments = valueList[11] + phenotypeString = valueList[12] + mouse = valueList[13] + + # Skip empty phenotypes + if not phenotypeString: + continue + + # Parse the phenotypes + phenotypes = phenotypeString.split(';') + for phenotype in phenotypes: + # Clean the phenotype string + phenotype = phenotype.strip() + + # Initialize phenotype-related variables + phenotype_name = "" + phenotype_mim_number = "" + phenotype_mapping_key = "" + inheritances = "" + + # Attempt to match the long phenotype pattern + matcher = re.match(r'^(.*),\s(\d{6})\s\((\d)\)(?:,\s*(.*))?$', phenotype) + if matcher: + phenotype_name = matcher.group(1).strip() + phenotype_mim_number = matcher.group(2).strip() + phenotype_mapping_key = matcher.group(3).strip() + inheritances_raw = matcher.group(4) + + if inheritances_raw: + inheritances = ','.join([inh.strip() for inh in inheritances_raw.split(',')]) + + else: + # Attempt to match the short phenotype pattern + matcher = re.match(r'^(.*)\((\d)\)(?:,\s*(.*))?$', phenotype) + if matcher: + phenotype_name = matcher.group(1).strip() + # phenotype_mim_number remains empty for short phenotypes + phenotype_mapping_key = matcher.group(2).strip() + inheritances_raw = matcher.group(3) + + if inheritances_raw: + inheritances = ','.join([inh.strip() for inh in inheritances_raw.split(',')]) + + # Prepare the output row + output_fields = [ + chromosome, + genomicPositionStart, + genomicPositionEnd, + cytoLocation, + computedCytoLocation, + mimNumber, + geneSymbols, + geneName, + approvedGeneSymbol, + entrezGeneID, + ensemblGeneID, + comments, + mouse, + phenotype_name, + phenotype_mim_number, + phenotype_mapping_key, + inheritances + ] + + # Replace any None or missing inheritances with empty string + output_fields = [field if field else "" for field in output_fields] + + # Join fields with tabs and print + print("\t".join(output_fields)) From fedaab9866f483e67c423d14ec9c741d269ec96c Mon Sep 17 00:00:00 2001 From: jjia1 Date: Wed, 13 Nov 2024 02:45:20 -0600 Subject: [PATCH 03/80] removed .ipynb checkpoints --- .gitignore | 2 +- .../LIME_TO_JSON-checkpoint.py | 91 ------------------- .../SHAP_to_JSON-checkpoint.py | 46 ---------- .../.ipynb_checkpoints/__init__-checkpoint.py | 2 - .../.ipynb_checkpoints/__init__-checkpoint.py | 1 - .../numpy_to_python-checkpoint.py | 16 ---- 6 files changed, 1 insertion(+), 157 deletions(-) delete mode 100644 bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py delete mode 100644 bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py delete mode 100644 bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py delete mode 100644 bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py delete mode 100644 bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py diff --git a/.gitignore b/.gitignore index 9897a4e..b6bf4db 100644 --- a/.gitignore +++ b/.gitignore @@ -8,7 +8,7 @@ run/bedtools test_data/ test_ref/ test_output/ -*.ipynb_checkpoints/ +**/.ipynb_checkpoints/ # Nextflow Related .nextflow* diff --git a/bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py b/bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py deleted file mode 100644 index 0a9310d..0000000 --- a/bin/model_interpreter/bin/.ipynb_checkpoints/LIME_TO_JSON-checkpoint.py +++ /dev/null @@ -1,91 +0,0 @@ -# Import necessary libraries -from lime import lime_tabular -import numpy as np -import pandas as pd -import json - -import sys -from pathlib import Path -sys.path.append(str(Path(__file__).parent.parent)) -from utils.numpy_to_python import convert_numpy_to_python - -def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode="classification"): - """ - Create a LIME explainer object - """ - if categorical_features is None: - categorical_features = [] - - explainer = lime_tabular.LimeTabularExplainer( - training_data=np.array(X_train), - feature_names=feature_names, - class_names=class_names, - categorical_features=categorical_features, - mode=mode, - verbose=False, - random_state=42 - ) - - return explainer - -def create_lime_json(variant_ids, feature_names, processed_df, model, explainer, output_file="./results/lime_values.json", num_features=20): - """ - Create JSON File from LIME explanations with preserved exact values - """ - json_data = [] - - # Get explanations for all instances - for idx, instance in enumerate(processed_df.values): - # Get explanation for this instance - explanation = explainer.explain_instance( - instance, - model.predict_proba, - num_features=num_features - ) - - # Get the feature explanations as list of (feature, importance) tuples - exp_list = explanation.as_list() - - # Handle intercept value (it's a dict with class label as key) - intercept_value = explanation.intercept[1] if isinstance(explanation.intercept, dict) else explanation.intercept - - # Handle local prediction (it's a numpy array) - local_pred = explanation.local_pred[0] if isinstance(explanation.local_pred, (list, np.ndarray)) else explanation.local_pred - - # Create entry for this instance - entry = { - "variant_id": variant_ids[idx], - "base_value": float(intercept_value), - "confidence": float(explanation.score), - "prediction_local": float(local_pred), - "model_output_score": {}, - "feature_values": {}, - "feature_explanations": [] # Add this to store the original explanation format - } - - # Store the original explanation format - for feature_text, importance in exp_list: - entry["feature_explanations"].append({ - "feature": feature_text, # This preserves the full text with conditions - "importance": float(importance) - }) - - # Extract just the feature name for the model_output_score - # Assuming format is "feature_name > value" or similar - feature_name = feature_text.split(' >')[0].strip() - entry["model_output_score"][feature_name] = float(importance) - - # Add actual feature values - for feature_name in feature_names: - entry["feature_values"][feature_name] = float(instance[feature_names.index(feature_name)]) - - json_data.append(entry) - - # Convert to JSON string with proper formatting - json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) - - # Save to file - with open(output_file, 'w') as f: - f.write(json_string) - - return json_string \ No newline at end of file diff --git a/bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py b/bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py deleted file mode 100644 index f9ebef7..0000000 --- a/bin/model_interpreter/bin/.ipynb_checkpoints/SHAP_to_JSON-checkpoint.py +++ /dev/null @@ -1,46 +0,0 @@ -import joblib -import pandas as pd -import numpy as np -import shap -from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor -import xgboost as xgb -import json - -import sys -from pathlib import Path -sys.path.append(str(Path(__file__).parent.parent)) -from utils.numpy_to_python import convert_numpy_to_python - -def create_shap_json(variant_ids, feature_names, shap_values, output_file = "./results/shap_values.json"): - """ - Create JSON File from shap values generated from shap.TreeExplainer - Example Usage where processed_df is feature matrix: - variant_ids = list(processed_df.index) - feature_names = list(processed_df.columns) - json_output = create_shap_json(variant_ids, feature_names, shap_values) - """ - json_data = [] - - for idx in range(len(shap_values.values)): - entry = { - "variant_id": variant_ids[idx], - "base_value": float(shap_values.base_values[idx]), - "model_output_score": {}, - "feature_values": {} - } - - # Add feature contributions and values - for feat_idx, feat_name in enumerate(feature_names): - entry["model_output_score"][feat_name] = float(shap_values.values[idx][feat_idx]) - entry["feature_values"][feat_name] = float(shap_values.data[idx][feat_idx]) - - json_data.append(entry) - - # Convert to JSON string with proper formatting - json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) - - # Optionally save to file - with open(output_file, 'w') as f: - f.write(json_string) - - return json_string \ No newline at end of file diff --git a/bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py b/bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py deleted file mode 100644 index 8eccd63..0000000 --- a/bin/model_interpreter/bin/.ipynb_checkpoints/__init__-checkpoint.py +++ /dev/null @@ -1,2 +0,0 @@ -from .SHAP_to_JSON import create_shap_json -from .LIME_TO_JSON import create_lime_explainer, create_lime_json \ No newline at end of file diff --git a/bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py b/bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py deleted file mode 100644 index 5f79acf..0000000 --- a/bin/model_interpreter/utils/.ipynb_checkpoints/__init__-checkpoint.py +++ /dev/null @@ -1 +0,0 @@ -from .numpy_to_python import convert_numpy_to_python \ No newline at end of file diff --git a/bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py b/bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py deleted file mode 100644 index 401fa8e..0000000 --- a/bin/model_interpreter/utils/.ipynb_checkpoints/numpy_to_python-checkpoint.py +++ /dev/null @@ -1,16 +0,0 @@ -import numpy as np - -def convert_numpy_to_python(obj): - """ - Convert Numpy to Python object for JSON storage - - Usage: for json.dumps - json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python) - - """ - - if isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, np.float32): - return float(obj) - return obj From d200244e3d4f9137cb9572140c765217972a6711 Mon Sep 17 00:00:00 2001 From: SpicyChicken6 <72659448+SpicyChicken6@users.noreply.github.com> Date: Wed, 13 Nov 2024 15:57:19 -0600 Subject: [PATCH 04/80] initialize hgmd branch --- utils/hgmd/readme.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 utils/hgmd/readme.txt diff --git a/utils/hgmd/readme.txt b/utils/hgmd/readme.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/utils/hgmd/readme.txt @@ -0,0 +1 @@ + From f205a2ff8d92cd8485d441f35b194766f16e4d5a Mon Sep 17 00:00:00 2001 From: Zhijian Yu Date: Wed, 13 Nov 2024 16:02:05 -0600 Subject: [PATCH 05/80] upload scripts for generating hgmd data files for merge_expand folder of data dependencies --- utils/hgmd/readme.txt | 1 - .../merge_expand/AA_abbreviations.txt | 23 + utils/hgmd_update/merge_expand/expandvar.R | 1192 +++++++++++++++++ utils/hgmd_update/merge_expand/setup.sh | 56 + 4 files changed, 1271 insertions(+), 1 deletion(-) delete mode 100644 utils/hgmd/readme.txt create mode 100755 utils/hgmd_update/merge_expand/AA_abbreviations.txt create mode 100644 utils/hgmd_update/merge_expand/expandvar.R create mode 100644 utils/hgmd_update/merge_expand/setup.sh diff --git a/utils/hgmd/readme.txt b/utils/hgmd/readme.txt deleted file mode 100644 index 8b13789..0000000 --- a/utils/hgmd/readme.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/utils/hgmd_update/merge_expand/AA_abbreviations.txt b/utils/hgmd_update/merge_expand/AA_abbreviations.txt new file mode 100755 index 0000000..03f1743 --- /dev/null +++ b/utils/hgmd_update/merge_expand/AA_abbreviations.txt @@ -0,0 +1,23 @@ +Full_Name ThreeLetter OneLetter +Alanine Ala A +Arginine Arg R +Asparagine Asn N +Aspartate Asp D +Aspartate/Asparagine Asx B +Cysteine Cys C +Glutamate Glu E +Glutamine Gln Q +Glutamate/Glutamine Glx Z +Glycine Gly G +Histidine His H +Isoleucine Ile I +Leucine Leu L +Lysine Lys K +Methionine Met M +Phenylalanine Phe F +Proline Pro P +Serine Ser S +Threonine Thr T +Tryptophan Trp W +Tyrosine Tyr Y +Valine Val V diff --git a/utils/hgmd_update/merge_expand/expandvar.R b/utils/hgmd_update/merge_expand/expandvar.R new file mode 100644 index 0000000..5fb0e74 --- /dev/null +++ b/utils/hgmd_update/merge_expand/expandvar.R @@ -0,0 +1,1192 @@ +# This script is used to expand the variants in the HGMD vcf file to gDNA level. +# (Originall written by @Dongxue Mao, reorganized by @Zhijian Yu) + + + +# Install and load libraries --------------------------------------------- +required_packages <- c("vcfR", "stringr", "stringi", "reshape2", + "tidyr", "plyr", "tidyr", "data.table", "tictoc") + +missing_packages <- required_packages[!(required_packages %in% + rownames(installed.packages()))] + +if(length(missing_packages)) { + install.packages(missing_packages) +} + +library(vcfR) +library(stringr) +library(stringi) +library(reshape2) +library(tidyr) +library(plyr) # could potentially be replaced by dplyr, needs testing +library("data.table") +library(tictoc) + + +# Prepare for data processing --------------------------------------------- + +# arg1: hgmd input vcf.gz +# arg2: out file directory +# arg3: reference file: AA_abbreviations.txt +# arg4: genome version + +## parse the args +args = commandArgs(trailingOnly = T) + +print("Printing out arguments;") +print(paste("args1:", args[1], sep = " ")) + +print(paste("args2:", args[2], sep = " ")) + +print(paste("args3:", args[3], sep = " ")) + +print(paste("args4:", args[4], sep = " ")) + +print("") + +genome_version <- args[4] + +#set working directory +setwd(args[2]) + +#create output folders +system("mkdir -p {Expand_Result,Expand_Result_final,TransVarInput,TransVarOut}") + + + +# Step 1 - ParseInfo_mc ---------------------------------------------------------------------------------------------------- + +## step 1 parse the infor in the hgmd vcf save as RDS +## step 2: dcast infor column and combine with other columns + +## parse the infor in the hgmd vcf save as RDS #### +print("Runnning Step 1 --- ParseInfo_mc") +tic("step1") + +# load the vcf +vcf <- read.vcfR(args[1], verbose = F) +vcf.df <- vcf@fix +vcf.df <- as.data.frame(vcf.df) + + +# seperate the info column to key, value +# rbind to long df +vcf_dt <- as.data.table(vcf.df) # Convert to data.table for efficiency + +# Split the INFO column into fields, handling ";" within quotes +vcf_dt[, fields := stri_split_regex(INFO, ';(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)', simplify = FALSE)] + +# Unnest the fields into long format +result_dt <- vcf_dt[, .(field = unlist(fields)), by = ID] + +# Split each field into key and value based on the first "=" using sub +result_dt[, key := sub("=.*", "", field)] +result_dt[, value := sub("^[^=]*=", "", field)] + +# Select and reorder the desired columns, including 'field' +results_df <- result_dt[, .(field, key, value, id = ID)] + +# Convert to data.frame +results_df <- as.data.frame(results_df) + +print("saving result") +saveRDS(results_df, file.path(args[2], "hgmd_mc.rds")) +toc() + +# Step 2 - Combine info ---------------------------------------------------------------------------------------------------- + +print("Runnning Step 2 --- CombineInfo") +tic("step2") + +## dcast infor column and combine with other columns #### +info.l <- readRDS(file.path(args[2], "hgmd_mc.rds")) +info.w <- reshape2::dcast(info.l, id ~ key, value.var = "value") + +# combine with other columns +vcf <- read.vcfR(args[1], verbose = FALSE) +vcf.df <- vcf@fix +vcf.df <- as.data.frame(vcf.df) + +vcf.all <- merge(vcf.df[, 1:7], + info.w, + by.x = "ID", + by.y = "id", + all = T) + +saveRDS(vcf.all, file.path(args[2], "hgmd_all.rds")) +toc() + + +# Step 3&4 - pro2genome ---------------------------------------------------------------------------------------------------- + +tic("step 3 and 4") +print("Runnning Step 3&4 --- pro2genome") + +# parse pro2g result from TransVar +vcf.all <- readRDS(file.path(args[2], "hgmd_all.rds")) # 280294 var + +# keep CLASS == "DM" and "DM?" +vcf.all <- vcf.all[vcf.all$CLASS %in% c("DM", "DM?"), ] # 268505 var +write.table( + vcf.all, + file.path(args[2], "hgmd_DM.txt"), + sep = "\t", + col.names = T, + row.names = F, + quote = F +) +saveRDS(vcf.all, file.path(args[2], "hgmd_DM.rds")) + +# load var in HGMD +hgmd.orig <- readRDS(file.path(args[2], "hgmd_DM.rds")) +hgmd <- hgmd.orig +# parse PROT column +hgmd$PROT2 <- gsub(".[0-9]+%3A", ":", hgmd$PROT) +hgmd$PROT2 <- gsub("%3B", ";", hgmd$PROT2) +hgmd$PROT2 <- gsub("%3D", "=", hgmd$PROT2) +hgmd$PROT2 <- gsub("\\(|\\)", "", hgmd$PROT2) + +## pre-filter the HGMD entries #### +# parse PROT column +hgmd.part <- hgmd[, c("ID", "GENE", "PROT", "PROT2")] + +# drop fs var (no expansion for fs) +hgmd.filt <- hgmd.part[!grepl("fs", hgmd.part$PROT), ] + +# drop var with multiple protein change (no expansion for them) (there are 48) +hgmd.filt <- hgmd.filt[!grepl(";", hgmd.filt$PROT2), ] + +# drop synonymous var (will parse as non-coding) +hgmd.filt <- hgmd.filt[!grepl("=", hgmd.filt$PROT2), ] + +# drop stop loss or stop gain (with * in the PROT) +hgmd.filt <- hgmd.filt[!grepl("\\*", hgmd.filt$PROT2), ] + +# drop inframe insertions and delins (ins and delins) +hgmd.filt <- hgmd.filt[!grepl("ins", hgmd.filt$PROT2), ] +hgmd.filt <- hgmd.filt[!grepl("dup", hgmd.filt$PROT2), ] + +# drop inframe deletions that affect more than 1 aa +del <- hgmd.filt[grepl("del", hgmd.filt$PROT2), ] +del <- separate( + data = del, + col = "PROT2", + into = c("NP_ID", "pChange"), + remove = F, + sep = ":" +) +del_multi_AA <- del[grepl("_", del$pChange), ] # dim 2698 +hgmd.filt <- hgmd.filt[!(hgmd.filt$ID %in% del_multi_AA$ID), ] + +hgmd.non.coding <- hgmd[!(hgmd$ID %in% hgmd.filt$ID), ] + +# check No of del with 2 AA del +del_multi_AA$pChange <- gsub("del", "", del_multi_AA$pChange) +del_multi_AA$pChange <- gsub("p\\.", "", del_multi_AA$pChange) +del_multi_AA <- separate( + data = del_multi_AA, + col = "pChange", + into = c("p_start", "p_end"), + remove = F, + sep = "_" +) +del_multi_AA$p_start <- str_extract(del_multi_AA$p_start, "[0-9]+$") +del_multi_AA$p_end <- str_extract(del_multi_AA$p_end, "[0-9]+$") +del_multi_AA$del_plength <- as.numeric(del_multi_AA$p_end) - as.numeric(del_multi_AA$p_start) + 1 + +# drop ones that are NA in PROT +hgmd.filt <- hgmd.filt[!is.na(hgmd.filt$PROT), ] + +# save hgmd.filt +write.table( + hgmd.filt, + file.path(args[2], "Expand_Result", "hgmd.filt.txt"), + sep = "\t", + col.names = T, + row.names = F, + quote = F +) +saveRDS(hgmd.filt, file.path(args[2], "Expand_Result", "hgmd.filt.rds")) + +## parse the single AA del #### +# Add alt of the AA (all possible change of change, except the ref) +del.1aa <- hgmd.filt[grepl("del", hgmd.filt$PROT2), ] +del.1aa <- separate( + data = del.1aa, + col = "PROT2", + into = c("NP_ID", "pChange"), + remove = F, + sep = ":" +) +del.1aa$pChange <- gsub("del", "", del.1aa$pChange) +del.1aa$pChange <- gsub("p\\.", "", del.1aa$pChange) +del.1aa$pLoc <- str_extract(del.1aa$pChange, "[0-9]+$") +del.1aa$pRef <- str_extract(del.1aa$pChange, "^[A-z]+") + +# load AA table +AA.table <- read.delim2(args[3], sep = "\t", header = T) +AA.filt <- AA.table[!grepl("\\/", AA.table$Full_Name), ] +AA.3letter <- AA.filt$ThreeLetter +AA.1letter <- AA.filt$OneLetter +# c('Ile','Phe','Lys','Tyr','Val','Glu','Met','Thr','Leu','Asp','Gly','Gln','Pro','Ser','His','Asn','Cys','Arg','Ala','Trp','Ter') + +del.1aa2 <- del.1aa[rep(seq_len(nrow(del.1aa)), each = 20), ] +del.1aa2$pAlt <- AA.3letter + +# remove Synonymous +del.1aa3 <- del.1aa2[del.1aa2$pRef != del.1aa2$pAlt, ] + +# format the expanded AA to PROT2 format +del.1aa3$PROT2_exp <- paste0(del.1aa3$NP_ID, + ":p.", + del.1aa3$pRef, + del.1aa3$pLoc, + del.1aa3$pAlt) + +##TransVar with del.1aa #### + +var4transVar.Del <- unique(del.1aa3$PROT2_exp) # 50998 var4transVar.Del +write.table( + var4transVar.Del, + file.path(args[2], "TransVarInput", "del_var.txt"), + quote = F, + col.names = F, + row.names = F +) + +# Warning from TransVar [_annotate_snv_protein] warning: unknown alternative: TER, ignore alternative. + + +## run TransVar step 1 #### +system( + paste( + "conda run -n hgmd transvar panno --refseq -l TransVarInput/del_var.txt --idmap protein_id --refversion", + genome_version, + "> TransVarOut/out_del_1aa.txt" + ), + intern = FALSE +) + +# load result +del2g.orig <- read.delim2( + file.path(args[2], "TransVarOut", "out_del_1aa.txt"), + header = T, + stringsAsFactors = F +) # 59567-2022version 80678-2024version +del2g <- del2g.orig[del2g.orig$transcript != ".", ] # 59147 + +# del2g <- del2g[grepl("candidate_codons=",del2g$info),] # 50711 +var <- unique(del2g$input) # 59147 +failed.del2g <- del.1aa3[!(del.1aa3$PROT2_exp %in% var) & + del.1aa3$pAlt != "Ter", ] + +length(unique(failed.del2g$ID)) + +# add the HGMD info +# 1. add the HGMD ID +del2g.wID <- merge(del2g, + del.1aa3[, c("PROT2_exp", "ID", "PROT2")], + by.x = "input", + by.y = "PROT2_exp", + all.x = T) + +# save the result +write.table( + del2g.wID, + file.path(args[2], "Expand_Result", "del2g.wID.txt"), + sep = "\t", + col.names = T, + row.names = F, + quote = F +) +saveRDS(del2g.wID, file.path(args[2], "Expand_Result", "del2g.wID.rds")) + + +##TransVar with NP #### + +# select var to run TransVar: non del +NP_df <- hgmd.filt[!(hgmd.filt$PROT2 %in% del.1aa3$PROT2), ] + +var4transVar.p <- unique(NP_df$PROT2) # 167983 +table(grepl("\\?", var4transVar.p)) # 807 T + +write.table( + var4transVar.p[!grepl("\\?", var4transVar.p)], + file.path(args[2], "TransVarInput", "NP_var.txt"), + quote = F, + col.names = F, + row.names = F +) + +NP_df_exp <- NP_df +NP_df_exp <- separate( + data = NP_df_exp, + col = "PROT2", + into = c("NP_ID", "pChange"), + remove = F, + sep = ":" +) +NP_df_exp$pChange <- gsub("p\\.", "", NP_df_exp$pChange) +NP_df_exp$pAlt <- str_extract(NP_df_exp$pChange, "([A-z]|\\?)+$") +NP_df_exp$pRef <- str_extract(NP_df_exp$pChange, "^[A-z]+") +NP_df_exp$pLoc <- str_extract(NP_df_exp$pChange, "[0-9]+") + +# AA in different format 1 letter or 3 letter +# parse them seperately + +NP_df_exp_3aa <- NP_df_exp[NP_df_exp$pAlt %in% AA.3letter, ] +NP_df_exp_1aa <- NP_df_exp[NP_df_exp$pAlt %in% LETTERS, ] +NP_df_exp_qMark <- NP_df_exp[NP_df_exp$pAlt == "?", ] # all start loss + +# 3 letters +NP_df_exp_3aa <- NP_df_exp_3aa[rep(seq_len(nrow(NP_df_exp_3aa)), each = 20), ] +NP_df_exp_3aa$pAlt <- AA.3letter +NP_df_exp_3aa <- NP_df_exp_3aa[NP_df_exp_3aa$pRef != NP_df_exp_3aa$pAlt, ] +NP_df_exp_3aa$PROT2_exp <- paste0( + NP_df_exp_3aa$NP_ID, + ":p.", + NP_df_exp_3aa$pRef, + NP_df_exp_3aa$pLoc, + NP_df_exp_3aa$pAlt +) + +# 1 letter +NP_df_exp_1aa <- NP_df_exp_1aa[rep(seq_len(nrow(NP_df_exp_1aa)), each = 20), ] +NP_df_exp_1aa$pAlt <- AA.1letter +NP_df_exp_1aa <- NP_df_exp_1aa[NP_df_exp_1aa$pRef != NP_df_exp_1aa$pAlt, ] +NP_df_exp_1aa$PROT2_exp <- paste0( + NP_df_exp_1aa$NP_ID, + ":p.", + NP_df_exp_1aa$pRef, + NP_df_exp_1aa$pLoc, + NP_df_exp_1aa$pAlt +) + +pRef <- as.data.frame(table(NP_df_exp_qMark$pRef)) +pRef <- pRef$Var1 +# check if all are start loss +length(setdiff(pRef, c("M", "Met"))) == 0 + +# rename pRef +NP_df_exp_qMark$pRef = "Met" +NP_df_exp_qMark <- NP_df_exp_qMark[rep(seq_len(nrow(NP_df_exp_qMark)), each = 19), ] +AA.nonStart = AA.3letter[AA.3letter != "Met"] + +NP_df_exp_qMark$pAlt <- AA.nonStart +NP_df_exp_qMark$PROT2_exp <- paste0( + NP_df_exp_qMark$NP_ID, + ":p.", + NP_df_exp_qMark$pRef, + NP_df_exp_qMark$pLoc, + NP_df_exp_qMark$pAlt +) + +NP_df_exp_final <- rbind(NP_df_exp_3aa, NP_df_exp_1aa, NP_df_exp_qMark) + +var4transVar.missense <- unique(c( + NP_df_exp_3aa$PROT2_exp, + NP_df_exp_1aa$PROT2_exp, + NP_df_exp_qMark$PROT2_exp +)) # 2186825 +write.table( + var4transVar.missense, + file.path(args[2], "TransVarInput", "NP_missense_var.txt"), + quote = F, + col.names = F, + row.names = F +) + +##run TransVar step 2 #### +system( + paste( + "conda run -n hgmd transvar panno -l TransVarInput/NP_var.txt --refseq --idmap protein_id --refversion", + genome_version, + "> TransVarOut/out_NP.txt" + ), + intern = FALSE +) + +system( + paste( + "conda run -n hgmd transvar panno -l TransVarInput/NP_missense_var.txt --refseq --idmap protein_id --refversion", + genome_version, + "> TransVarOut/out_NP_missense.txt" + ), + intern = FALSE +) + +## load transVar NP result #### +# NP +NP2g.orig <- read.delim2( + file.path(args[2], "TransVarOut", "out_NP.txt"), + header = T, + stringsAsFactors = F +) # 166544 +# write.table(NP2g.orig, file.path(args[2],"TransVarOut","out_NP.txt"), col.names = T, row.names = F, quote = F, sep = "\t") + +NP2g <- NP2g.orig[NP2g.orig$transcript != ".", ] # 165680 +NP2g <- NP2g[grepl("candidate_codons=", NP2g$info), ] # all failed are M1?, start loss, can be rescued in NP2g_exp + +# check failed +var <- unique(NP2g$input) # 164045 +# failed.NP.var <- var4transVar.p[!(var4transVar.p %in% var)] # 2303 + +# NP exp +NP2g_exp.orig <- fread( + file.path(args[2], "TransVarOut", "out_NP_missense.txt"), + header = T, + stringsAsFactors = F, + sep = "\t" +) # 137258 + +NP2g_exp <- NP2g_exp.orig[NP2g_exp.orig$transcript != ".", ] #2624793 +# NP2g_exp <- NP2g_exp[grepl("candidate_codons=",NP2g_exp$info),] # 2167881 + +# check failed +var_exp <- unique(NP2g_exp$input) # 2624793 + +NP2g_exp.wID <- merge( + NP2g_exp, + NP_df_exp_final[, c("PROT2_exp", "ID", "PROT2")], + by.x = "input", + by.y = "PROT2_exp", + all.x = T +) +write.table( + NP2g_exp.wID, + file.path(args[2], "Expand_Result", "NP2g_exp.wID.txt"), + sep = "\t", + col.names = T, + row.names = F, + quote = F +) +saveRDS(NP2g_exp.wID, + file.path(args[2], "Expand_Result", "NP2g_exp.wID.rds")) + + +## ADD hgmd ID to the NP2g result #### +NP2g.wID <- merge(NP2g, + hgmd.filt, + by.x = "input", + by.y = "PROT2", + all.x = T) # 137187 +# table(duplicated(NP2g.wID$input)) +# table(duplicated(NP2g.wID$ID)) +NP2g.wID$input_type <- "hgmd.NP_ID" +NP2g.wID$PROT2 <- NP2g.wID$input + +# save the result +write.table( + NP2g.wID, + file.path(args[2], "Expand_Result", "NP2g.wID.txt"), + sep = "\t", + col.names = T, + row.names = F, + quote = F +) +saveRDS(NP2g.wID, file.path(args[2], "Expand_Result", "NP2g.wID.rds")) + +## with gDNA #### + +## step 1: gDNA to protein change. #### +# most failed due to change in the Gene name/ transcript ID / protein location +# for those, run TransVar with gDNA -> pro change +# then run TransVar with pro change -> get the gDNA change + +var <- unique(c(NP2g.wID$input, del2g.wID$PROT2, NP2g_exp.wID$PROT2)) +failed.NP2g <- hgmd.filt[!(hgmd.filt$PROT2 %in% var), ] # 1524 +failed.ID <- unique(failed.NP2g$ID) + +# get the gDNA for the failed.NP2g (failed NP and Gene) +gDNA.failed.NP2g <- hgmd[hgmd$ID %in% failed.ID, c("ID", "CHROM", "POS", "REF", "ALT", "PROT2")] +gDNA.failed.NP2g$gDNA <- paste0( + "chr", + gDNA.failed.NP2g$CHROM, + ":g.", + gDNA.failed.NP2g$POS, + gDNA.failed.NP2g$REF, + ">", + gDNA.failed.NP2g$ALT +) + +write.table( + unique(gDNA.failed.NP2g$gDNA), + file.path(args[2], "TransVarInput", "gDNA_var.txt"), + quote = F, + col.names = F, + row.names = F +) + +## run TransVar step 3 #### +system( + paste( + "conda run -n hgmd transvar ganno -l TransVarInput/gDNA_var.txt --refseq --idmap protein_id --refversion", + genome_version, + "> TransVarOut/out_gDNA.txt" + ), + intern = FALSE +) + +# extract the ref and alt protein for future mapping +gDNA.failed.NP2g.part <- gDNA.failed.NP2g[, c("ID", "gDNA", "PROT2")] +gDNA.failed.NP2g.part <- separate( + data = gDNA.failed.NP2g.part, + col = "PROT2", + into = c("NP_ID", "pChange"), + remove = F, + sep = ":" +) +gDNA.failed.NP2g.part$pChange <- gsub("p\\.", "", gDNA.failed.NP2g.part$pChange) +gDNA.failed.NP2g.part$pAlt <- str_extract(gDNA.failed.NP2g.part$pChange, "([A-z]|\\?)+$") +gDNA.failed.NP2g.part$pRef <- str_extract(gDNA.failed.NP2g.part$pChange, "^[A-z]+") +gDNA.failed.NP2g.part$pLoc <- str_extract(gDNA.failed.NP2g.part$pChange, "[0-9]+") + +# replace 3 letter AA to 1 letter AA (TransVar using 1 letter AA) +gDNA.failed.NP2g.part$pAlt <- mapvalues(gDNA.failed.NP2g.part$pAlt, + from = AA.table$ThreeLetter, + to = AA.table$OneLetter) +gDNA.failed.NP2g.part$pRef <- mapvalues(gDNA.failed.NP2g.part$pRef, + from = AA.table$ThreeLetter, + to = AA.table$OneLetter) +# consider stop: U and Ter +gDNA.failed.NP2g.part$pRef <- mapvalues(gDNA.failed.NP2g.part$pRef, + from = c("U", "Ter"), + to = c("*", "*")) + +# load result +pDNA2pro.orig <- read.delim2( + file.path(args[2], "TransVarOut", "out_gDNA.txt"), + header = T, + stringsAsFactors = F +) +pDNA2pro <- pDNA2pro.orig[grepl("p.", pDNA2pro.orig$coordinates.gDNA.cDNA.protein.), c("input", "gene", "coordinates.gDNA.cDNA.protein.", "info")] + +# parse to info and extract the aliases(NP ID) +# vectorized transformation +library(dplyr) +info.df <- pDNA2pro |> + mutate(original_info = as.character(info)) |> + separate_rows(info, sep = ";") |> + rename(fields = info) |> + separate( + fields, + into = c("key", "value"), + sep = "=", + fill = "right", + extra = "merge", + # Merge any additional splits into 'value' + remove = FALSE # Retain the original 'fields' column + ) |> + rename(coordinates = coordinates.gDNA.cDNA.protein.) |> + select(fields, key, value, original_info, coordinates, input) |> + rename(info = original_info) +library(plyr) + +info.w.orig <- reshape2::dcast( + info.df, + formula = input + coordinates + info ~ key, + value.var = "value", + fill = NULL +) +info.w <- info.w.orig[, c("input", "coordinates", "aliases")] +info.w <- unique(info.w) +info.w <- info.w %>% + separate( + col = "coordinates", + c("gDNA", "cDNA", "protein"), + sep = "/", + remove = T + ) +info.w <- info.w[, c("input", "aliases", "protein")] +colnames(info.w) <- c("input", "TransVar_NP_ID", "TransVar_pChange") +# extract TransVar_pRef, TransVar_pAlt +info.w$TransVar_pChange <- gsub("p\\.", "", info.w$TransVar_pChange) +info.w$TransVar_pAlt <- str_extract(info.w$TransVar_pChange, "([A-z]|\\?|\\*)+$") +info.w$TransVar_pRef <- str_extract(info.w$TransVar_pChange, "^[A-z]+|\\*") +info.w$TransVar_pLoc <- str_extract(info.w$TransVar_pChange, "[0-9]+") + +# compare with original HGMD pRef, pAlt +pDNA2pro.wID <- merge( + gDNA.failed.NP2g.part, + info.w, + by.x = "gDNA", + by.y = "input", + all.y = T +) +pDNA2pro.wID.all <- merge( + gDNA.failed.NP2g.part, + info.w, + by.x = "gDNA", + by.y = "input", + all = T +) +ID.failed <- gDNA.failed.NP2g.part$ID[!(gDNA.failed.NP2g.part$gDNA %in% info.w$input)] # 150 +# keep the entries where both pRef and pAlt matches + +# Some of the cases are difficult +# 1. inframe del --- parse separately +# 2. start loss --- parse separately +# 3. reversed p ref/alt from HGMD (most likely error from hgmd) handled by the for loop bellow + +pDNA2pro.wID.selected <- data.frame() + +# Vectorized Transformation with dplyr +library(dplyr) + +pDNA2pro.wID.selected <- pDNA2pro.wID %>% + mutate( + priority = case_when( + pRef == TransVar_pRef & pAlt == TransVar_pAlt ~ 1, + pAlt == TransVar_pRef & pRef == TransVar_pAlt ~ 2, + pRef == TransVar_pRef & + paste0(pAlt, pRef) == TransVar_pAlt ~ 3, + pRef == TransVar_pRef & pRef == "M" & pLoc == "1" ~ 4, + TRUE ~ NA_real_ + ) + ) %>% + filter(!is.na(priority)) %>% + group_by(ID) %>% + slice(1) %>% + select(-priority) |> + ungroup() + +# reload plyr to mask functions for compatability +library(plyr) + +# pDNA2pro.failed <- gDNA.failed.NP2g.part[gDNA.failed.NP2g.part$ID %in% ID.failed,] +pDNA2pro.failed <- pDNA2pro.wID.all[pDNA2pro.wID.all$ID %in% ID.failed, ] +length(unique(pDNA2pro.failed$gDNA)) # 19 +# "CD169908" "CD198588", TransVar failed to map to a pChange, therefore, no expansion can be done for this two cases. + +# expand the pDNA2pro.wID +pDNA2pro.wID_exp <- pDNA2pro.wID[rep(seq_len(nrow(pDNA2pro.wID)), each = 20), ] +pDNA2pro.wID_exp$exp.pAlt <- AA.filt$OneLetter + +pDNA2pro.wID_exp <- pDNA2pro.wID_exp[pDNA2pro.wID_exp$TransVar_pRef != pDNA2pro.wID_exp$exp.pAlt, ] +pDNA2pro.wID_exp$PROT2_exp <- paste0( + pDNA2pro.wID_exp$TransVar_NP_ID, + ":p.", + pDNA2pro.wID_exp$TransVar_pRef, + pDNA2pro.wID_exp$TransVar_pLoc, + pDNA2pro.wID_exp$exp.pAlt +) + +write.table( + unique(pDNA2pro.wID_exp$PROT2_exp), + file.path(args[2], "TransVarInput", "gDNA2pro_exp_var.txt"), + quote = F, + col.names = F, + row.names = F +) + +## run transvar step 4 #### +system( + paste( + "conda run -n hgmd transvar panno -l TransVarInput/gDNA2pro_exp_var.txt --refseq --idmap protein_id --refversion", + genome_version, + "> TransVarOut/out_gDNA2pro_exp.txt" + ), + intern = FALSE +) + +## step 2: then map back to gDNA #### + +# load result +gDNA2pro2g.orig <- read.delim2( + file.path(args[2], "TransVarOut", "out_gDNA2pro_exp.txt"), + header = T, + stringsAsFactors = F +) +gDNA2pro2g <- gDNA2pro2g.orig[gDNA2pro2g.orig$transcript != ".", ] +gDNA2pro2g <- gDNA2pro2g[grepl("candidate_codons=", gDNA2pro2g$info), ] # same nrow as gDNA2pro2g.orig +gDNA2pro2g$idx <- rownames(gDNA2pro2g) +g2p2g.info <- gDNA2pro2g[, c("idx", "info")] + +colnames(gDNA2pro2g)[1] <- "PROT2_exp" +gDNA2pro2g.wID <- merge( + gDNA2pro2g, + pDNA2pro.wID_exp, + by.x = "PROT2_exp", + by.y = "PROT2_exp", + all = T +) + +## save the gDNA2pro2g result #### +write.table( + gDNA2pro2g.wID, + file.path(args[2], "Expand_Result", "gDNA2pro2g.wID.txt"), + sep = "\t", + col.names = T, + row.names = F, + quote = F +) +saveRDS(gDNA2pro2g.wID, + file.path(args[2], "Expand_Result", "gDNA2pro2g.wID.rds")) +toc() + + +# Step 5 - gDNA2VCF ---------------------------------------------------------------------------------------------------- +# step5 extract the gDNA chagne and convert to VCF formats + +print("Runnning Step 5 --- gDNA2VCF") +tic("step 5") + +g.built <- genome_version +del.orig <- readRDS(file.path(args[2], "Expand_Result", "del2g.wID.rds")) +NP_exp.orig <- readRDS(file.path(args[2], "Expand_Result", "NP2g_exp.wID.rds")) +gDNA.orig <- readRDS(file.path(args[2], "Expand_Result", "gDNA2pro2g.wID.rds")) + +# hgmd.orig <- readRDS("hgmd.filt.rds") +hgmd.orig <- readRDS(file.path(args[2], "hgmd_DM.rds")) +hgmd <- hgmd.orig[, c("ID", "RANKSCORE", "CLASS")] + +# function to parse the infor column +# parse to info and extract the all the possible gDNA changes +library(dplyr) #use dplyr instead of plyr here +parse_info <- function(input.df) { + input.df$idx <- as.numeric(rownames(input.df)) + infor.wIdx <- input.df[, c("idx", "info")] + + # Vectorized Transformation + info.df <- infor.wIdx %>% + separate_rows(info, sep = ";") %>% + separate(info, + into = c("key", "value"), + sep = "=", + fill = "right") %>% + select(idx, key, value) + + info.df$idx <- as.numeric(info.df$idx) + info.df.w <- reshape2::dcast( + info.df, + formula = idx ~ key, + value.var = "value", + fill = NULL + ) + + if (sum(info.df.w$idx != infor.wIdx$idx) == 0) { + output.df <- cbind(input.df, info.df.w) + output.df$idx <- NULL + output.df$coordinates.gDNA <- gsub("\\/.*", "", output.df$coordinates.gDNA.cDNA.protein.) + + return(output.df) + } else { + print("error") + } + +} + +# parse the info +del.parse.info <- parse_info(del.orig) +saveRDS(del.parse.info, + file.path(args[2], "Expand_Result", "Step5_del.parse.info.rds")) + + +NP_exp.orig <- as.data.table(NP_exp.orig) +colnames(NP_exp.orig)[5] <- "coordinates.gDNA.cDNA.protein." +NP_exp.parse.info <- parse_info(NP_exp.orig) +saveRDS( + NP_exp.parse.info, + file.path(args[2], "Expand_Result", "Step5_NP_exp.parse.info.rds") +) + +gDNA.parse.info <- parse_info(gDNA.orig) +saveRDS(gDNA.parse.info, "Step5_gDNA.parse.info.rds") + +del.parse.info <- readRDS(file.path(args[2], "Expand_Result", "Step5_del.parse.info.rds")) +NP_exp.parse.info <- readRDS(file.path(args[2], "Expand_Result", "Step5_NP_exp.parse.info.rds")) +gDNA.parse.info <- readRDS(file.path(args[2], "Expand_Result", "Step5_gDNA.parse.info.rds")) + + +library(plyr) #go back with plyr +if (T) { + ## del first #### + del.failed.idx <- is.na(del.parse.info$candidate_mnv_variants) & + is.na(del.parse.info$candidate_snv_variants) & + is.na(del.parse.info$coordinates.gDNA) + del.failed <- del.parse.info[del.failed.idx, ] + del.filt <- del.parse.info[!del.failed.idx, ] + + del.gDNA <- del.filt[, c( + "ID", + "PROT2", + "input", + "candidate_mnv_variants", + "candidate_snv_variants", + "coordinates.gDNA" + )] + + # parse the candidate variants + + del.gDNA.mnv <- del.filt[!is.na(del.filt$candidate_mnv_variants), c("ID", "PROT2", "input", "candidate_mnv_variants")] + del.gDNA.snv <- del.filt[!is.na(del.filt$candidate_snv_variants), c("ID", "PROT2", "input", "candidate_snv_variants")] + del.gDNA.orig <- del.filt[!is.na(del.filt$coordinates.gDNA), c("ID", "PROT2", "input", "coordinates.gDNA")] + # colnames(del.gDNA.mnv) <- colnames(del.gDNA.snv) <- colnames(del.gDNA.orig) <- c("ID","PROT2_exp","input","candidate_variants") + colnames(del.gDNA.mnv) <- colnames(del.gDNA.snv) <- colnames(del.gDNA.orig) <- c("ID", "PROT2", "PROT2_exp", "candidate_variants") # edits Jan 24, 2023 + del.gDNA <- rbind(del.gDNA.snv, del.gDNA.orig) + + del.gDNA <- del.gDNA %>% + mutate(candidate_variants = strsplit(candidate_variants, ",")) %>% + unnest(candidate_variants) %>% + as.data.frame() + + # NP_exp #### + NP_exp.failed.idx <- is.na(NP_exp.parse.info$candidate_mnv_variants) & + is.na(NP_exp.parse.info$candidate_snv_variants) & + is.na(NP_exp.parse.info$coordinates.gDNA) + NP_exp.failed <- NP_exp.parse.info[NP_exp.failed.idx, ] + NP_exp.filt <- NP_exp.parse.info[!NP_exp.failed.idx, ] + + NP_exp.gDNA <- NP_exp.filt[, c( + "ID", + "PROT2", + "input", + "candidate_mnv_variants", + "candidate_snv_variants", + "coordinates.gDNA" + )] + + # parse the candidate variants + + NP_exp.gDNA.mnv <- NP_exp.filt[!is.na(NP_exp.filt$candidate_mnv_variants), c("ID", "PROT2", "input", "candidate_mnv_variants")] + NP_exp.gDNA.snv <- NP_exp.filt[!is.na(NP_exp.filt$candidate_snv_variants), c("ID", "PROT2", "input", "candidate_snv_variants")] + NP_exp.gDNA.orig <- NP_exp.filt[!is.na(NP_exp.filt$coordinates.gDNA), c("ID", "PROT2", "input", "coordinates.gDNA")] + colnames(NP_exp.gDNA.mnv) <- colnames(NP_exp.gDNA.snv) <- colnames(NP_exp.gDNA.orig) <- c("ID", "PROT2", "PROT2_exp", "candidate_variants") + NP_exp.gDNA <- rbind(NP_exp.gDNA.snv, NP_exp.gDNA.mnv, NP_exp.gDNA.orig) + + NP_exp.gDNA <- NP_exp.gDNA %>% + mutate(candidate_variants = strsplit(candidate_variants, ",")) %>% + unnest(candidate_variants) %>% + as.data.frame() + + ## gDNA #### + + gDNA.failed.idx <- is.na(gDNA.parse.info$candidate_mnv_variants) & + is.na(gDNA.parse.info$candidate_snv_variants) & + is.na(gDNA.parse.info$coordinates.gDNA) + gDNA.failed <- gDNA.parse.info[gDNA.failed.idx, ] + gDNA.filt <- gDNA.parse.info[!gDNA.failed.idx, ] + # table(gDNA.failed$candidate_codons) + + + gDNA.gDNA <- gDNA.filt[, c( + "ID", + "PROT2", + "PROT2_exp", + "candidate_mnv_variants", + "candidate_snv_variants", + "coordinates.gDNA" + )] + # parse the candidate variants + + gDNA.gDNA.mnv <- gDNA.filt[!is.na(gDNA.filt$candidate_mnv_variants), c("ID", "PROT2", "PROT2_exp", "candidate_mnv_variants")] + gDNA.gDNA.snv <- gDNA.filt[!is.na(gDNA.filt$candidate_snv_variants), c("ID", "PROT2", "PROT2_exp", "candidate_snv_variants")] + gDNA.gDNA.orig <- gDNA.filt[!is.na(gDNA.filt$coordinates.gDNA), c("ID", "PROT2", "PROT2_exp", "coordinates.gDNA")] + colnames(gDNA.gDNA.mnv) <- colnames(gDNA.gDNA.snv) <- colnames(gDNA.gDNA.orig) <- c("ID", "PROT2", "PROT2_exp", "candidate_variants") + gDNA.gDNA <- rbind(gDNA.gDNA.snv, gDNA.gDNA.mnv, gDNA.gDNA.orig) + + gDNA.gDNA <- gDNA.gDNA %>% + mutate(candidate_variants = strsplit(candidate_variants, ",")) %>% + unnest(candidate_variants) %>% + as.data.frame() + + ## Try convert gDNA to VCF format with code #### + # from del + del.gDNA$gChr <- str_extract(del.gDNA$candidate_variants, + "^chr([:digit:]+)|^(chr[A-Z])") + del.gDNA$gChr <- gsub("chr", "", del.gDNA$gChr) + del.gDNA$gLoc <- str_extract(del.gDNA$candidate_variants, "g.([:digit:]+)") + del.gDNA$gLoc <- gsub("g.", "", del.gDNA$gLoc) + # del.gDNA$gRef <- str_extract(del.gDNA$candidate_variants, "g.(([:digit:]|\\_)+|[A-Z]+)") + del.gDNA$gRef_tmp <- str_extract(del.gDNA$candidate_variants, "(([A-z]|\\>)+)$") + del.gDNA$gRef <- str_extract(del.gDNA$gRef_tmp, "([A-Z]+)") + del.gDNA$gRef_tmp <- NULL + del.gDNA$gAlt <- str_extract(del.gDNA$candidate_variants, "([A-Z])+$") + del.gDNA$HGMD_exp_type <- "Del_to_Missense" + + # from gDNA + gDNA.gDNA$gChr <- str_extract(gDNA.gDNA$candidate_variants, + "^chr([:digit:]+)|^(chr[A-Z])") + gDNA.gDNA$gChr <- gsub("chr", "", gDNA.gDNA$gChr) + gDNA.gDNA$gLoc <- str_extract(gDNA.gDNA$candidate_variants, "g.([:digit:]+)") + gDNA.gDNA$gLoc <- gsub("g.", "", gDNA.gDNA$gLoc) + # gDNA.gDNA$gRef <- str_extract(gDNA.gDNA$candidate_variants, "g.(([:digit:]|\\_)+|[A-Z]+)") + gDNA.gDNA$gRef_tmp <- str_extract(gDNA.gDNA$candidate_variants, "(([A-z]|\\>)+)$") + gDNA.gDNA$gRef <- str_extract(gDNA.gDNA$gRef_tmp, "([A-Z]+)") + gDNA.gDNA$gRef_tmp <- NULL + gDNA.gDNA$gAlt <- str_extract(gDNA.gDNA$candidate_variants, "([A-Z])+$") + + # add the HGMD_exp_type + PROT2_Alt <- str_extract(gDNA.gDNA$PROT2, "([A-z])+$") + PROT2_exp_Alt <- str_extract(gDNA.gDNA$PROT2_exp, "([A-z])+$") + gDNA.gDNA$HGMD_exp_type[PROT2_Alt == PROT2_exp_Alt] <- "Same_pChange" + gDNA.gDNA$HGMD_exp_type[PROT2_Alt != PROT2_exp_Alt] <- "Different_pChange" + gDNA.gDNA$HGMD_exp_type[grepl("\\?", gDNA.gDNA$PROT2)] <- "Start_Loss" + gDNA.gDNA$HGMD_exp_type[grepl("\\*", gDNA.gDNA$PROT2_exp)] <- "Stop_Loss" + + table(gDNA.gDNA$HGMD_exp_type) + + # from NP_exp.gDNA + NP_exp.gDNA$gChr <- str_extract(NP_exp.gDNA$candidate_variants, + "^chr([:digit:]+)|^(chr[A-Z])") + NP_exp.gDNA$gChr <- gsub("chr", "", NP_exp.gDNA$gChr) + NP_exp.gDNA$gLoc <- str_extract(NP_exp.gDNA$candidate_variants, "g.([:digit:]+)") + NP_exp.gDNA$gLoc <- gsub("g.", "", NP_exp.gDNA$gLoc) + # NP_exp.gDNA$gRef <- str_extract(NP_exp.gDNA$candidate_variants, "g.(([:digit:]|\\_)+|[A-Z]+)") + NP_exp.gDNA$gRef_tmp <- str_extract(NP_exp.gDNA$candidate_variants, "(([A-z]|\\>)+)$") + NP_exp.gDNA$gRef <- str_extract(NP_exp.gDNA$gRef_tmp, "([A-Z]+)") + NP_exp.gDNA$gRef_tmp <- NULL + NP_exp.gDNA$gAlt <- str_extract(NP_exp.gDNA$candidate_variants, "([A-Z])+$") + + # add the HGMD_exp_type + PROT2_Alt <- str_extract(NP_exp.gDNA$PROT2, "([A-z])+$") + PROT2_exp_Alt <- str_extract(NP_exp.gDNA$PROT2_exp, "([A-z])+$") + # check if the length of Alt, exp_Alt are the same + PROT2_Alt_nchar <- nchar(PROT2_Alt) + PROT2_exp_Alt_nchar <- nchar(PROT2_exp_Alt) + table(PROT2_Alt_nchar == PROT2_exp_Alt_nchar) # all T + + NP_exp.gDNA$HGMD_exp_type[PROT2_Alt == PROT2_exp_Alt] <- "Same_pChange" + NP_exp.gDNA$HGMD_exp_type[PROT2_Alt != PROT2_exp_Alt] <- "Different_pChange" + NP_exp.gDNA$HGMD_exp_type[grepl("\\?", NP_exp.gDNA$PROT2)] <- "Start_Loss" + NP_exp.gDNA$HGMD_exp_type[grepl("\\*", NP_exp.gDNA$PROT2_exp)] <- "Stop_Loss" + + table(is.na(NP_exp.gDNA$HGMD_exp_type)) + + ## add the RankScore #### + HGMD_exp.all <- rbind(del.gDNA, NP_exp.gDNA, gDNA.gDNA) + + HGMD_exp.all <- merge(HGMD_exp.all, + hgmd, + by.x = "ID", + by.y = "ID", + all.x = T) + saveRDS(HGMD_exp.all, + file.path(args[2], "Expand_Result_final", "hgmd.Coding.rds")) + write.table( + HGMD_exp.all, + file.path(args[2], "Expand_Result_final", "hgmd.Coding.txt"), + col.names = T, + row.names = F, + quote = F, + sep = "\t" + ) + +} + +toc() + +# Step 6 - nonCoding ---------------------------------------------------------------------------------------------------- +print("Runnning Step 6 --- nonCoding") +tic("step 6") + +hgmd.orig <- readRDS(file.path(args[2], "hgmd_DM.rds")) +hgmd.filt <- readRDS(file.path(args[2], "Expand_Result", "hgmd.filt.rds")) + +hgmd <- hgmd.orig +hgmd$PROT2 <- gsub(".[0-9]+%3A", ":", hgmd$PROT) +hgmd$PROT2 <- gsub("%3B", ";", hgmd$PROT2) +hgmd$PROT2 <- gsub("%3D", "=", hgmd$PROT2) +hgmd$PROT2 <- gsub("\\(|\\)", "", hgmd$PROT2) + +## pre-filter the HGMD entries #### + +# parse PROT column +hgmd <- hgmd[, c("ID", "CHROM", "POS", "REF", "ALT", "GENE", "PROT", "PROT2")] + +## var to drop #### +# frame shift (drop) +hgmd$fs <- grepl("fs", hgmd$PROT2) + +# drop var with multiple protein change (no expansion for them) (there are 48) +hgmd$multi_pChange <- grepl(";", hgmd$PROT2) + +# drop stop loss or stop gain (with * in the PROT) +hgmd$StopLossGain <- grepl("\\*", hgmd$PROT2) + +# drop inframe insertions and delins (ins and delins), keep del +hgmd$ins <- grepl("ins", hgmd$PROT2) +hgmd$dup <- grepl("dup", hgmd$PROT2) + +# drop inframe deletions that affect more than 1 aa +# del2 <- hgmd.filt[grepl("del", hgmd.filt$PROT2),] +del <- hgmd[grepl("del", hgmd$PROT2), ] +del <- separate( + data = del, + col = "PROT2", + into = c("NP_ID", "pChange"), + remove = F, + sep = ":" +) +del_multi_AA <- del[grepl("_", del$pChange), ] # dim 2319 +hgmd$del_multi_AA <- hgmd$ID %in% del_multi_AA$ID + +# drop ones that are NA in PROT +hgmd$NA_PROT2 <- is.na(hgmd$PROT2) + +# synonymous var (will parse as non-coding) +hgmd$synonymous <- grepl("=", hgmd$PROT2) + +# hgmd drop +drop_for_pExp <- hgmd$fs + hgmd$multi_pChange + hgmd$StopLossGain + hgmd$ins + hgmd$dup + hgmd$del_multi_AA + hgmd$synonymous + hgmd$NA_PROT2 +table(drop_for_pExp) +# same number as hgmd.filt, confirmed + +# for non-coding +# first, not in hgmd.filt +# then drop hgmd$fs + hgmd$multi_pChange + hgmd$StopLossGain + hgmd$ins + hgmd$dup + hgmd$del_multi_AA +drop_for_noncoding <- hgmd$fs + hgmd$multi_pChange + hgmd$StopLossGain + hgmd$ins + hgmd$dup + hgmd$del_multi_AA + (hgmd$ID %in% hgmd.filt$ID) +table(drop_for_noncoding) # 32165 remaining for non-coding + +hgmd.non.coding <- hgmd[drop_for_noncoding == 0, ] + +hgmd.non.coding$RefLength <- nchar(hgmd.non.coding$REF) +hgmd.non.coding$AltLength <- nchar(hgmd.non.coding$ALT) +hgmd.non.coding$delLength <- hgmd.non.coding$RefLength - hgmd.non.coding$AltLength + +hgmd.non.coding$ExpPos_start <- as.numeric(hgmd.non.coding$POS) - 2 +hgmd.non.coding$ExpPos_stop <- as.numeric(hgmd.non.coding$POS) + hgmd.non.coding$RefLength + 2 +hgmd.non.coding.part <- hgmd.non.coding[, c( + "ID", + "CHROM", + "POS", + "REF", + "ALT", + "GENE", + "PROT2", + "RefLength", + "AltLength", + "delLength", + "ExpPos_start", + "ExpPos_stop" +)] + +Ref.length.df <- as.data.frame(table(hgmd.non.coding.part$RefLength[hgmd.non.coding.part$RefLength <= 10])) +Ref.length.df$Var1 <- as.character(Ref.length.df$Var1) +Ref.length.df <- rbind(Ref.length.df, c(">10", sum(hgmd.non.coding.part$RefLength > 10))) + +del.length.df <- as.data.frame(table(hgmd.non.coding.part$delLength[hgmd.non.coding.part$delLength <= 10 & + hgmd.non.coding.part$delLength >= 0])) +del.length.df$Var1 <- as.character(del.length.df$Var1) +del.length.df <- rbind(del.length.df, c(">10", sum(hgmd.non.coding.part$delLength > 10))) +del.length.df <- rbind(del.length.df, c("<0", sum(hgmd.non.coding.part$delLength < 0))) + +hgmd.non.coding.select <- hgmd.non.coding.part[hgmd.non.coding.part$RefLength <= 10 & + hgmd.non.coding.part$delLength >= 0, ] + +hgmd.non.coding.all <- merge( + hgmd.non.coding.select, + hgmd.orig[, c("ID", "RANKSCORE")], + by.x = "ID", + by.y = "ID", + all.x = T +) +hgmd.non.coding.all$HGMD_Exp <- "nonCoding" +hgmd.non.coding.final <- hgmd.non.coding.all[, c( + "CHROM", + "ExpPos_start", + "ExpPos_stop", + "RANKSCORE", + "HGMD_Exp", + "RefLength", + "delLength" +)] + +saveRDS( + hgmd.non.coding.final, + file.path(args[2], "Expand_Result_final", "hgmd.nonCoding.rds") +) +write.table( + hgmd.non.coding.final, + file.path(args[2], "Expand_Result_final", "hgmd.nonCoding.txt"), + sep = "\t", + col.names = T, + quote = F, + row.names = F +) + +saveRDS(hgmd.non.coding.all, + file.path(args[2], "Expand_Result", "hgmd.nonCoding.all.rds")) +write.table( + hgmd.non.coding.all, + file.path(args[2], "Expand_Result", "hgmd.nonCoding.all.txt"), + sep = "\t", + col.names = T, + quote = F, + row.names = F +) + + +# Step 7 dropped +# Step 8 - reformat4AI ---------------------------------------------------------------------------------------------------- +print("Runnning Step 8 (skipping step7) --- reformat4AI") +tic("step 8") +# reformat expansion +Coding <- fread(file.path(args[2], "Expand_Result_final", "hgmd.Coding.txt")) +nonCoding <- fread(file.path(args[2], "Expand_Result_final", "hgmd.nonCoding.txt")) + +Coding <- Coding[, c("ID", + "HGMD_exp_type", + "gAlt", + "gRef", + "RANKSCORE", + "CLASS", + "gLoc", + "gChr")] +colnames(Coding) <- c("hgmdID", + "c_HGMD_Exp", + "alt", + "ref", + 'c_RANKSCORE', + 'CLASS', + 'new_pos', + "new_chr") + +Coding$new_chr[Coding$new_chr == "X"] <- "23" +Coding$new_chr[Coding$new_chr == "Y"] <- "24" + + + +## One potential issue!!!!!!: it keeps col.names = T when writing the output, which could cause the issue in reading this tsv file in R; However, it doesn't affect results when testing with pd in Python +write.table( + Coding, + file.path(args[2], "Expand_Result_final", "hgmd_c.tsv"), + sep = "\t", + col.names = T, + row.names = T, + quote = F +) + +nonCoding <- nonCoding[, c( + "RANKSCORE", + "HGMD_Exp", + "RefLength", + "delLength", + "ExpPos_start", + "ExpPos_stop", + "CHROM" +)] +colnames(nonCoding) <- c( + "nc_RANKSCORE", + 'nc_HGMD_Exp', + 'hgmd_refLength', + 'hgmd_delLength', + "new_start", + 'new_stop', + "new_chr" +) + +nonCoding$new_chr[nonCoding$new_chr == "X"] <- "23" +nonCoding$new_chr[nonCoding$new_chr == "Y"] <- "24" + +## One potential issue!!!!!!: it keeps col.names = T when writing the output, which could cause the issue in reading this tsv file in R; However, it doesn't affect results when testing with pd in Python +write.table( + nonCoding, + file.path(args[2], "Expand_Result_final", "hgmd_nc.tsv"), + sep = "\t", + col.names = T, + row.names = T, + quote = F +) +toc() diff --git a/utils/hgmd_update/merge_expand/setup.sh b/utils/hgmd_update/merge_expand/setup.sh new file mode 100644 index 0000000..467442c --- /dev/null +++ b/utils/hgmd_update/merge_expand/setup.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +#----------------------------- Setup ----------------------------- + +HGMD_hg19_DIR=HGMD_Pro_2024.3_hg19.vcf.gz +HGMD_hg38_DIR=HGMD_Pro_2024.3_hg38.vcf.gz + +OUT_hg19_DIR=output/hg19 +OUT_hg38_DIR=output/hg38 + +REF_hg19_DIR=references/hg19 +REF_hg38_DIR=references/hg38 +REF_aa=references/AA_abbreviations.txt + +RSCRIPT_PATH=expandvar.R + +# download reference genomes +wget -P $REF_hg19_DIR https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz +wget -P $REF_hg38_DIR https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz + +# decompress reference genomes +gunzip $REF_hg19_DIR/*.gz + +# download AA abbreviations +# just Amino Acids 3 letters and 1 letter names, there is a copy at skyriver: /home/zhijiany/workdir/references/AA_abbreviations.txt + +# setup conda environment +conda create -n hgmd r-base python=3.11 samtools -y +conda activate hgmd + +# index reference genomes +conda run -n hgmd samtools faidx $REF_hg19_DIR/hg19.fa +conda run -n hgmd samtools faidx $REF_hg38_DIR/hg38.fa + +# install transvar +conda run -n hgmd pip install transvar + +# setup transvar annotations and reference genomes +#hg19 +transvar config --download_anno --refversion hg19 +transvar config -k reference -v $REF_hg19_DIR/hg19.fa --refversion hg19 + +#hg38 +transvar config --download_anno --refversion hg38 +transvar config -k reference -v $REF_hg38_DIR/hg38.fa --refversion hg38 + + + +#----------------------------- Run the R script ----------------------------- + +#Run the Rscript for hg19 +conda run -n hgmd Rscript $RSCRIPT_PATH $HGMD_hg19_DIR $OUT_hg19_DIR $REF_aa hg19 + +#Run the R script for hg38 +conda run -n hgmd Rscript $RSCRIPT_PATH $HGMD_hg38_DIR $OUT_hg38_DIR $REF_aa hg38 + From e26ed4b1dcba521b8a082f72abe6edc47da5742e Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 14 Nov 2024 10:16:25 -0600 Subject: [PATCH 06/80] Update Scikit Learn to >=1.3.0 and Stop Using predict_new/ --- bin/run_final.py | 4 ++-- main.nf | 2 -- requirements.txt | 2 +- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/bin/run_final.py b/bin/run_final.py index 835aca0..20c406d 100755 --- a/bin/run_final.py +++ b/bin/run_final.py @@ -10,10 +10,10 @@ import sys # read in precalulated model -model = joblib.load("predict_new/hg19/final_model_wo_bg_val.job") +model = joblib.load("model_inputs/default/final_model.job") # read in features to use -features = list(pd.read_csv("predict_new/hg19/features.csv")) +features = list(pd.read_csv("model_inputs/default/features.csv")) # predict with Linhua's code out = rank_patient(model, sys.argv[1] + ".matrix.txt", features, train_causals=None) diff --git a/main.nf b/main.nf index 6b10486..2c13b9b 100644 --- a/main.nf +++ b/main.nf @@ -572,7 +572,6 @@ process PREDICTION { path merged_matrix path merged_compressed_scores - path ref_predict_new_dir path ref_model_inputs_dir output: @@ -720,7 +719,6 @@ workflow { PREDICTION( MERGE_SCORES_BY_CHROMOSOME.out.merged_matrix, MERGE_SCORES_BY_CHROMOSOME.out.merged_compressed_scores, - file(params.ref_predict_new_dir), file(params.ref_model_inputs_dir) ) } diff --git a/requirements.txt b/requirements.txt index 4db1b60..84ce567 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,4 @@ tqdm matplotlib requests tables -scikit-learn==1.1.2 +scikit-learn>=1.3.0 From 80bb19ed3435420b071653bd9dd1abc0db58d1ca Mon Sep 17 00:00:00 2001 From: Zhijian Yu Date: Thu, 14 Nov 2024 11:12:23 -0600 Subject: [PATCH 07/80] fix a file path issue --- utils/hgmd_update/merge_expand/expandvar.R | 48 ++++++++++------------ 1 file changed, 22 insertions(+), 26 deletions(-) diff --git a/utils/hgmd_update/merge_expand/expandvar.R b/utils/hgmd_update/merge_expand/expandvar.R index 5fb0e74..c53eccf 100644 --- a/utils/hgmd_update/merge_expand/expandvar.R +++ b/utils/hgmd_update/merge_expand/expandvar.R @@ -124,10 +124,10 @@ tic("step 3 and 4") print("Runnning Step 3&4 --- pro2genome") # parse pro2g result from TransVar -vcf.all <- readRDS(file.path(args[2], "hgmd_all.rds")) # 280294 var +vcf.all <- readRDS(file.path(args[2], "hgmd_all.rds")) # keep CLASS == "DM" and "DM?" -vcf.all <- vcf.all[vcf.all$CLASS %in% c("DM", "DM?"), ] # 268505 var +vcf.all <- vcf.all[vcf.all$CLASS %in% c("DM", "DM?"), ] write.table( vcf.all, file.path(args[2], "hgmd_DM.txt"), @@ -176,7 +176,7 @@ del <- separate( remove = F, sep = ":" ) -del_multi_AA <- del[grepl("_", del$pChange), ] # dim 2698 +del_multi_AA <- del[grepl("_", del$pChange), ] hgmd.filt <- hgmd.filt[!(hgmd.filt$ID %in% del_multi_AA$ID), ] hgmd.non.coding <- hgmd[!(hgmd$ID %in% hgmd.filt$ID), ] @@ -246,7 +246,7 @@ del.1aa3$PROT2_exp <- paste0(del.1aa3$NP_ID, ##TransVar with del.1aa #### -var4transVar.Del <- unique(del.1aa3$PROT2_exp) # 50998 var4transVar.Del +var4transVar.Del <- unique(del.1aa3$PROT2_exp) write.table( var4transVar.Del, file.path(args[2], "TransVarInput", "del_var.txt"), @@ -274,10 +274,10 @@ del2g.orig <- read.delim2( header = T, stringsAsFactors = F ) # 59567-2022version 80678-2024version -del2g <- del2g.orig[del2g.orig$transcript != ".", ] # 59147 +del2g <- del2g.orig[del2g.orig$transcript != ".", ] # del2g <- del2g[grepl("candidate_codons=",del2g$info),] # 50711 -var <- unique(del2g$input) # 59147 +var <- unique(del2g$input) failed.del2g <- del.1aa3[!(del.1aa3$PROT2_exp %in% var) & del.1aa3$pAlt != "Ter", ] @@ -308,8 +308,8 @@ saveRDS(del2g.wID, file.path(args[2], "Expand_Result", "del2g.wID.rds")) # select var to run TransVar: non del NP_df <- hgmd.filt[!(hgmd.filt$PROT2 %in% del.1aa3$PROT2), ] -var4transVar.p <- unique(NP_df$PROT2) # 167983 -table(grepl("\\?", var4transVar.p)) # 807 T +var4transVar.p <- unique(NP_df$PROT2) +table(grepl("\\?", var4transVar.p)) write.table( var4transVar.p[!grepl("\\?", var4transVar.p)], @@ -337,7 +337,7 @@ NP_df_exp$pLoc <- str_extract(NP_df_exp$pChange, "[0-9]+") NP_df_exp_3aa <- NP_df_exp[NP_df_exp$pAlt %in% AA.3letter, ] NP_df_exp_1aa <- NP_df_exp[NP_df_exp$pAlt %in% LETTERS, ] -NP_df_exp_qMark <- NP_df_exp[NP_df_exp$pAlt == "?", ] # all start loss +NP_df_exp_qMark <- NP_df_exp[NP_df_exp$pAlt == "?", ] # 3 letters NP_df_exp_3aa <- NP_df_exp_3aa[rep(seq_len(nrow(NP_df_exp_3aa)), each = 20), ] @@ -425,7 +425,7 @@ NP2g.orig <- read.delim2( ) # 166544 # write.table(NP2g.orig, file.path(args[2],"TransVarOut","out_NP.txt"), col.names = T, row.names = F, quote = F, sep = "\t") -NP2g <- NP2g.orig[NP2g.orig$transcript != ".", ] # 165680 +NP2g <- NP2g.orig[NP2g.orig$transcript != ".", ] NP2g <- NP2g[grepl("candidate_codons=", NP2g$info), ] # all failed are M1?, start loss, can be rescued in NP2g_exp # check failed @@ -438,13 +438,13 @@ NP2g_exp.orig <- fread( header = T, stringsAsFactors = F, sep = "\t" -) # 137258 +) -NP2g_exp <- NP2g_exp.orig[NP2g_exp.orig$transcript != ".", ] #2624793 +NP2g_exp <- NP2g_exp.orig[NP2g_exp.orig$transcript != ".", ] # NP2g_exp <- NP2g_exp[grepl("candidate_codons=",NP2g_exp$info),] # 2167881 # check failed -var_exp <- unique(NP2g_exp$input) # 2624793 +var_exp <- unique(NP2g_exp$input) NP2g_exp.wID <- merge( NP2g_exp, @@ -470,9 +470,7 @@ NP2g.wID <- merge(NP2g, hgmd.filt, by.x = "input", by.y = "PROT2", - all.x = T) # 137187 -# table(duplicated(NP2g.wID$input)) -# table(duplicated(NP2g.wID$ID)) + all.x = T) NP2g.wID$input_type <- "hgmd.NP_ID" NP2g.wID$PROT2 <- NP2g.wID$input @@ -495,7 +493,7 @@ saveRDS(NP2g.wID, file.path(args[2], "Expand_Result", "NP2g.wID.rds")) # then run TransVar with pro change -> get the gDNA change var <- unique(c(NP2g.wID$input, del2g.wID$PROT2, NP2g_exp.wID$PROT2)) -failed.NP2g <- hgmd.filt[!(hgmd.filt$PROT2 %in% var), ] # 1524 +failed.NP2g <- hgmd.filt[!(hgmd.filt$PROT2 %in% var), ] failed.ID <- unique(failed.NP2g$ID) # get the gDNA for the failed.NP2g (failed NP and Gene) @@ -575,8 +573,7 @@ info.df <- pDNA2pro |> sep = "=", fill = "right", extra = "merge", - # Merge any additional splits into 'value' - remove = FALSE # Retain the original 'fields' column + remove = FALSE ) |> rename(coordinates = coordinates.gDNA.cDNA.protein.) |> select(fields, key, value, original_info, coordinates, input) |> @@ -656,7 +653,7 @@ library(plyr) # pDNA2pro.failed <- gDNA.failed.NP2g.part[gDNA.failed.NP2g.part$ID %in% ID.failed,] pDNA2pro.failed <- pDNA2pro.wID.all[pDNA2pro.wID.all$ID %in% ID.failed, ] -length(unique(pDNA2pro.failed$gDNA)) # 19 +length(unique(pDNA2pro.failed$gDNA)) # "CD169908" "CD198588", TransVar failed to map to a pChange, therefore, no expansion can be done for this two cases. # expand the pDNA2pro.wID @@ -792,7 +789,10 @@ saveRDS( ) gDNA.parse.info <- parse_info(gDNA.orig) -saveRDS(gDNA.parse.info, "Step5_gDNA.parse.info.rds") +saveRDS( + gDNA.parse.info, + file.path(args[2], "Expand_Result","Step5_gDNA.parse.info.rds") +) del.parse.info <- readRDS(file.path(args[2], "Expand_Result", "Step5_del.parse.info.rds")) NP_exp.parse.info <- readRDS(file.path(args[2], "Expand_Result", "Step5_NP_exp.parse.info.rds")) @@ -801,7 +801,6 @@ gDNA.parse.info <- readRDS(file.path(args[2], "Expand_Result", "Step5_gDNA.parse library(plyr) #go back with plyr if (T) { - ## del first #### del.failed.idx <- is.na(del.parse.info$candidate_mnv_variants) & is.na(del.parse.info$candidate_snv_variants) & is.na(del.parse.info$coordinates.gDNA) @@ -831,7 +830,6 @@ if (T) { unnest(candidate_variants) %>% as.data.frame() - # NP_exp #### NP_exp.failed.idx <- is.na(NP_exp.parse.info$candidate_mnv_variants) & is.na(NP_exp.parse.info$candidate_snv_variants) & is.na(NP_exp.parse.info$coordinates.gDNA) @@ -867,8 +865,6 @@ if (T) { is.na(gDNA.parse.info$coordinates.gDNA) gDNA.failed <- gDNA.parse.info[gDNA.failed.idx, ] gDNA.filt <- gDNA.parse.info[!gDNA.failed.idx, ] - # table(gDNA.failed$candidate_codons) - gDNA.gDNA <- gDNA.filt[, c( "ID", @@ -1189,4 +1185,4 @@ write.table( row.names = T, quote = F ) -toc() +toc() \ No newline at end of file From a7913d169f0a1c6a69ce34d5f3adf606517b4cf8 Mon Sep 17 00:00:00 2001 From: jjia1 Date: Thu, 14 Nov 2024 21:45:24 +0000 Subject: [PATCH 08/80] added ModelInterpreter --- .gitignore | 5 + bin/extraModel_main.py | 50 +- bin/model_interpreter/bin/SHAP_to_JSON.py | 24 +- bin/model_interpreter/example.ipynb | 1392 - .../results/lime_analysis.json | 195860 ------------- .../results/shap_analysis.json | 225992 --------------- .../genomic_variant_report_template.html | 163 - .../variant_model_interpreter.py | 53 +- main.nf | 47 +- nextflow.config | 2 +- requirements.txt | 2 +- 11 files changed, 117 insertions(+), 423473 deletions(-) delete mode 100644 bin/model_interpreter/example.ipynb delete mode 100644 bin/model_interpreter/results/lime_analysis.json delete mode 100644 bin/model_interpreter/results/shap_analysis.json delete mode 100644 bin/model_interpreter/templates/genomic_variant_report_template.html diff --git a/.gitignore b/.gitignore index b6bf4db..fd7eea8 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ test_data/ test_ref/ test_output/ **/.ipynb_checkpoints/ +*.ipynb # Nextflow Related .nextflow* @@ -22,3 +23,7 @@ params.yaml # Ignore R history and session files .Rhistory .RData + +# Remove some files from model interpreter +*/model_interpreter/results +*/model_interpreter/templates diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index 57c1f50..e4c7ce5 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -11,6 +11,7 @@ from tqdm import tqdm from scipy.stats import rankdata from extraModel.integrate_output import * +from model_interpreter.variant_model_interpreter import ModelInterpreter parser = argparse.ArgumentParser() @@ -45,6 +46,33 @@ print(f"### Start generating recessive matrix and running predictions.") +def run_model_interpretation(model, data_df, features, output_prefix, run_id, model_type): + """Run SHAP and LIME analysis for a specific model""" + interpreter = ModelInterpreter() + print(interpreter, model) + interpreter.set_model(model) # Set the already loaded model + + # Prepare data (use only the features used by this model) + model_data = data_df.loc[:, features] + + # Calculate SHAP values + interpreter.calculate_shap_values(model_data) + + print(model_data.index[0:9]) + interpreter.variant_ids = list(model_data.index) + print(model_data.columns[0:9]) + interpreter.feature_names = list(model_data.columns) + + print(interpreter.model_type) + print(interpreter.shap_values.base_values) + # Create output directories if they don't exist + os.makedirs("shap_outputs", exist_ok=True) + interpreter.run_shap_analysis(output_file=f"shap_outputs/{run_id}_{model_type}_shap_values.json") + + os.makedirs("lime_outputs", exist_ok=True) + interpreter.run_lime_analysis(model_data, output_file=f"lime_outputs/{run_id}_{model_type}_lime_values.json") + + def assign_ranking(df): pred_df = df.copy() @@ -78,6 +106,16 @@ def AIM(data_folder, sample_id): df_pred = df_pred.sort_values("confidence", ascending=False) df_pred = assign_ranking(df_pred) df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") + + # Run model interpretation + run_model_interpretation( + model=model_dict[mn]["model"], + data_df=df_pred, + features=model_dict[mn]["features"], + output_prefix=mn, + run_id=sample_id, + model_type = mn + ) print(f"### Start processing recessive data and make predictions.") @@ -109,6 +147,16 @@ def AIM(data_folder, sample_id): df_pred = df_pred.sort_values("confidence", ascending=False, kind="stable") df_pred = assign_ranking(df_pred) df_pred.to_csv(f"{out_folder}/{sample_id}_{mn}_predictions.csv") + + # Run model interpretation + run_model_interpretation( + model=model_dict[mn]["model"], + data_df=df_pred, + features=model_dict[mn]["features"], + output_prefix=mn, + run_id=sample_id, + model_type = mn + ) else: # AIM found no recessive variant pair pass @@ -123,4 +171,4 @@ def AIM(data_folder, sample_id): # for sample_id in tqdm(sample_folders): -AIM(out_folder, sample_id) +AIM(out_folder, sample_id) \ No newline at end of file diff --git a/bin/model_interpreter/bin/SHAP_to_JSON.py b/bin/model_interpreter/bin/SHAP_to_JSON.py index f9ebef7..fdbebb1 100644 --- a/bin/model_interpreter/bin/SHAP_to_JSON.py +++ b/bin/model_interpreter/bin/SHAP_to_JSON.py @@ -11,20 +11,28 @@ sys.path.append(str(Path(__file__).parent.parent)) from utils.numpy_to_python import convert_numpy_to_python -def create_shap_json(variant_ids, feature_names, shap_values, output_file = "./results/shap_values.json"): +def create_shap_json(variant_ids, feature_names, shap_values, output_file="./results/shap_values.json"): """ - Create JSON File from shap values generated from shap.TreeExplainer - Example Usage where processed_df is feature matrix: - variant_ids = list(processed_df.index) - feature_names = list(processed_df.columns) - json_output = create_shap_json(variant_ids, feature_names, shap_values) + Create JSON file from SHAP values. """ + if not variant_ids or not feature_names: + raise ValueError("variant_ids or feature_names cannot be None") + json_data = [] - + + # Determine if base_values is an array and extract the relevant value for binary classification + is_multiclass = isinstance(shap_values.base_values[0], (list, np.ndarray)) + for idx in range(len(shap_values.values)): + # Handle the case where base_values is an array (e.g., binary classification) + if is_multiclass: + base_value = float(shap_values.base_values[idx][1]) # Use the base value for class 1 + else: + base_value = float(shap_values.base_values[idx]) + entry = { "variant_id": variant_ids[idx], - "base_value": float(shap_values.base_values[idx]), + "base_value": base_value, "model_output_score": {}, "feature_values": {} } diff --git a/bin/model_interpreter/example.ipynb b/bin/model_interpreter/example.ipynb deleted file mode 100644 index aaea862..0000000 --- a/bin/model_interpreter/example.ipynb +++ /dev/null @@ -1,1392 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1aa4b5e6-4e6e-451a-8496-3c13d3b8fe75", - "metadata": {}, - "outputs": [], - "source": [ - "from variant_model_interpreter import ModelInterpreter" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ba16b3e6-b9a4-4ecb-a471-8b22fa7392f7", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0bf68cf0-92ae-4ca4-a8f0-14a57cce1e23", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:variant_model_interpreter:Loaded model with 41 features\n", - "INFO:variant_model_interpreter:Model features: ['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score', 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI', 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED', 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg', 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax', 'cons_transcript_ablation', 'cons_splice_acceptor_variant', 'cons_splice_donor_variant', 'cons_stop_gained', 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost', 'cons_transcript_amplification', 'cons_inframe_insertion', 'cons_inframe_deletion', 'cons_missense_variant', 'cons_protein_altering_variant', 'cons_splice_region_variant', 'cons_splice_donor_5th_base_variant', 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD', 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L', 'simple_repeat']\n", - "INFO:variant_model_interpreter:Missing features: set()\n", - "INFO:variant_model_interpreter:Extra features: {'GERPpp_RS', 'predict', 'clinvarNumB', 'isB/LB', 'curationScoreOMIM', 'c_HGMD_Exp_Del_to_Missense', 'CLASS', 'c_ClinVar_Exp_Different_pChange', 'ranking', 'clinvarNumLB', 'confidence', 'REVEL_score', 'FATHMM_score', 'omimSymptomSimScore', 'omimGeneFound', 'hgmdSymptomSimScore', 'SIFT_score', 'c_HGMD_Exp_Start_Loss', 'recessive', 'c_ClinVar_Exp_Del_to_Missense', 'hgmdVarFound', 'identifier', 'curationScoreHGMD', 'gnomadGeneOELof', 'CADD_phred', 'AR.matched', 'curationScoreClinVar', 'clinVarSymMatchFlag', 'Polyphen2_HDIV_score', 'Polyphen2_HVAR_score', 'isP/LP', 'nc_isBLB', 'hgmdGeneFound', 'dgvVarFound', 'omimVarFound', 'c_HGMD_Exp_Stop_Loss', 'phyloP100way_vertebrate', 'clinVarVarFound', 'clinvarNumLP', 'c_RANKSCORE', 'hgmd_rs', 'AD.matched', 'nc_ClinVar_Exp', 'c_isPLP', 'nc_HGMD_Exp', 'nc_RANKSCORE', 'clinVarGeneFound', 'hgmdSymptomScore', 'hgmdSymMatchFlag', 'fathmm_MKL_coding_score', 'max_ranking', 'c_ClinVar_Exp_Same_pChange', 'c_HGMD_Exp_Same_pChange', 'c_CLNREVSTAT', 'DANN_score', 'diffuse_Phrank_STRING', 'MutationAssessor_score', 'c_isBLB', 'clinvarNumP', 'confidence level', 'M_CAP_score', 'c_HGMD_Exp_Different_pChange', 'phrank', 'omimSymMatchFlag', 'min_ranking', 'nc_isPLP', 'decipherVarFound', 'dominant', 'nc_CLNREVSTAT'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2511, 41)\n", - "Index(['conservationScoreDGV', 'gnomadAF', 'gnomadAFg', 'LRT_score',\n", - " 'LRT_Omega', 'gnomadGeneZscore', 'gnomadGenePLI',\n", - " 'gnomadGeneOELofUpper', 'IMPACT', 'CADD_PHRED',\n", - " 'conservationScoreGnomad', 'conservationScoreOELof', 'zyg',\n", - " 'ESP6500_AA_AF', 'ESP6500_EA_AF', 'hom', 'spliceAImax',\n", - " 'cons_transcript_ablation', 'cons_splice_acceptor_variant',\n", - " 'cons_splice_donor_variant', 'cons_stop_gained',\n", - " 'cons_frameshift_variant', 'cons_stop_lost', 'cons_start_lost',\n", - " 'cons_transcript_amplification', 'cons_inframe_insertion',\n", - " 'cons_inframe_deletion', 'cons_missense_variant',\n", - " 'cons_protein_altering_variant', 'cons_splice_region_variant',\n", - " 'cons_splice_donor_5th_base_variant',\n", - " 'cons_splice_donor_region_variant', 'IMPACT.from.Tier', 'TierAD',\n", - " 'TierAR', 'TierAR.adj', 'No.Var.HM', 'No.Var.H', 'No.Var.M', 'No.Var.L',\n", - " 'simple_repeat'],\n", - " dtype='object')\n", - " conservationScoreDGV gnomadAF gnomadAFg LRT_score \\\n", - "10-135085084-C-T 1.0 0.000019 0.000019 0.000000 \n", - "7-102281151-G-GA 3.0 0.000000 0.000000 0.126376 \n", - "6-17602826-G-T 3.0 0.000000 0.000000 0.000000 \n", - "18-42532220-G-A 3.0 0.000008 0.000032 0.000000 \n", - "23-39932684-A-G 3.0 0.000000 0.000000 0.000000 \n", - "\n", - " LRT_Omega gnomadGeneZscore gnomadGenePLI \\\n", - "10-135085084-C-T 0.000000 0.38049 2.198300e-14 \n", - "7-102281151-G-GA 1.341925 0.18170 1.130200e-01 \n", - "6-17602826-G-T 0.000000 0.30017 1.747100e-02 \n", - "18-42532220-G-A 0.000000 1.09650 1.000000e+00 \n", - "23-39932684-A-G 0.000000 1.87970 1.000000e+00 \n", - "\n", - " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", - "10-135085084-C-T 0.926 3.0 24.600000 ... \n", - "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", - "6-17602826-G-T 0.687 4.0 40.000000 ... \n", - "18-42532220-G-A 0.110 3.0 29.000000 ... \n", - "23-39932684-A-G 0.141 3.0 24.400000 ... \n", - "\n", - " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", - "10-135085084-C-T 0 3.0 2.0 \n", - "7-102281151-G-GA 0 4.0 1.0 \n", - "6-17602826-G-T 0 4.0 1.0 \n", - "18-42532220-G-A 0 3.0 2.0 \n", - "23-39932684-A-G 0 3.0 2.0 \n", - "\n", - " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M No.Var.L \\\n", - "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 0.0 \n", - "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 0.0 \n", - "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 0.0 \n", - "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 0.0 \n", - "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 0.0 \n", - "\n", - " simple_repeat \n", - "10-135085084-C-T 0 \n", - "7-102281151-G-GA 0 \n", - "6-17602826-G-T 0 \n", - "18-42532220-G-A 0 \n", - "23-39932684-A-G 0 \n", - "\n", - "[5 rows x 41 columns]\n", - "CPU times: user 3.26 s, sys: 254 ms, total: 3.51 s\n", - "Wall time: 335 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Initialize interpreter\n", - "interpreter = ModelInterpreter()\n", - "\n", - "# Load model\n", - "interpreter.load_model(\"/home/johnathanj/model_xgb_sep2024_nd.job\")\n", - "\n", - "input_df = pd.read_csv('/home/johnathanj/projects/aim/explainable_AI/filtered_joint_2199195_SAMPLE_NAME_2199195_nd_predictions.csv', index_col = 0)\n", - "\n", - "# Prepare your data\n", - "processed_df = interpreter.prepare_data(input_df)\n", - "print(processed_df.shape)\n", - "print(processed_df.columns)\n", - "print(processed_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "54416630-d199-4f4c-b792-e58ed7f67310", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:variant_model_interpreter:Created SHAP explainer for xgboost model\n", - "INFO:variant_model_interpreter:SHAP values calculated successfully\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 11.2 s, sys: 290 ms, total: 11.5 s\n", - "Wall time: 796 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Calculate SHAP values\n", - "interpreter.calculate_shap_values(processed_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ca6e2bc3-cd07-4e59-97c6-b3419d83172b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 8.17 s, sys: 169 ms, total: 8.34 s\n", - "Wall time: 320 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Save SHAP JSON to custom location\n", - "shap_json = interpreter.run_shap_analysis(output_file='results/shap_analysis.json')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5682ab7f-bec3-4000-910f-30fb7d706d6a", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6h 12min 4s, sys: 7h 21min 39s, total: 13h 33min 43s\n", - "Wall time: 18min 13s\n" - ] - } - ], - "source": [ - "%%time\n", - "# Save LIME JSON to custom location\n", - "lime_json = interpreter.run_lime_analysis(\n", - " processed_df=processed_df,\n", - " num_features = 5,\n", - " output_file='results/lime_analysis.json'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18ee2a07-f8b6-43ae-8c2d-cf4cc2fdb207", - "metadata": {}, - "outputs": [], - "source": [ - "break" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a4120b3e-b226-4385-8e53-7c1aabd05d52", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import importlib\n", - "from bin import SHAP_to_JSON\n", - "importlib.reload(SHAP_to_JSON)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "afce815d-67a0-40f1-a91e-392241a0c2ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from bin import LIME_TO_JSON\n", - "importlib.reload(LIME_TO_JSON)" - ] - }, - { - "cell_type": "markdown", - "id": "4d4c5489-4b4c-464e-a5c8-750e2d39d58d", - "metadata": {}, - "source": [ - "# STOP HERE\n", - "### DEBUGGING/TESTING BELOW" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "fe784ff8-65f2-4262-876e-222490cf7a68", - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary libraries\n", - "from lime import lime_tabular\n", - "import numpy as np\n", - "import pandas as pd\n", - "import json\n", - "\n", - "def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode=\"classification\"):\n", - " \"\"\"\n", - " Create a LIME explainer object\n", - " \"\"\"\n", - " if categorical_features is None:\n", - " categorical_features = []\n", - " \n", - " explainer = lime_tabular.LimeTabularExplainer(\n", - " training_data=np.array(X_train),\n", - " feature_names=feature_names,\n", - " class_names=class_names,\n", - " categorical_features=categorical_features,\n", - " mode=mode,\n", - " verbose=True,\n", - " random_state=42\n", - " )\n", - " \n", - " return explainer\n", - "\n", - "def create_lime_json(variant_ids, feature_names, processed_df, model, explainer, output_file=\"./lime_values.json\", num_features=20):\n", - " \"\"\"\n", - " Create JSON File from LIME explanations\n", - " \"\"\"\n", - " json_data = []\n", - " \n", - " # Get explanations for all instances\n", - " for idx, instance in enumerate(processed_df.values):\n", - " # Get explanation for this instance\n", - " if hasattr(model, 'predict_proba'):\n", - " explanation = explainer.explain_instance(\n", - " instance, \n", - " model.predict_proba,\n", - " num_features=num_features\n", - " )\n", - " else:\n", - " explanation = explainer.explain_instance(\n", - " instance, \n", - " model.predict,\n", - " num_features=num_features\n", - " )\n", - " \n", - " # Get the feature explanations as list of (feature, importance) tuples\n", - " exp_list = explanation.as_list()\n", - " \n", - " # Create entry for this instance\n", - " entry = {\n", - " \"variant_id\": variant_ids[idx],\n", - " \"base_value\": float(explanation.intercept), # Added intercept\n", - " \"confidence\": float(explanation.score), # Added confidence score\n", - " \"prediction_local\": float(explanation.local_pred), # Added local prediction\n", - " \"model_output_score\": {},\n", - " \"feature_values\": {}\n", - " }\n", - " \n", - " # Add feature contributions and values\n", - " for feature_name in feature_names:\n", - " # Find matching feature in explanation\n", - " feature_exp = next((x for x in exp_list if feature_name in x[0]), None)\n", - " \n", - " # Add contribution (0 if feature not in top features)\n", - " entry[\"model_output_score\"][feature_name] = float(feature_exp[1]) if feature_exp else 0.0\n", - " \n", - " # Add actual feature value\n", - " entry[\"feature_values\"][feature_name] = float(instance[feature_names.index(feature_name)])\n", - " \n", - " json_data.append(entry)\n", - " \n", - " # Convert to JSON string with proper formatting\n", - " json_string = json.dumps(json_data, indent=2, default=convert_numpy_to_python)\n", - " \n", - " # Save to file\n", - " with open(output_file, 'w') as f:\n", - " f.write(json_string)\n", - " \n", - " return json_string" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bad81388-859c-4686-960a-0c058ae3f36e", - "metadata": {}, - "outputs": [], - "source": [ - "model = interpreter.model" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c1315bb9-e2db-4f9a-9c2a-7226132dfb4d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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conservationScoreDGVgnomadAFgnomadAFgLRT_scoreLRT_OmegagnomadGeneZscoregnomadGenePLIgnomadGeneOELofUpperIMPACTCADD_PHRED...cons_splice_donor_region_variantIMPACT.from.TierTierADTierARTierAR.adjNo.Var.HMNo.Var.HNo.Var.MNo.Var.Lsimple_repeat
10-135085084-C-T1.00.0000190.0000190.0000000.0000000.380492.198300e-140.9263.024.600000...03.02.02.02.02.00.02.00.00
7-102281151-G-GA3.00.0000000.0000000.1263761.3419250.181701.130200e-011.8454.09.240707...04.01.03.03.01.01.00.00.00
6-17602826-G-T3.00.0000000.0000000.0000000.0000000.300171.747100e-020.6874.040.000000...04.01.01.01.04.02.02.00.00
18-42532220-G-A3.00.0000080.0000320.0000000.0000001.096501.000000e+000.1103.029.000000...03.02.03.03.01.00.01.00.00
23-39932684-A-G3.00.0000000.0000000.0000000.0000001.879701.000000e+000.1413.024.400000...03.02.03.03.01.00.01.00.00
..................................................................
6-32485548-AGTG-A1.00.0000280.0000000.1263761.3419250.606373.635900e-071.8491.09.240707...01.04.04.04.07.04.03.00.00
6-32485856-T-C1.00.0009500.0260430.0000000.0000000.606373.635900e-071.8494.031.000000...04.01.01.01.07.04.03.00.00
6-32485889-G-A1.00.0051050.0028470.0000000.0000000.606373.635900e-071.8491.05.533000...01.04.04.04.07.04.03.00.00
6-32487420-T-C1.00.0369300.1205430.3372121.3863300.606373.635900e-071.8493.09.694000...03.02.01.51.57.04.03.00.00
24-21154426-G-A3.00.0000000.0000000.0000000.000000-18.516001.860500e-1641.9951.010.920000...01.04.04.04.00.00.00.00.00
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2511 rows × 41 columns

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" - ], - "text/plain": [ - " conservationScoreDGV gnomadAF gnomadAFg LRT_score \\\n", - "10-135085084-C-T 1.0 0.000019 0.000019 0.000000 \n", - "7-102281151-G-GA 3.0 0.000000 0.000000 0.126376 \n", - "6-17602826-G-T 3.0 0.000000 0.000000 0.000000 \n", - "18-42532220-G-A 3.0 0.000008 0.000032 0.000000 \n", - "23-39932684-A-G 3.0 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... \n", - "6-32485548-AGTG-A 1.0 0.000028 0.000000 0.126376 \n", - "6-32485856-T-C 1.0 0.000950 0.026043 0.000000 \n", - "6-32485889-G-A 1.0 0.005105 0.002847 0.000000 \n", - "6-32487420-T-C 1.0 0.036930 0.120543 0.337212 \n", - "24-21154426-G-A 3.0 0.000000 0.000000 0.000000 \n", - "\n", - " LRT_Omega gnomadGeneZscore gnomadGenePLI \\\n", - "10-135085084-C-T 0.000000 0.38049 2.198300e-14 \n", - "7-102281151-G-GA 1.341925 0.18170 1.130200e-01 \n", - "6-17602826-G-T 0.000000 0.30017 1.747100e-02 \n", - "18-42532220-G-A 0.000000 1.09650 1.000000e+00 \n", - "23-39932684-A-G 0.000000 1.87970 1.000000e+00 \n", - "... ... ... ... \n", - "6-32485548-AGTG-A 1.341925 0.60637 3.635900e-07 \n", - "6-32485856-T-C 0.000000 0.60637 3.635900e-07 \n", - "6-32485889-G-A 0.000000 0.60637 3.635900e-07 \n", - "6-32487420-T-C 1.386330 0.60637 3.635900e-07 \n", - "24-21154426-G-A 0.000000 -18.51600 1.860500e-164 \n", - "\n", - " gnomadGeneOELofUpper IMPACT CADD_PHRED ... \\\n", - "10-135085084-C-T 0.926 3.0 24.600000 ... \n", - "7-102281151-G-GA 1.845 4.0 9.240707 ... \n", - "6-17602826-G-T 0.687 4.0 40.000000 ... \n", - "18-42532220-G-A 0.110 3.0 29.000000 ... \n", - "23-39932684-A-G 0.141 3.0 24.400000 ... \n", - "... ... ... ... ... \n", - "6-32485548-AGTG-A 1.849 1.0 9.240707 ... \n", - "6-32485856-T-C 1.849 4.0 31.000000 ... \n", - "6-32485889-G-A 1.849 1.0 5.533000 ... \n", - "6-32487420-T-C 1.849 3.0 9.694000 ... \n", - "24-21154426-G-A 1.995 1.0 10.920000 ... \n", - "\n", - " cons_splice_donor_region_variant IMPACT.from.Tier TierAD \\\n", - "10-135085084-C-T 0 3.0 2.0 \n", - "7-102281151-G-GA 0 4.0 1.0 \n", - "6-17602826-G-T 0 4.0 1.0 \n", - "18-42532220-G-A 0 3.0 2.0 \n", - "23-39932684-A-G 0 3.0 2.0 \n", - "... ... ... ... \n", - "6-32485548-AGTG-A 0 1.0 4.0 \n", - "6-32485856-T-C 0 4.0 1.0 \n", - "6-32485889-G-A 0 1.0 4.0 \n", - "6-32487420-T-C 0 3.0 2.0 \n", - "24-21154426-G-A 0 1.0 4.0 \n", - "\n", - " TierAR TierAR.adj No.Var.HM No.Var.H No.Var.M \\\n", - "10-135085084-C-T 2.0 2.0 2.0 0.0 2.0 \n", - "7-102281151-G-GA 3.0 3.0 1.0 1.0 0.0 \n", - "6-17602826-G-T 1.0 1.0 4.0 2.0 2.0 \n", - "18-42532220-G-A 3.0 3.0 1.0 0.0 1.0 \n", - "23-39932684-A-G 3.0 3.0 1.0 0.0 1.0 \n", - "... ... ... ... ... ... \n", - "6-32485548-AGTG-A 4.0 4.0 7.0 4.0 3.0 \n", - "6-32485856-T-C 1.0 1.0 7.0 4.0 3.0 \n", - "6-32485889-G-A 4.0 4.0 7.0 4.0 3.0 \n", - "6-32487420-T-C 1.5 1.5 7.0 4.0 3.0 \n", - "24-21154426-G-A 4.0 4.0 0.0 0.0 0.0 \n", - "\n", - " No.Var.L simple_repeat \n", - "10-135085084-C-T 0.0 0 \n", - "7-102281151-G-GA 0.0 0 \n", - "6-17602826-G-T 0.0 0 \n", - "18-42532220-G-A 0.0 0 \n", - "23-39932684-A-G 0.0 0 \n", - "... ... ... \n", - "6-32485548-AGTG-A 0.0 0 \n", - "6-32485856-T-C 0.0 0 \n", - "6-32485889-G-A 0.0 0 \n", - "6-32487420-T-C 0.0 0 \n", - "24-21154426-G-A 0.0 0 \n", - "\n", - "[2511 rows x 41 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "processed_df" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "cdf6763b-b355-418f-8f68-9b1f0587a7e0", - "metadata": {}, - "outputs": [], - "source": [ - "explainer = create_lime_explainer(\n", - " X_train=processed_df,\n", - " feature_names=processed_df.columns,\n", - " mode='classification', # or 'regression' depending on your task\n", - " class_names = ['is_not', 'is_causal']\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "161ccab2-fde1-4982-93b9-2c6772b603e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explainer" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "fd5a3dbf-cb7d-4609-a5e3-9dcd7a197688", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intercept -1.4518621776516535e-06\n", - "Prediction_local [1.5876037e-05]\n", - "Right: 0.00039676012\n" - ] - } - ], - "source": [ - "explanation = explainer.explain_instance(processed_df.values[0], model.predict_proba, num_features = 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7a9fdab6-0dbe-4a79-b3e9-a7bd67555245", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: -1.4518621776516535e-06}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explanation.intercept" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d70a0175-ef28-4b3e-9e89-7027cdea175f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.5876037e-05])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explanation.local_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b31bc13b-3838-4d78-a420-6cf45ff48cf9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "explainer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb3cb60a-e4c4-4a89-9635-db908f8b36d5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0767273-c2a2-4042-9a9d-3e239fed48f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/bin/model_interpreter/results/lime_analysis.json b/bin/model_interpreter/results/lime_analysis.json deleted file mode 100644 index e7a10b8..0000000 --- a/bin/model_interpreter/results/lime_analysis.json +++ /dev/null @@ -1,195860 +0,0 @@ -[ - { - "variant_id": "10-135085084-C-T", - "base_value": 0.001294243269921963, - "confidence": 0.006173459274502857, - "prediction_local": 0.0002548341544847479, - "model_output_score": { - "No.Var.L <= 0.00": -0.0007777347546341007, - "zyg <= 1.00": -0.0004272535032511538, - "simple_repeat <= 0.00": -0.0003072199055137973, - "spliceAImax": 0.0002474872715900633, - "CADD_PHRED": 0.00022531177637177355 - }, - "feature_values": { - "conservationScoreDGV": 1.0, - "gnomadAF": 1.864e-05, - "gnomadAFg": 1.864e-05, - "LRT_score": 0.0, - "LRT_Omega": 0.0, - "gnomadGeneZscore": 0.38049, - "gnomadGenePLI": 2.1983e-14, - "gnomadGeneOELofUpper": 0.926, - "IMPACT": 3.0, - "CADD_PHRED": 24.6, - "conservationScoreGnomad": 2.0, - "conservationScoreOELof": 1.0, - "zyg": 1.0, - "ESP6500_AA_AF": 0.0, - "ESP6500_EA_AF": 0.0, - "hom": 0.0, - "spliceAImax": 0.01, - "cons_transcript_ablation": 0.0, - "cons_splice_acceptor_variant": 0.0, - "cons_splice_donor_variant": 0.0, - "cons_stop_gained": 0.0, - "cons_frameshift_variant": 0.0, - "cons_stop_lost": 0.0, - "cons_start_lost": 0.0, - "cons_transcript_amplification": 0.0, - "cons_inframe_insertion": 0.0, - "cons_inframe_deletion": 0.0, - "cons_missense_variant": 1.0, - "cons_protein_altering_variant": 0.0, - "cons_splice_region_variant": 0.0, - "cons_splice_donor_5th_base_variant": 0.0, - "cons_splice_donor_region_variant": 0.0, - "IMPACT.from.Tier": 3.0, - "TierAD": 2.0, - "TierAR": 2.0, - "TierAR.adj": 2.0, - "No.Var.HM": 2.0, - "No.Var.H": 0.0, - "No.Var.M": 2.0, - "No.Var.L": 0.0, - "simple_repeat": 0.0 - }, - "feature_explanations": [ - { - "feature": "No.Var.L <= 0.00", - "importance": -0.0007777347546341007 - }, - { - "feature": "zyg <= 1.00", - "importance": -0.0004272535032511538 - }, - { - "feature": "simple_repeat <= 0.00", - "importance": -0.0003072199055137973 - }, - { - "feature": "spliceAImax > 0.00", - "importance": 0.0002474872715900633 - }, - { - "feature": "CADD_PHRED > 10.30", - "importance": 0.00022531177637177355 - } - ] - }, - { - "variant_id": "7-102281151-G-GA", - "base_value": 4.780132221806786e-06, - "confidence": 0.027151243167847783, - "prediction_local": 9.433351257437148e-06, - "model_output_score": { - "cons_splice_region_variant <= 0.00": -4.886193782135828e-06, - "gnomadAF <= 0.00": 4.768534545419402e-06, - "spliceAImax": 4.041017341407845e-06, - "zyg": 3.0335751904298185e-06, - "3.75 < CADD_PHRED <= 9.24": -2.303714259490877e-06 - }, - "feature_values": { - "conservationScoreDGV": 3.0, - "gnomadAF": 0.0, - "gnomadAFg": 0.0, - "LRT_score": 0.1263759060895084, - "LRT_Omega": 1.341924612619222, - 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"gnomadGenePLI": 0.22050103545188904, - "gnomadGeneOELofUpper": 0.19196084141731262, - "IMPACT": 0.2995164394378662, - "CADD_PHRED": -0.794739305973053, - "conservationScoreGnomad": 0.0, - "conservationScoreOELof": 0.0, - "zyg": 0.3303452730178833, - "ESP6500_AA_AF": 0.0, - "ESP6500_EA_AF": -0.0005441090906970203, - "hom": -2.7936060428619385, - "spliceAImax": 0.050390202552080154, - "cons_transcript_ablation": 0.0, - "cons_splice_acceptor_variant": 0.004777200985699892, - "cons_splice_donor_variant": 0.0015439316630363464, - "cons_stop_gained": 0.010039666667580605, - "cons_frameshift_variant": -0.01769348792731762, - "cons_stop_lost": 0.0, - "cons_start_lost": 0.0, - "cons_transcript_amplification": 0.0, - "cons_inframe_insertion": 0.0, - "cons_inframe_deletion": -0.0001818835735321045, - "cons_missense_variant": -0.006185370497405529, - "cons_protein_altering_variant": 0.0, - "cons_splice_region_variant": 0.008251354098320007, - "cons_splice_donor_5th_base_variant": 0.0, - "cons_splice_donor_region_variant": 0.0, - "IMPACT.from.Tier": 2.363917350769043, - "TierAD": 0.0, - "TierAR": 0.1267298460006714, - "TierAR.adj": 0.0, - "No.Var.HM": -0.04336792230606079, - "No.Var.H": -0.05897371098399162, - "No.Var.M": 0.012213566340506077, - "No.Var.L": 0.013540907762944698, - "simple_repeat": 0.0011542135616764426 - }, - "feature_values": { - "conservationScoreDGV": 1.0, - "gnomadAF": 0.03693, - "gnomadAFg": 0.120543, - "LRT_score": 0.337212, - "LRT_Omega": 1.38633, - "gnomadGeneZscore": 0.60637, - "gnomadGenePLI": 3.6359e-07, - "gnomadGeneOELofUpper": 1.849, - "IMPACT": 3.0, - "CADD_PHRED": 9.694, - "conservationScoreGnomad": 1.0, - "conservationScoreOELof": 1.0, - "zyg": 2.0, - "ESP6500_AA_AF": 0.0, - "ESP6500_EA_AF": 0.0, - "hom": 77.0, - "spliceAImax": 0.0, - "cons_transcript_ablation": 0.0, - "cons_splice_acceptor_variant": 0.0, - "cons_splice_donor_variant": 0.0, - "cons_stop_gained": 0.0, - "cons_frameshift_variant": 0.0, - "cons_stop_lost": 0.0, - "cons_start_lost": 0.0, - "cons_transcript_amplification": 0.0, - "cons_inframe_insertion": 0.0, - "cons_inframe_deletion": 0.0, - "cons_missense_variant": 1.0, - "cons_protein_altering_variant": 0.0, - "cons_splice_region_variant": 0.0, - "cons_splice_donor_5th_base_variant": 0.0, - "cons_splice_donor_region_variant": 0.0, - "IMPACT.from.Tier": 3.0, - "TierAD": 2.0, - "TierAR": 1.5, - "TierAR.adj": 1.5, - "No.Var.HM": 7.0, - "No.Var.H": 4.0, - "No.Var.M": 3.0, - "No.Var.L": 0.0, - "simple_repeat": 0.0 - } - }, - { - "variant_id": "24-21154426-G-A", - "base_value": -10.256918907165527, - "model_output_score": { - "conservationScoreDGV": 0.004759486764669418, - "gnomadAF": -0.40886542201042175, - "gnomadAFg": -0.10506249219179153, - "LRT_score": -0.19558551907539368, - "LRT_Omega": -0.18366000056266785, - "gnomadGeneZscore": -0.8151912093162537, - "gnomadGenePLI": -0.5185702443122864, - "gnomadGeneOELofUpper": -0.9614138007164001, - "IMPACT": -0.34536826610565186, - "CADD_PHRED": -2.3626279830932617, - "conservationScoreGnomad": 0.0, - "conservationScoreOELof": 0.0, - "zyg": -0.15922428667545319, - "ESP6500_AA_AF": 0.0, - "ESP6500_EA_AF": -7.89832483860664e-06, - "hom": 0.010412431322038174, - "spliceAImax": -1.5045604705810547, - "cons_transcript_ablation": 0.0, - "cons_splice_acceptor_variant": 0.005138596054166555, - "cons_splice_donor_variant": -0.00945999938994646, - "cons_stop_gained": 0.006892947014421225, - "cons_frameshift_variant": -0.015127758495509624, - "cons_stop_lost": 0.0, - "cons_start_lost": 0.0, - "cons_transcript_amplification": 0.0, - "cons_inframe_insertion": 0.0, - "cons_inframe_deletion": -0.0006365925073623657, - "cons_missense_variant": -0.059125833213329315, - "cons_protein_altering_variant": 0.0, - "cons_splice_region_variant": -0.11330326646566391, - "cons_splice_donor_5th_base_variant": 0.0, - "cons_splice_donor_region_variant": 0.0, - "IMPACT.from.Tier": -3.4541122913360596, - "TierAD": 0.0, - "TierAR": -0.03708724305033684, - "TierAR.adj": 0.0, - "No.Var.HM": -0.6974364519119263, - "No.Var.H": -0.03599334880709648, - "No.Var.M": -0.2082396000623703, - "No.Var.L": -0.0194710586220026, - "simple_repeat": 0.008274316787719727 - }, - "feature_values": { - "conservationScoreDGV": 3.0, - "gnomadAF": 0.0, - "gnomadAFg": 0.0, - "LRT_score": 0.0, - "LRT_Omega": 0.0, - "gnomadGeneZscore": -18.516, - "gnomadGenePLI": 1.8605e-164, - "gnomadGeneOELofUpper": 1.995, - "IMPACT": 1.0, - "CADD_PHRED": 10.92, - "conservationScoreGnomad": 2.0, - "conservationScoreOELof": 1.0, - "zyg": 1.0, - "ESP6500_AA_AF": 0.0, - "ESP6500_EA_AF": 0.0, - "hom": 0.0, - "spliceAImax": 0.0, - "cons_transcript_ablation": 0.0, - "cons_splice_acceptor_variant": 0.0, - "cons_splice_donor_variant": 0.0, - "cons_stop_gained": 0.0, - "cons_frameshift_variant": 0.0, - "cons_stop_lost": 0.0, - "cons_start_lost": 0.0, - "cons_transcript_amplification": 0.0, - "cons_inframe_insertion": 0.0, - "cons_inframe_deletion": 0.0, - "cons_missense_variant": 0.0, - "cons_protein_altering_variant": 0.0, - "cons_splice_region_variant": 0.0, - "cons_splice_donor_5th_base_variant": 0.0, - "cons_splice_donor_region_variant": 0.0, - "IMPACT.from.Tier": 1.0, - "TierAD": 4.0, - "TierAR": 4.0, - "TierAR.adj": 4.0, - "No.Var.HM": 0.0, - "No.Var.H": 0.0, - "No.Var.M": 0.0, - "No.Var.L": 0.0, - "simple_repeat": 0.0 - } - } -] \ No newline at end of file diff --git a/bin/model_interpreter/templates/genomic_variant_report_template.html b/bin/model_interpreter/templates/genomic_variant_report_template.html deleted file mode 100644 index 9a04fff..0000000 --- a/bin/model_interpreter/templates/genomic_variant_report_template.html +++ /dev/null @@ -1,163 +0,0 @@ - - - - - Genomic Variant Analysis Report - - - -
-

Genomic Variant Analysis Report

-

Generated on: {{ timestamp }}

-
- -
-
-

Analysis Overview

-

Model Type: {{ model_type }}

-

Total Variants Analyzed: {{ total_variants }}

-

Features Considered: {{ feature_count }}

-
-
- -
-

Global Feature Importance

- Feature Importance Plot -
- -
-

SHAP Summary Plot

- SHAP Summary Plot -
- -
-

Individual Variant Analysis

- {% for variant in variants %} -
-
- Variant ID: {{ variant.variant_id }} -
- -

Prediction: - - {{ "Pathogenic" if variant.prediction == 1 else "Benign" }} - - {% if variant.probability is not none %} - (Confidence: {{ "%.1f"|format(variant.probability * 100) }}%) - {% endif %} -

- -

Feature Impacts:

-
- Feature Impact Plot -
- - - - - - - - {% for impact in variant.feature_impacts[:5] %} - - - - - - {% endfor %} -
FeatureImpactMagnitude
{{ impact.feature }} -
-
-
-
- 		{{ "%.3f"|format(impact.magnitude) }}
-
- {% endfor %} -
- - \ No newline at end of file diff --git a/bin/model_interpreter/variant_model_interpreter.py b/bin/model_interpreter/variant_model_interpreter.py index 14b5c51..9981673 100644 --- a/bin/model_interpreter/variant_model_interpreter.py +++ b/bin/model_interpreter/variant_model_interpreter.py @@ -1,4 +1,4 @@ -from bin import create_lime_explainer, create_lime_json, create_shap_json +from .bin import create_lime_explainer, create_lime_json, create_shap_json import joblib import pandas as pd @@ -29,30 +29,43 @@ def __init__(self): self.variant_ids = None self.feature_names = None - def load_model(self, model_path: str) -> None: + def set_model(self, model) -> None: """ - Load the model and extract feature names + Accept a pre-loaded model directly and extract its feature names. """ try: - self.model = joblib.load(model_path) - - # Extract feature names used during training - if isinstance(self.model, (RandomForestClassifier, RandomForestRegressor)): + # Check the model type and extract feature names + if isinstance(model, (RandomForestClassifier, RandomForestRegressor)): self.model_type = "random_forest" - self.task_type = "classification" if isinstance(self.model, RandomForestClassifier) else "regression" - self.model_features = list(self.model.feature_names_in_) - elif isinstance(self.model, xgb.XGBModel): + self.task_type = "classification" if isinstance(model, RandomForestClassifier) else "regression" + self.model_features = list(model.feature_names_in_) + elif isinstance(model, xgb.XGBModel): self.model_type = "xgboost" - self.task_type = "classification" if self.model.objective.startswith('binary:') else "regression" - self.model_features = list(self.model.get_booster().feature_names) + self.task_type = "classification" if model.objective.startswith('binary:') else "regression" + self.model_features = list(model.get_booster().feature_names) else: - raise ValueError(f"Unsupported model type: {type(self.model)}") + raise ValueError(f"Unsupported model type: {type(model)}") - logger.info(f"Loaded model with {len(self.model_features)} features") + self.model = model + self.is_model_loaded = True + logger.info(f"Model set successfully with {len(self.model_features)} features") logger.info(f"Model features: {self.model_features}") - self.is_model_loaded = True + except Exception as e: + logger.error(f"Error setting model: {str(e)}") + raise + + def load_model(self, model_path: str) -> None: + """ + Load a model from a file using joblib. + """ + try: + # Load the model from a file + loaded_model = joblib.load(model_path) + # Delegate to set_model to extract features and set properties + self.set_model(loaded_model) + except Exception as e: logger.error(f"Error loading model: {str(e)}") raise @@ -125,6 +138,16 @@ def calculate_shap_values(self, df: pd.DataFrame) -> None: self.shap_values = self.shap_explainer(df) logger.info("SHAP values calculated successfully") + + # Conditionally adjust SHAP values for RandomForestClassifier + if self.model_type == "random_forest" and self.task_type == "classification": + # Extract SHAP values for the positive class + self.shap_values.values = self.shap_values.values[:, :, 1] + logger.info("Extracted SHAP values for positive class (class 1)") + + # Set variant_ids and feature_names if not already set + self.variant_ids = list(df.index) + self.feature_names = list(df.columns) except Exception as e: logger.error(f"Error calculating SHAP values: {str(e)}") diff --git a/main.nf b/main.nf index 2aa9426..fde87f0 100644 --- a/main.nf +++ b/main.nf @@ -567,57 +567,30 @@ process MERGE_SCORES_BY_CHROMOSOME { process PREDICTION { publishDir "${params.outdir}/${params.run_id}/prediction/", mode: "copy" - + input: path merged_matrix path merged_compressed_scores - path ref_predict_new_dir path ref_model_inputs_dir - + output: path "conf_4Model/*.csv" path "conf_4Model/integrated/*.csv" - + path "lime_outputs" + path "shap_outputs" + script: """ mkdir final_matrix_expanded mkdir conf_4Model - + run_final.py ${params.run_id} merge_rm.py ${params.run_id} extraModel_main.py -id ${params.run_id} """ } -process MODEL_INTERPRETABILITY { - publishDir "${params.outdir}/${params.run_id}/model_interpretability/", mode: "copy" - - input: - path feature_matrix // Now this will be each CSV file that needs interpretation - path model_file // The model file - - output: - path "${feature_matrix.baseName}/shap_outputs/shap_values.json", emit: shap_results - path "${feature_matrix.baseName}/lime_outputs/lime_values.json", emit: lime_results - - script: - """ - mkdir -p "${feature_matrix.baseName}/shap_outputs" "${feature_matrix.baseName}/lime_outputs" "${feature_matrix.baseName}/outputs" - - echo "Analyzing predictions using feature matrix: ${feature_matrix}" - echo "Using model: ${model_file}" - - python ${projectDir}/bin/model_interpreter/main.py \ - --model-path ${model_file} \ - --input-path ${feature_matrix} \ - --output-path ${feature_matrix.baseName}/outputs/values.json - - mv ${feature_matrix.baseName}/outputs/*_shap.json ${feature_matrix.baseName}/shap_outputs/ - mv ${feature_matrix.baseName}/outputs/*_lime.json ${feature_matrix.baseName}/lime_outputs/ - """ -} - workflow VCF_PRE_PROCESS { take: @@ -746,17 +719,11 @@ workflow { ) // Run Prediction on the final merged output + // Includes Model interpretability step to predict - keep it all together in one place since model already loaded? PREDICTION( MERGE_SCORES_BY_CHROMOSOME.out.merged_matrix, MERGE_SCORES_BY_CHROMOSOME.out.merged_compressed_scores, file(params.ref_predict_new_dir), file(params.ref_model_inputs_dir) ) - - // Add Model Interpretability step - now with file paths from original - MODEL_INTERPRETABILITY( - PREDICTION.out.flatten().filter(~/.*\.csv$/), // Get ALL CSV files - file("${params.ref_predict_new_dir}/hg19/final_model_wo_bg_val.job") - ) - } diff --git a/nextflow.config b/nextflow.config index a70ecc0..e4b4a70 100644 --- a/nextflow.config +++ b/nextflow.config @@ -86,7 +86,7 @@ process { errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } maxRetries = 5 - container = "chaozhongliu/aim-lite:latest" + container = "aim-lite:latest" cache = "lenient" } diff --git a/requirements.txt b/requirements.txt index 9af4ba4..56147f6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,4 @@ requests tables scikit-learn==1.1.2 shap -lime +lime \ No newline at end of file From cf3e403818ba27dc5ee768ac3b33d21a80aa81d9 Mon Sep 17 00:00:00 2001 From: jjia1 Date: Fri, 15 Nov 2024 17:59:57 +0000 Subject: [PATCH 09/80] addressed review in PR #103 --- .gitignore | 1 + bin/model_interpreter/bin/LIME_TO_JSON.py | 9 +++--- bin/model_interpreter/bin/SHAP_to_JSON.py | 13 +++----- bin/model_interpreter/main.nf | 32 ------------------- .../variant_model_interpreter.py | 15 ++++----- main.nf | 2 +- nextflow.config | 2 +- 7 files changed, 17 insertions(+), 57 deletions(-) delete mode 100644 bin/model_interpreter/main.nf diff --git a/.gitignore b/.gitignore index fd7eea8..c8f5e51 100644 --- a/.gitignore +++ b/.gitignore @@ -27,3 +27,4 @@ params.yaml # Remove some files from model interpreter */model_interpreter/results */model_interpreter/templates +*/model_interpreter/main.nf diff --git a/bin/model_interpreter/bin/LIME_TO_JSON.py b/bin/model_interpreter/bin/LIME_TO_JSON.py index 0a9310d..ea9f673 100644 --- a/bin/model_interpreter/bin/LIME_TO_JSON.py +++ b/bin/model_interpreter/bin/LIME_TO_JSON.py @@ -1,14 +1,13 @@ # Import necessary libraries -from lime import lime_tabular -import numpy as np -import pandas as pd -import json - import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) from utils.numpy_to_python import convert_numpy_to_python +from lime import lime_tabular +import numpy as np +import json + def create_lime_explainer(X_train, feature_names, categorical_features=None, class_names=['negative', 'positive'], mode="classification"): """ Create a LIME explainer object diff --git a/bin/model_interpreter/bin/SHAP_to_JSON.py b/bin/model_interpreter/bin/SHAP_to_JSON.py index fdbebb1..47c8351 100644 --- a/bin/model_interpreter/bin/SHAP_to_JSON.py +++ b/bin/model_interpreter/bin/SHAP_to_JSON.py @@ -1,14 +1,9 @@ -import joblib -import pandas as pd -import numpy as np -import shap -from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor -import xgboost as xgb -import json - import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) + +import numpy as np +import json from utils.numpy_to_python import convert_numpy_to_python def create_shap_json(variant_ids, feature_names, shap_values, output_file="./results/shap_values.json"): @@ -51,4 +46,4 @@ def create_shap_json(variant_ids, feature_names, shap_values, output_file="./res with open(output_file, 'w') as f: f.write(json_string) - return json_string \ No newline at end of file + return json_string diff --git a/bin/model_interpreter/main.nf b/bin/model_interpreter/main.nf deleted file mode 100644 index 64a6a4f..0000000 --- a/bin/model_interpreter/main.nf +++ /dev/null @@ -1,32 +0,0 @@ -// modules/model_interpreter/main.nf -process modelInterpreterShapPy { - cpus '48' - publishDir "./published${context.publishDirSuffix}" - - input: - path modelPath - path inputDataPath - val context - - output: - path 'shapOutputs/shap_values.json', emit: shapValuesPath - - """ - mkdir -p ./shapOutputs - python ${moduleDir}/main.py \ - --model-path ${modelPath} \ - --input-path ${inputDataPath} \ - --output-path ./shapOutputs/shap_values.json - """ -} - -workflow modelInterpreter { - take: - modelPath - inputDataPath - context - main: - modelInterpreterShapPy(modelPath, inputDataPath, context) - emit: - shapValuesPath = modelInterpreterShapPy.out.shapValuesPath -} \ No newline at end of file diff --git a/bin/model_interpreter/variant_model_interpreter.py b/bin/model_interpreter/variant_model_interpreter.py index 9981673..6ce3675 100644 --- a/bin/model_interpreter/variant_model_interpreter.py +++ b/bin/model_interpreter/variant_model_interpreter.py @@ -1,17 +1,13 @@ -from .bin import create_lime_explainer, create_lime_json, create_shap_json +from typing import List, Optional, Dict, Any +import json +import logging import joblib import pandas as pd -import numpy as np import shap -from lime import lime_tabular from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor import xgboost as xgb -from typing import Dict -import logging -import json -from typing import List, Optional, Union, Dict, Any - +from .bin import create_lime_explainer, create_lime_json, create_shap_json # Configure logging logging.basicConfig(level=logging.INFO) @@ -210,4 +206,5 @@ def run_lime_analysis(self, processed_df: pd.DataFrame, num_features = 20, model=self.model, explainer=self.lime_explainer, output_file=output_file - ) \ No newline at end of file + ) + \ No newline at end of file diff --git a/main.nf b/main.nf index fde87f0..b2b04b2 100644 --- a/main.nf +++ b/main.nf @@ -719,7 +719,7 @@ workflow { ) // Run Prediction on the final merged output - // Includes Model interpretability step to predict - keep it all together in one place since model already loaded? + // Includes Model interpretability step to predict PREDICTION( MERGE_SCORES_BY_CHROMOSOME.out.merged_matrix, MERGE_SCORES_BY_CHROMOSOME.out.merged_compressed_scores, diff --git a/nextflow.config b/nextflow.config index e4b4a70..a70ecc0 100644 --- a/nextflow.config +++ b/nextflow.config @@ -86,7 +86,7 @@ process { errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } maxRetries = 5 - container = "aim-lite:latest" + container = "chaozhongliu/aim-lite:latest" cache = "lenient" } From 55b8d02fc639313c98a842c1a80d9022a1aee7fc Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Mon, 18 Nov 2024 14:44:38 -0600 Subject: [PATCH 10/80] Disable LIME analysis as it takes too long time --- bin/extraModel_main.py | 19 ++++++++----------- main.nf | 1 - 2 files changed, 8 insertions(+), 12 deletions(-) diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index e4c7ce5..050ddb6 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -47,32 +47,29 @@ print(f"### Start generating recessive matrix and running predictions.") def run_model_interpretation(model, data_df, features, output_prefix, run_id, model_type): - """Run SHAP and LIME analysis for a specific model""" + """Run SHAP analysis for a specific model""" interpreter = ModelInterpreter() print(interpreter, model) interpreter.set_model(model) # Set the already loaded model - + # Prepare data (use only the features used by this model) model_data = data_df.loc[:, features] - + # Calculate SHAP values interpreter.calculate_shap_values(model_data) - + print(model_data.index[0:9]) interpreter.variant_ids = list(model_data.index) print(model_data.columns[0:9]) interpreter.feature_names = list(model_data.columns) - - print(interpreter.model_type) + + print(interpreter.model_type) print(interpreter.shap_values.base_values) # Create output directories if they don't exist os.makedirs("shap_outputs", exist_ok=True) interpreter.run_shap_analysis(output_file=f"shap_outputs/{run_id}_{model_type}_shap_values.json") - - os.makedirs("lime_outputs", exist_ok=True) - interpreter.run_lime_analysis(model_data, output_file=f"lime_outputs/{run_id}_{model_type}_lime_values.json") - - + # os.makedirs("lime_outputs", exist_ok=True) + # interpreter.run_lime_analysis(model_data, output_file=f"lime_outputs/{run_id}_{model_type}_lime_values.json") def assign_ranking(df): pred_df = df.copy() diff --git a/main.nf b/main.nf index 099eb27..3ecd8f1 100644 --- a/main.nf +++ b/main.nf @@ -576,7 +576,6 @@ process PREDICTION { output: path "conf_4Model/*.csv" path "conf_4Model/integrated/*.csv" - path "lime_outputs" path "shap_outputs" script: From c3b4bcfa1cd824c6359f58c2639294f547bbeb72 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 20 Nov 2024 15:51:54 -0600 Subject: [PATCH 11/80] Validate VCF First Before Preprocessing --- main.nf | 16 +++++++++++++++- test.txt | 0 2 files changed, 15 insertions(+), 1 deletion(-) create mode 100644 test.txt diff --git a/main.nf b/main.nf index 3ecd8f1..bff316c 100644 --- a/main.nf +++ b/main.nf @@ -74,6 +74,19 @@ showUsage() showVersion() validateInputParams() +process VALIDATE_VCF { + container 'quay.io/biocontainers/vcftools:0.1.16--pl5321hdcf5f25_11' + + input: + path vcf + + output: + path "$vcf", emit: vcf + + """ + vcf-validator $vcf + """ +} // Process to handle the VCF file process NORMALIZE_VCF { @@ -654,7 +667,8 @@ workflow PHRANK_SCORING { } workflow { - NORMALIZE_VCF(params.input_vcf) + VALIDATE_VCF(params.input_vcf) + NORMALIZE_VCF(VALIDATE_VCF.out.vcf) BUILD_REFERENCE_INDEX() VCF_PRE_PROCESS( diff --git a/test.txt b/test.txt new file mode 100644 index 0000000..e69de29 From 539a79cfddcf1a6f45ca1bb905932b8055077275 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 21 Nov 2024 16:46:34 -0600 Subject: [PATCH 12/80] Separate R Dockerfiles --- Dockerfiles/r.Dockerfile | 5 +++++ main.nf | 40 +++++++++++++++++++++++++++++++--------- 2 files changed, 36 insertions(+), 9 deletions(-) create mode 100644 Dockerfiles/r.Dockerfile diff --git a/Dockerfiles/r.Dockerfile b/Dockerfiles/r.Dockerfile new file mode 100644 index 0000000..a8598eb --- /dev/null +++ b/Dockerfiles/r.Dockerfile @@ -0,0 +1,5 @@ +FROM rocker/tidyverse + +# Install R libs +RUN R -e "install.packages('ontologyIndex',dependencies=TRUE, repos='http://cran.rstudio.com/')" +RUN R -e "install.packages('ontologySimilarity',dependencies=TRUE, repos='http://cran.rstudio.com/')" diff --git a/main.nf b/main.nf index bff316c..1246ded 100644 --- a/main.nf +++ b/main.nf @@ -364,6 +364,8 @@ process GENESYM_TO_PHRANK { process HPO_SIM { + container 'aim-lite-r' + input: path hpo path omim_hgmd_phen @@ -514,28 +516,43 @@ process ANNOTATE_BY_MODULES { """ } -process JOIN_TIER_PHRANK { +process ANNOTATE_TIER { + container 'aim-lite-r' tag "${scores.simpleName}" input: path scores path phrank - path ref_annot_dir path ref_var_tier_dir - path ref_merge_expand_dir output: - path "${scores.simpleName}_scores.txt.gz", emit: compressed_scores path "${scores.simpleName}_Tier.v2.tsv", emit: tier script: """ mv $scores scores.csv VarTierDiseaseDBFalse.R ${params.ref_ver} + mv Tier.v2.tsv ${scores.simpleName}_Tier.v2.tsv + """ +} + +process JOIN_PHRANK { + tag "${scores.simpleName}" + + input: + path scores + path phrank + path ref_merge_expand_dir + + output: + path "${scores.simpleName}_scores.txt.gz", emit: compressed_scores + + script: + """ + mv $scores scores.csv generate_new_matrix_2.py ${params.run_id} ${params.ref_ver} mv scores.txt.gz ${scores.simpleName}_scores.txt.gz - mv Tier.v2.tsv ${scores.simpleName}_Tier.v2.tsv """ } @@ -712,19 +729,24 @@ workflow { NORMALIZE_VCF.out.vcf, ) - JOIN_TIER_PHRANK ( + ANNOTATE_TIER ( ANNOTATE_BY_MODULES.out.scores, PHRANK_SCORING.out, - file(params.ref_annot_dir), file(params.ref_var_tier_dir), + ) + + JOIN_PHRANK ( + ANNOTATE_BY_MODULES.out.scores, + PHRANK_SCORING.out, file(params.ref_merge_expand_dir) ) + MERGE_SCORES_BY_CHROMOSOME( PHRANK_SCORING.out, - JOIN_TIER_PHRANK.out.tier.collect(), - JOIN_TIER_PHRANK.out.compressed_scores.collect(), + ANNOTATE_TIER.out.tier.collect(), + JOIN_PHRANK.out.compressed_scores.collect(), file(params.ref_annot_dir), file(params.ref_mod5_diffusion_dir), file(params.ref_merge_expand_dir) From e6de2c12783761c3c19d24dd65de55b09b8ea041 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 21 Nov 2024 16:48:41 -0600 Subject: [PATCH 13/80] Remove a line --- main.nf | 1 - 1 file changed, 1 deletion(-) diff --git a/main.nf b/main.nf index 1246ded..1bece2d 100644 --- a/main.nf +++ b/main.nf @@ -742,7 +742,6 @@ workflow { file(params.ref_merge_expand_dir) ) - MERGE_SCORES_BY_CHROMOSOME( PHRANK_SCORING.out, ANNOTATE_TIER.out.tier.collect(), From 0f6211736d092a438b4e35cd6dc3f4e4d3e9d48f Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 21 Nov 2024 16:50:57 -0600 Subject: [PATCH 14/80] Remove R Installation from main Dockerfile --- Dockerfile | 31 ------------------------------- 1 file changed, 31 deletions(-) diff --git a/Dockerfile b/Dockerfile index bd1733a..b0bd3f8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -37,37 +37,6 @@ RUN pip3 install --upgrade pip RUN pip3 install -r /opt/requirements.txt RUN pip3 install bgzip - -# Install R -RUN apt-get update -RUN apt install -y --no-install-recommends software-properties-common dirmngr -# Add the keys -RUN apt install wget -RUN wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc - -# add the R 4.0 repo from CRAN -- adjust 'focal' to 'groovy' or 'bionic' as needed -#RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver ha.pool.sks-keyservers.net --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver pgp.mit.edu --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || \ -# apt-key adv --keyserver keyserver.pgp.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 -#RUN add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" - -RUN add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" -RUN add-apt-repository universe -RUN apt-get update - -RUN apt install -y r-base r-base-core - -# Install R libs -RUN R -e "install.packages('data.table',dependencies=TRUE, repos='http://cran.rstudio.com/')" -RUN R -e "install.packages('dplyr',dependencies=TRUE, repos='http://cran.rstudio.com/')" -RUN R -e "install.packages('ontologyIndex',dependencies=TRUE, repos='http://cran.rstudio.com/')" -RUN R -e "install.packages('ontologySimilarity',dependencies=TRUE, repos='http://cran.rstudio.com/')" -RUN R -e "install.packages('tidyverse',dependencies=TRUE, repos='http://cran.rstudio.com/')" - - - # Install bcftools RUN wget https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2 RUN mv bcftools-1.20.tar.bz2 /opt/bcftools-1.20.tar.bz2 From 42cd31496b56f6f04bb48946c5a0df0b5608ba45 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Thu, 21 Nov 2024 17:18:47 -0600 Subject: [PATCH 15/80] Use docker image from docker hub --- main.nf | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/main.nf b/main.nf index 1bece2d..e4c0c0b 100644 --- a/main.nf +++ b/main.nf @@ -364,7 +364,7 @@ process GENESYM_TO_PHRANK { process HPO_SIM { - container 'aim-lite-r' + container 'zhandongliulab/aim-lite-r' input: path hpo @@ -517,7 +517,7 @@ process ANNOTATE_BY_MODULES { } process ANNOTATE_TIER { - container 'aim-lite-r' + container 'zhandongliulab/aim-lite-r' tag "${scores.simpleName}" input: From 4fb7f8742148d7540a26b608172e4e8882b1af15 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 18 Dec 2024 10:43:52 -0600 Subject: [PATCH 16/80] Add --input_variant parameter for handling single variant --- bin/add_c_nc.py | 37 ++++---- bin/extraModel/generate_bivar_data.py | 5 +- bin/feature.py | 2 +- bin/generate_input_vcf.py | 36 ++++++++ docs/source/nf_usage.txt | 1 + main.nf | 124 ++++++++++++++++---------- nextflow.config | 5 +- test.txt | 0 8 files changed, 139 insertions(+), 71 deletions(-) create mode 100755 bin/generate_input_vcf.py delete mode 100644 test.txt diff --git a/bin/add_c_nc.py b/bin/add_c_nc.py index 8c4b74d..91fc24b 100755 --- a/bin/add_c_nc.py +++ b/bin/add_c_nc.py @@ -10,13 +10,13 @@ def add_c_nc(score, ref): if ref == "hg38": clin_c = pd.read_csv("merge_expand/hg38/clin_c.tsv.gz", sep="\t") clin_nc = pd.read_csv("merge_expand/hg38/clin_nc.tsv.gz", sep="\t") - hgmd_nc = pd.read_csv("merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") hgmd_c = pd.read_csv("merge_expand/hg38/hgmd_c.tsv.gz", sep="\t") + hgmd_nc = pd.read_csv("merge_expand/hg38/hgmd_nc.tsv.gz", sep="\t") else: clin_c = pd.read_csv("merge_expand/hg19/clin_c.tsv.gz", sep="\t") clin_nc = pd.read_csv("merge_expand/hg19/clin_nc.tsv.gz", sep="\t") - hgmd_nc = pd.read_csv("merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") hgmd_c = pd.read_csv("merge_expand/hg19/hgmd_c.tsv.gz", sep="\t") + hgmd_nc = pd.read_csv("merge_expand/hg19/hgmd_nc.tsv.gz", sep="\t") a = score.pos.values ac = score.chrom.values @@ -28,7 +28,7 @@ def add_c_nc(score, ref): i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh) & (ac[:, None] == bc)) - cln = pd.concat( + clin = pd.concat( [ temp.loc[i, :].reset_index(drop=True), clin_nc.loc[j, :].reset_index(drop=True), @@ -38,7 +38,7 @@ def add_c_nc(score, ref): # Take into account When HGMD data base is empty if hgmd_nc.shape[0] == 0: - pass + hgmd = hgmd_nc else: # merge by region @@ -58,12 +58,12 @@ def add_c_nc(score, ref): axis=1, ) - merged = score.merge(cln, how="left", on="varId") - if hgmd_nc.shape[0] == 0: - merged["nc_HGMD_Exp"] = np.NaN - merged["nc_RANKSCORE"] = np.NaN - else: - merged = merged.merge(hgmd, how="left", on="varId") + merged = score.merge( + clin_c.rename(columns={'new_chr': 'chrom', 'new_pos': 'pos'}), + how="left", + on=["chrom", "pos", "ref", "alt"], + ) + merged = merged.merge(clin, how="left", on="varId") if hgmd_c.shape[0] == 0: merged["c_HGMD_Exp"] = np.NaN @@ -71,16 +71,15 @@ def add_c_nc(score, ref): merged["CLASS"] = np.NaN else: merged = merged.merge( - hgmd_c, + hgmd_c.rename(columns={'new_chr': 'chrom', 'new_pos': 'pos'}), how="left", - left_on=["chrom", "pos", "ref", "alt"], - right_on=["new_chr", "new_pos", "ref", "alt"], + on=["chrom", "pos", "ref", "alt"], ) - merged = merged.merge( - clin_c, - how="left", - left_on=["chrom", "pos", "ref", "alt"], - right_on=["new_chr", "new_pos", "ref", "alt"], - ) + + if hgmd_nc.shape[0] == 0: + merged["nc_HGMD_Exp"] = np.NaN + merged["nc_RANKSCORE"] = np.NaN + else: + merged = merged.merge(hgmd, how="left", on="varId") return merged diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index 06a7c2c..31b6866 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -68,7 +68,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): ] # Remove all the duplicated variant pairs. - gene_var_pairs_df = pd.DataFrame(gene_var_pairs) + gene_var_pairs_df = pd.DataFrame(gene_var_pairs, columns=['geneEnsId', 'varId1', 'varId2']) # gene_var_pairs_df = gene_var_pairs_df.drop_duplicates(['varId1', 'varId2']) # Use only subset columns of features @@ -130,4 +130,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): # Sort before saving recessive_feature_df = recessive_feature_df.sort_index() + if recessive_feature_df.shape[0] == 0: + return + recessive_feature_df.to_csv(f"{recessive_folder}/{sample_id}.csv") diff --git a/bin/feature.py b/bin/feature.py index cd60f43..b1cd200 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -258,7 +258,7 @@ def main(): print("input annoatated varFile:", args.varFile) t1 = time.time() varDf = pd.read_csv( - args.varFile, sep="\t", skiprows=numHeaderSkip, error_bad_lines=False + args.varFile, sep="\t", skiprows=numHeaderSkip, ) # varDf=varDf[0:10] # #do this if need to have a small test diff --git a/bin/generate_input_vcf.py b/bin/generate_input_vcf.py new file mode 100755 index 0000000..5dbe48a --- /dev/null +++ b/bin/generate_input_vcf.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python3.8 + +import string +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("variant", type=str) + +args = parser.parse_args() + +vcf_template = string.Template(""" +##fileformat=VCFv4.2 +##FORMAT= +##FORMAT= +##FORMAT= +##FORMAT= +##FORMAT= +##FORMAT= +##FILTER= +##reference=file:///staging/human/reference/b37/b37.fa.default/reference.bin +#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE +$chrom $pos . $ref $alt . . . GT:AD:DP:GQ:PL 0/1:7,5:12:99:142,0,214 +""".strip()) + +def main(variant): + chrom, pos, ref, alt = variant.split("-") + with open("input.vcf", "w", encoding="ascii") as text_file: + text_file.write(vcf_template.substitute( + chrom=chrom, + pos=pos, + ref=ref, + alt=alt, + )) + +if __name__ == "__main__": + main(**vars(args)) diff --git a/docs/source/nf_usage.txt b/docs/source/nf_usage.txt index f5b0129..5f1f2e1 100644 --- a/docs/source/nf_usage.txt +++ b/docs/source/nf_usage.txt @@ -18,6 +18,7 @@ Args: --outdir Output directory. --run_id Unique identifier for this run. (default: 1) --ref_ver Reference genome version [hg38 or hg19] (default: hg19) + --input_variant Variant ID Formatted in 'chr-pos-alt-ref' (optional) --bed_filter Path to a BED file to perform an analysis only for regions of interest (optional) --exome_filter Enable exonic filter. Addition will filter out variants outside of exonic region (default: false) diff --git a/main.nf b/main.nf index e4c0c0b..e6bc09d 100644 --- a/main.nf +++ b/main.nf @@ -27,46 +27,48 @@ def showVersion() { def validateInputParams() { def checkPathParamMap = [ - "input_vcf": params.input_vcf, "input_hpo": params.input_hpo, "ref_dir" : params.ref_dir, "ref_ver" : params.ref_ver, ] checkPathParamMap.each { paramName, paramValue -> - if (paramValue) { - // Check if the file exists - if(!(paramName == "ref_ver")) { - def fileObj = file(paramValue, checkIfExists: true) - // println("Check file: '--${paramName}' value '${paramValue}' ") - - // Check the file type based on the parameter name - if (paramName == "input_vcf" && !(paramValue.endsWith(".vcf") || paramValue.endsWith(".vcf.gz"))) { - println("Error: '--${paramName}' value '${paramValue}' should be a VCF file (.vcf) or (.vcf.gz)") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } else if (paramName == "input_hpo" && !(paramValue.endsWith(".hpo") || paramValue.endsWith(".txt"))) { - println("Error: '--${paramName}' value '${paramValue}' should be an HPO file (.hpo) or (.txt)") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } else if (paramName == "ref_dir" && !fileObj.isDirectory()) { - println("Error: '--${paramName}' value '${paramValue}' should be an directory.") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - } - - if (paramName == "ref_ver" && !(paramValue.equals("hg19") || paramValue.equals("hg38")) ) { - println("Error: '--${paramName}' value ${paramValue} should be either set to 'hg19' or 'hg38'.") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } + if (paramValue) + return - } else { - println("Input parameter '${paramName}' not specified or is null!") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } + println("Input parameter '${paramName}' not specified or is null!") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (params.input_variant && params.input_vcf) { + println("Error: Cannot use '--input_variant' with --input_vcf'") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (params.input_vcf && !params.input_vcf.endsWith(".vcf") && !params.input_vcf.endsWith(".vcf.gz")) { + println("Error: '--input_vcf' value '${params.input_vcf}' should be a VCF file (.vcf) or (.vcf.gz)") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (params.input_variant && params.input_variant.count('-') != 3) { + println("Error: '--input_variant' should be formated like 'X-47038564-T-C'") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (!file(params.ref_dir).isDirectory()) { + println("Error: '--ref_dir' should be an directory.") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (!params.ref_ver.equals("hg19") && !params.ref_ver.equals("hg38") ) { + println("Error: '--ref_ver' value ${params.ref_ver} should be either set to 'hg19' or 'hg38'.") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 } } @@ -74,6 +76,18 @@ showUsage() showVersion() validateInputParams() +process GENERATE_INPUT_VCF { + input: + val id + + output: + path "input.vcf", emit: vcf + + """ + generate_input_vcf.py $id + """ +} + process VALIDATE_VCF { container 'quay.io/biocontainers/vcftools:0.1.16--pl5321hdcf5f25_11' @@ -401,7 +415,7 @@ process FILTER_PROBAND { path ref_gnomad_exome_idx output: - path "${params.run_id}.filt.rmBL.vcf", emit: vcf + path "${params.run_id}.filt.rmBL.vcf.gz", emit: vcf script: """ @@ -420,6 +434,7 @@ process FILTER_PROBAND { isec_tmp1/0000.vcf.gz isec_tmp2/0000.vcf.gz mv isec_tmp3/0002.vcf ${params.run_id}.filt.rmBL.vcf + bgzip ${params.run_id}.filt.rmBL.vcf """ } @@ -435,13 +450,12 @@ process SPLIT_VCF_BY_CHROMOSOME { """ # Get the list of chromosomes from the VCF file - bgzip ${vcf} - bcftools index ${vcf}.gz + bcftools index ${vcf} - bcftools query -f '%CHROM\n' ${vcf}.gz | sort | uniq > chrom_list.txt + bcftools query -f '%CHROM\n' ${vcf} | sort | uniq > chrom_list.txt # Split the VCF file by chromosome while read chrom; do - bcftools view -r \${chrom} ${vcf}.gz -Oz -o chr\${chrom}.vcf.gz + bcftools view -r \${chrom} ${vcf} -Oz -o chr\${chrom}.vcf.gz done < chrom_list.txt """ } @@ -673,7 +687,7 @@ workflow PHRANK_SCORING { VARIANTS_TO_ENSEMBL(VCF_TO_VARIANTS.out, params.ref_loc) ENSEMBL_TO_GENESYM(VARIANTS_TO_ENSEMBL.out, params.ref_to_sym, params.ref_sorted_sym) GENESYM_TO_PHRANK(ENSEMBL_TO_GENESYM.out, - params.input_hpo, + file(params.input_hpo), params.phrank_dagfile, params.phrank_disease_annotation, params.phrank_gene_annotation, @@ -684,19 +698,31 @@ workflow PHRANK_SCORING { } workflow { - VALIDATE_VCF(params.input_vcf) + if (params.input_vcf) { + input_vcf = file(params.input_vcf) + } else if (params.input_variant) { + GENERATE_INPUT_VCF(params.input_variant) + input_vcf = GENERATE_INPUT_VCF.out.vcf + } + + VALIDATE_VCF(input_vcf) NORMALIZE_VCF(VALIDATE_VCF.out.vcf) - BUILD_REFERENCE_INDEX() - VCF_PRE_PROCESS( - NORMALIZE_VCF.out.vcf, - NORMALIZE_VCF.out.tbi, - BUILD_REFERENCE_INDEX.out.fasta, - BUILD_REFERENCE_INDEX.out.fasta_index, - BUILD_REFERENCE_INDEX.out.fasta_dict, - ) + if (params.input_vcf) { + BUILD_REFERENCE_INDEX() + VCF_PRE_PROCESS( + NORMALIZE_VCF.out.vcf, + NORMALIZE_VCF.out.tbi, + BUILD_REFERENCE_INDEX.out.fasta, + BUILD_REFERENCE_INDEX.out.fasta_index, + BUILD_REFERENCE_INDEX.out.fasta_dict, + ) + vcf = VCF_PRE_PROCESS.out.vcf + } else { + vcf = NORMALIZE_VCF.out.vcf + } - SPLIT_VCF_BY_CHROMOSOME(VCF_PRE_PROCESS.out.vcf) + SPLIT_VCF_BY_CHROMOSOME(vcf) ANNOTATE_BY_VEP( SPLIT_VCF_BY_CHROMOSOME.out.chr_vcfs.flatten(), params.vep_dir_cache, diff --git a/nextflow.config b/nextflow.config index a70ecc0..6e31140 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,4 +1,7 @@ params { + input_vcf = null + input_variant = null + run_id = 1 ref_ver = "hg19" ref_dir = " " @@ -48,7 +51,7 @@ params { // VEP vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" - vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" + vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz" vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" vep_dir_cache = "${ref_dir}/vep/${ref_ver}/" diff --git a/test.txt b/test.txt deleted file mode 100644 index e69de29..0000000 From fe2ea6589a7dab7bfe16ce06f19a05550216039b Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 18 Dec 2024 13:31:21 -0600 Subject: [PATCH 17/80] Move some dependent params logics from config to main.nf --- bin/generate_input_vcf.py | 1 - main.nf | 73 +++++++++++++++++++++++++++++++++++++++ nextflow.config | 73 ++------------------------------------- 3 files changed, 75 insertions(+), 72 deletions(-) diff --git a/bin/generate_input_vcf.py b/bin/generate_input_vcf.py index 5dbe48a..93f9765 100755 --- a/bin/generate_input_vcf.py +++ b/bin/generate_input_vcf.py @@ -17,7 +17,6 @@ ##FORMAT= ##FORMAT= ##FILTER= -##reference=file:///staging/human/reference/b37/b37.fa.default/reference.bin #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE $chrom $pos . $ref $alt . . . GT:AD:DP:GQ:PL 0/1:7,5:12:99:142,0,214 """.strip()) diff --git a/main.nf b/main.nf index e6bc09d..0de7e18 100644 --- a/main.nf +++ b/main.nf @@ -72,9 +72,79 @@ def validateInputParams() { } } +def addDependentParams() { + params.ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" + + // for data dependency + params.chrmap = "${params.ref_dir}/bcf_annotate/chrmap.txt" + + params.ref_loc = "${params.ref_dir}/phrank/${params.ref_ver}/${params.ref_assembly}_symbol_to_location.txt" + params.ref_to_sym = "${params.ref_dir}/phrank/${params.ref_ver}/ensembl_to_symbol.txt" + params.ref_sorted_sym = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_symbol_sorted.txt" + + // FILTER BED + // EXONIC FILTER BED + params.ref_exonic_filter_bed = "${params.ref_dir}/filter_exonic/${params.ref_ver}.bed" + if (params.bed_filter) { + params.ref_filter_bed = params.bed_filter + } else if (params.exome_filter) { + params.ref_filter_bed = params.ref_exonic_filter_bed + } else { + params.ref_filter_bed = "/dev/null" + } + + // for phrank + params.phrank_dagfile = "${params.ref_dir}/phrank/${params.ref_ver}/child_to_parent.txt" + params.phrank_disease_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_pheno.txt" + params.phrank_gene_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_phenotype.txt" + params.phrank_disease_gene = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_gene.txt" + + // OMIM and HPO + params.omim_hgmd_phen = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HGMD_phen.tsv" + params.omim_obo = "${params.ref_dir}/omim_annotate/hp.obo" + params.omim_genemap2 = "${params.ref_dir}/omim_annotate/${params.ref_ver}/genemap2_v2022.rds" + params.omim_pheno = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HPO_OMIM.tsv" + + // GNOMAD VCF + params.ref_gnomad_genome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.genomes.vcf.gz" + params.ref_gnomad_genome_idx = "${params.ref_gnomad_genome}.tbi" + params.ref_gnomad_exome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.exomes.vcf.gz" + params.ref_gnomad_exome_idx = "${params.ref_gnomad_exome}.tbi" + + // VEP + params.vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" + params.vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz" + params.vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" + + params.vep_dir_cache = "${params.ref_dir}/vep/${params.ref_ver}/" + params.vep_dir_plugins = "${params.ref_dir}/vep/${params.ref_ver}/Plugins/" + params.vep_custom_gnomad = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_gnomad_name}" + params.vep_custom_clinvar = "${params.ref_dir}/vep/${params.ref_ver}/clinvar_20220730.vcf.gz" + params.vep_custom_hgmd = "${params.ref_dir}/vep/${params.ref_ver}/HGMD_Pro_2022.2_${params.ref_ver}.vcf.gz" + params.vep_plugin_revel = "${params.ref_dir}/vep/${params.ref_ver}/new_tabbed_revel_${params.ref_assembly}.tsv.gz" // changed for hg19 + params.vep_plugin_spliceai_snv = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.snv.${params.ref_ver}.vcf.gz" + params.vep_plugin_spliceai_indel = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.indel.${params.ref_ver}.vcf.gz" + params.vep_plugin_cadd = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_cadd_name}" // changed for hg19 + params.vep_plugin_dbnsfp = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_dbnsfp_name}" + params.vep_idx = "${params.ref_dir}/vep/${params.ref_ver}/*.tbi" + + params.ref_annot_dir = "${params.ref_dir}/annotate" + params.ref_var_tier_dir = "${params.ref_dir}/var_tier" + params.ref_merge_expand_dir = "${params.ref_dir}/merge_expand" + params.ref_mod5_diffusion_dir = "${params.ref_dir}/mod5_diffusion" + params.ref_predict_new_dir = "${params.ref_dir}/predict_new" + params.ref_model_inputs_dir = "${params.ref_dir}/model_inputs" + + // Documentation + params.usage_file = "${projectDir}/docs/source/nf_usage.txt" + params.script_chunking = "${projectDir}/scripts/split_chunks.py" + params.script_annot = "${projectDir}/scripts/annotation/*.py" +} + showUsage() showVersion() validateInputParams() +addDependentParams() process GENERATE_INPUT_VCF { input: @@ -174,6 +244,9 @@ process BUILD_REFERENCE_INDEX { script: """ + export http_proxy=http://your.proxy.server:port + export https_proxy=http://your.proxy.server:port + wget --quiet http://hgdownload.soe.ucsc.edu/goldenPath/${params.ref_ver}/bigZips/${params.ref_ver}.fa.gz gunzip ${params.ref_ver}.fa.gz sed 's/>chr/>/g' ${params.ref_ver}.fa > num_prefix_${params.ref_ver}.fa diff --git a/nextflow.config b/nextflow.config index 6e31140..474ea34 100644 --- a/nextflow.config +++ b/nextflow.config @@ -4,81 +4,12 @@ params { run_id = 1 ref_ver = "hg19" - ref_dir = " " - bed_filter = " " + ref_dir = null + bed_filter = null exome_filter = false help = false version = false - - ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" - - // for data dependency - chrmap = "${ref_dir}/bcf_annotate/chrmap.txt" - - ref_loc = "${ref_dir}/phrank/${ref_ver}/${ref_assembly}_symbol_to_location.txt" - ref_to_sym = "${ref_dir}/phrank/${ref_ver}/ensembl_to_symbol.txt" - ref_sorted_sym = "${ref_dir}/phrank/${ref_ver}/gene_to_symbol_sorted.txt" - - // FILTER BED - // EXONIC FILTER BED - ref_exonic_filter_bed = "${ref_dir}/filter_exonic/${ref_ver}.bed" - if (params.bed_filter != "") { - ref_filter_bed = bed_filter - } else if (params.exome_filter) { - ref_filter_bed = ref_exonic_filter_bed - } else { - ref_filter_bed = "/dev/null" - } - - // for phrank - phrank_dagfile = "${ref_dir}/phrank/${ref_ver}/child_to_parent.txt" - phrank_disease_annotation = "${ref_dir}/phrank/${ref_ver}/disease_to_pheno.txt" - phrank_gene_annotation = "${ref_dir}/phrank/${ref_ver}/gene_to_phenotype.txt" - phrank_disease_gene = "${ref_dir}/phrank/${ref_ver}/disease_to_gene.txt" - - // OMIM and HPO - omim_hgmd_phen = "${ref_dir}/omim_annotate/${ref_ver}/HGMD_phen.tsv" - omim_obo = "${ref_dir}/omim_annotate/hp.obo" - omim_genemap2 = "${ref_dir}/omim_annotate/${ref_ver}/genemap2_v2022.rds" - omim_pheno = "${ref_dir}/omim_annotate/${ref_ver}/HPO_OMIM.tsv" - - // GNOMAD VCF - ref_gnomad_genome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.genomes.vcf.gz" - ref_gnomad_genome_idx = "${ref_gnomad_genome}.tbi" - ref_gnomad_exome = "${ref_dir}/filter_vep/${ref_ver}/gnomad.${ref_ver}.blacklist.exomes.vcf.gz" - ref_gnomad_exome_idx = "${ref_gnomad_exome}.tbi" - - // VEP - vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" - vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz" - vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" - - vep_dir_cache = "${ref_dir}/vep/${ref_ver}/" - vep_dir_plugins = "${ref_dir}/vep/${ref_ver}/Plugins/" - vep_custom_gnomad = "${ref_dir}/vep/${ref_ver}/${vep_gnomad_name}" - vep_custom_clinvar = "${ref_dir}/vep/${ref_ver}/clinvar_20220730.vcf.gz" - vep_custom_hgmd = "${ref_dir}/vep/${ref_ver}/HGMD_Pro_2022.2_${ref_ver}.vcf.gz" - vep_plugin_revel = "${ref_dir}/vep/${ref_ver}/new_tabbed_revel_${ref_assembly}.tsv.gz" // changed for hg19 - vep_plugin_spliceai_snv = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.snv.${ref_ver}.vcf.gz" - vep_plugin_spliceai_indel = "${ref_dir}/vep/${ref_ver}/spliceai_scores.masked.indel.${ref_ver}.vcf.gz" - vep_plugin_cadd = "${ref_dir}/vep/${ref_ver}/${vep_cadd_name}" // changed for hg19 - vep_plugin_dbnsfp = "${ref_dir}/vep/${ref_ver}/${vep_dbnsfp_name}" - vep_idx = "${ref_dir}/vep/${ref_ver}/*.tbi" - - // Documentation - usage_file = "${projectDir}/docs/source/nf_usage.txt" - - script_chunking = "${projectDir}/scripts/split_chunks.py" - script_annot = "${projectDir}/scripts/annotation/*.py" - - ref_annot_dir = "${ref_dir}/annotate" - ref_var_tier_dir = "${ref_dir}/var_tier" - ref_merge_expand_dir = "${ref_dir}/merge_expand" - ref_mod5_diffusion_dir = "${ref_dir}/mod5_diffusion" - ref_predict_new_dir = "${ref_dir}/predict_new" - ref_model_inputs_dir = "${ref_dir}/model_inputs" - } docker.enabled = true From ce6b975004b2acccaa0b8e9c0d8a42dcc3efe5e1 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 18 Dec 2024 13:33:50 -0600 Subject: [PATCH 18/80] Set version to 1.1 --- main.nf | 2 +- nextflow.config | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/main.nf b/main.nf index 0de7e18..bb875c8 100644 --- a/main.nf +++ b/main.nf @@ -21,7 +21,7 @@ def showVersion() { return } - println "1.0.0" + println "1.1.0" exit 0 } diff --git a/nextflow.config b/nextflow.config index 474ea34..c63c4a7 100644 --- a/nextflow.config +++ b/nextflow.config @@ -20,7 +20,7 @@ process { errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } maxRetries = 5 - container = "chaozhongliu/aim-lite:latest" + container = "zhandongliulab/aim-lite:1.1" cache = "lenient" } From 91d48796078a74514db1594286393d3363323668 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 18 Dec 2024 13:44:44 -0600 Subject: [PATCH 19/80] fix dockerfile slightly --- Dockerfile | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Dockerfile b/Dockerfile index b0bd3f8..a6f4524 100644 --- a/Dockerfile +++ b/Dockerfile @@ -9,7 +9,7 @@ #FROM ubuntu:18.04 FROM ensemblorg/ensembl-vep:release_104.3 USER root -ENV DEBIAN_FRONTEND noninteractive +ENV DEBIAN_FRONTEND=noninteractive # Install dependencies RUN apt-get update && apt-get install -y \ @@ -38,7 +38,7 @@ RUN pip3 install -r /opt/requirements.txt RUN pip3 install bgzip # Install bcftools -RUN wget https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2 +RUN curl -OL https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2 RUN mv bcftools-1.20.tar.bz2 /opt/bcftools-1.20.tar.bz2 RUN tar -xf /opt/bcftools-1.20.tar.bz2 -C /opt/ && \ rm /opt/bcftools-1.20.tar.bz2 && \ @@ -49,7 +49,7 @@ RUN tar -xf /opt/bcftools-1.20.tar.bz2 -C /opt/ && \ rm -rf /opt/bcftools-1.20 # Install bedtools -RUN wget https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary +RUN curl -OL https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary RUN mv bedtools.static.binary /run/bedtools RUN chmod a+x /run/bedtools From 0f4a4f2a0c9196d655b9fc5cd97b7d0c08e77b09 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 18 Dec 2024 13:48:53 -0600 Subject: [PATCH 20/80] Add outdir as necessary input --- main.nf | 1 + 1 file changed, 1 insertion(+) diff --git a/main.nf b/main.nf index bb875c8..25ace82 100644 --- a/main.nf +++ b/main.nf @@ -30,6 +30,7 @@ def validateInputParams() { "input_hpo": params.input_hpo, "ref_dir" : params.ref_dir, "ref_ver" : params.ref_ver, + "outdir" : params.outdir, ] checkPathParamMap.each { paramName, paramValue -> From 7f054157f20750a27d1f55087342fec1ace483ed Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 18 Dec 2024 14:28:48 -0600 Subject: [PATCH 21/80] remove proxy setting --- main.nf | 3 --- 1 file changed, 3 deletions(-) diff --git a/main.nf b/main.nf index 25ace82..a90a6f5 100644 --- a/main.nf +++ b/main.nf @@ -245,9 +245,6 @@ process BUILD_REFERENCE_INDEX { script: """ - export http_proxy=http://your.proxy.server:port - export https_proxy=http://your.proxy.server:port - wget --quiet http://hgdownload.soe.ucsc.edu/goldenPath/${params.ref_ver}/bigZips/${params.ref_ver}.fa.gz gunzip ${params.ref_ver}.fa.gz sed 's/>chr/>/g' ${params.ref_ver}.fa > num_prefix_${params.ref_ver}.fa From b1417d332011d0b7d9499f62645f7d097a73c492 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Mon, 27 Jan 2025 13:31:01 -0600 Subject: [PATCH 22/80] Use vcf.gz for gnomad grch38 3.1.2 --- main.nf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.nf b/main.nf index a90a6f5..66e726e 100644 --- a/main.nf +++ b/main.nf @@ -114,7 +114,7 @@ def addDependentParams() { // VEP params.vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" - params.vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.bgz" + params.vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" params.vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" params.vep_dir_cache = "${params.ref_dir}/vep/${params.ref_ver}/" From bbbf71689a36ea6d1093237f0732c2d773ab87da Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Thu, 3 Apr 2025 11:36:13 -0500 Subject: [PATCH 23/80] Rebranch the trio branch from develop branch --- docs/source/nf_usage.txt | 2 ++ main.nf | 35 +++++++++++++++++++++++++---------- nextflow.config | 1 + 3 files changed, 28 insertions(+), 10 deletions(-) diff --git a/docs/source/nf_usage.txt b/docs/source/nf_usage.txt index 5f1f2e1..5d42773 100644 --- a/docs/source/nf_usage.txt +++ b/docs/source/nf_usage.txt @@ -21,6 +21,8 @@ Args: --input_variant Variant ID Formatted in 'chr-pos-alt-ref' (optional) --bed_filter Path to a BED file to perform an analysis only for regions of interest (optional) --exome_filter Enable exonic filter. Addition will filter out variants outside of exonic region (default: false) + --mode Specify the analysis mode [singleton or trio] (default: singleton) + --input_ped Path to the pedgree file for trio mode Metadata Flags: --help Display the usage information. diff --git a/main.nf b/main.nf index 66e726e..b052d79 100644 --- a/main.nf +++ b/main.nf @@ -192,7 +192,6 @@ process NORMALIZE_VCF { INPUT_VCF_TYPE="\$(file -b \${SYM_LINK})" fi - if echo "\${INPUT_VCF_TYPE}" | grep -q 'BGZF'; then echo "The file is in BGZF format, ready for tabix." cp $vcf input.vcf.gz @@ -207,6 +206,23 @@ process NORMALIZE_VCF { exit 1 fi + # Step 2: if mode is 'trio' + if [ "${params.mode}" == "trio" ]; then + echo "Mode is 'trio': Normalizing joint VCF file." + # Normalize to handle multiallelic rows + bcftools norm --multiallelics -both \ + -Oz -o normalized.tmp.vcf.gz \ + input.vcf.gz + tabix normalized.tmp.vcf.gz + # Remove duplicates + bcftools norm --rm-dup none \ + -Oz -o input.vcf.gz \ + normalized.tmp.vcf.gz + else + echo "Mode is 'singleton': Skipping normalization." + fi + + tabix -p vcf input.vcf.gz """ } @@ -771,15 +787,9 @@ workflow PHRANK_SCORING { workflow { if (params.input_vcf) { input_vcf = file(params.input_vcf) - } else if (params.input_variant) { - GENERATE_INPUT_VCF(params.input_variant) - input_vcf = GENERATE_INPUT_VCF.out.vcf - } - VALIDATE_VCF(input_vcf) - NORMALIZE_VCF(VALIDATE_VCF.out.vcf) - - if (params.input_vcf) { + VALIDATE_VCF(input_vcf) + NORMALIZE_VCF(VALIDATE_VCF.out.vcf) BUILD_REFERENCE_INDEX() VCF_PRE_PROCESS( NORMALIZE_VCF.out.vcf, @@ -789,7 +799,12 @@ workflow { BUILD_REFERENCE_INDEX.out.fasta_dict, ) vcf = VCF_PRE_PROCESS.out.vcf - } else { + } else if (params.input_variant) { + GENERATE_INPUT_VCF(params.input_variant) + input_vcf = GENERATE_INPUT_VCF.out.vcf + + VALIDATE_VCF(input_vcf) + NORMALIZE_VCF(VALIDATE_VCF.out.vcf) vcf = NORMALIZE_VCF.out.vcf } diff --git a/nextflow.config b/nextflow.config index c63c4a7..b105943 100644 --- a/nextflow.config +++ b/nextflow.config @@ -7,6 +7,7 @@ params { ref_dir = null bed_filter = null exome_filter = false + mode = "singleton" help = false version = false From 35ba05025d0d2abbb49dbbd9bf9e2ec2f0709887 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 11:45:01 -0500 Subject: [PATCH 24/80] Enable Trio pipeline with --input_ped argument --- bin/extraModel/generate_bivar_data.py | 4 +- bin/extraModel_main.py | 2 +- bin/merge_rm.py | 14 +- bin/run_final.py | 13 +- bin/trio/inh_utils.py | 184 ++++ bin/trio_check_inheritance.GATK.py | 174 ++++ bin/trio_merge_inheritance.py | 17 + bin/trio_merge_rm.py | 15 + bin/trio_rename_VCFheader.py | 23 + docs/source/nf_usage.txt | 1 - main.nf | 886 +----------------- modules/local/singleton/main.nf | 571 +++++++++++ modules/local/trio/main.nf | 165 ++++ modules/local/utils/main.nf | 141 +++ nextflow.config | 4 +- resources/trio_pipeline/acceptable_header.txt | 198 ++++ resources/trio_pipeline/features.csv | 1 + resources/trio_pipeline/features_NDG.csv | 1 + resources/trio_pipeline/rf_trio_NDG.job | Bin 0 -> 6685865 bytes resources/trio_pipeline/rf_trio_base.job | Bin 0 -> 3129593 bytes subworkflows/local/singleton/main.nf | 138 +++ 21 files changed, 1688 insertions(+), 864 deletions(-) create mode 100644 bin/trio/inh_utils.py create mode 100755 bin/trio_check_inheritance.GATK.py create mode 100755 bin/trio_merge_inheritance.py create mode 100755 bin/trio_merge_rm.py create mode 100755 bin/trio_rename_VCFheader.py create mode 100644 modules/local/singleton/main.nf create mode 100644 modules/local/trio/main.nf create mode 100644 modules/local/utils/main.nf create mode 100644 resources/trio_pipeline/acceptable_header.txt create mode 100755 resources/trio_pipeline/features.csv create mode 100644 resources/trio_pipeline/features_NDG.csv create mode 100644 resources/trio_pipeline/rf_trio_NDG.job create mode 100644 resources/trio_pipeline/rf_trio_base.job create mode 100644 subworkflows/local/singleton/main.nf diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index 31b6866..fa2b33c 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -10,7 +10,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): if labeling: - raise 'The below code was not tested with real data with labeling=True' + raise NotImplementedError('The below code was not tested with real data with labeling=True') recessive_folder = f"{data_folder}/recessive_matrix/" if not os.path.exists(recessive_folder): @@ -21,7 +21,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): os.mkdir(tmp_folder) # read feature matrix for single var - feature_fn = f"{sample_id}.csv" + feature_fn = f"{sample_id}.feature.csv" # feature_df = [] # for feature_fn in feature_fns: if "csv" in feature_fn: diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index 050ddb6..5e2c7a0 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -83,7 +83,7 @@ def assign_ranking(df): def AIM(data_folder, sample_id): - feature_fn = f"{sample_id}.csv" + feature_fn = f"{sample_id}.feature.csv" if not os.path.exists(feature_fn): print(f"{feature_fn} does not exist.") diff --git a/bin/merge_rm.py b/bin/merge_rm.py index ad3089d..44417ee 100755 --- a/bin/merge_rm.py +++ b/bin/merge_rm.py @@ -2,11 +2,13 @@ import pandas as pd import sys -###merge score files with final rank matrix +feature_path = sys.argv[1] +annot_path = sys.argv[2] +output_path = sys.argv[3] -# read rank matrix and score file -rm = pd.read_csv(sys.argv[1] + ".csv") -annot = pd.read_csv("scores.txt.gz", sep="\t", compression="gzip") +# read feature matrix and score file +df = pd.read_csv(feature_path) +annot = pd.read_csv(annot_path, sep="\t", compression="gzip") # get columns from score file @@ -37,10 +39,10 @@ ) # merge -test = rm.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") +test = df.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") test.to_csv( - "final_matrix_expanded/" + sys.argv[1] + ".expanded.csv.gz", + output_path, index=False, compression="gzip", ) diff --git a/bin/run_final.py b/bin/run_final.py index 20c406d..dba3dd2 100755 --- a/bin/run_final.py +++ b/bin/run_final.py @@ -9,14 +9,19 @@ from predict_new.confidence import * import sys +jobfile_path = sys.argv[1] +featurenames_path = sys.argv[2] +matrix_path = sys.argv[3] +output_path = sys.argv[4] + # read in precalulated model -model = joblib.load("model_inputs/default/final_model.job") +model = joblib.load(jobfile_path) # read in features to use -features = list(pd.read_csv("model_inputs/default/features.csv")) +features = list(pd.read_csv(featurenames_path)) # predict with Linhua's code -out = rank_patient(model, sys.argv[1] + ".matrix.txt", features, train_causals=None) +out = rank_patient(model, matrix_path, features, train_causals=None) # write out -out.to_csv(sys.argv[1] + ".csv") +out.to_csv(output_path) diff --git a/bin/trio/inh_utils.py b/bin/trio/inh_utils.py new file mode 100644 index 0000000..d4d9dc3 --- /dev/null +++ b/bin/trio/inh_utils.py @@ -0,0 +1,184 @@ +import pandas as pd +import numpy as np +from glob import glob +import sys + + +def matchID_in_inheritance(score_file, singleton_rank, inheritance_file, mode='test'): + + score_df = pd.read_csv(score_file, + sep='\t', compression='gzip') + score_df = score_df.loc[~(score_df['chrom'].isin([25,26]))] + rank_df = pd.read_csv(singleton_rank, index_col=0) + inh_df = pd.read_csv(inheritance_file) + print('score_df:', score_df.shape) + print('rank_df:', rank_df.shape) + print('inh_df:', inh_df.shape) + + inh_df = inh_df.loc[~(inh_df['varid'].str.startswith('GL'))].copy() + inh_df['varid'] = inh_df['varid'].str.replace('\.0','', regex=True) + inh_df['varid'] = inh_df['varid'].str.replace('X','23') + inh_df['varid'] = inh_df['varid'].str.replace('Y','24') + + ### To accommodate the situation where hg38 files have "chr" in the chromosome naming: Remove 'chr' prefix from varId in inheritance file ### + inh_df['varid'] = inh_df['varid'].str.replace('^chr', '', regex=True) + + + score_df_2 = score_df.loc[:,['varId','varId_dash','geneEnsId']].copy() + score_df_2['varId'] = score_df_2['varId'].str.split('_').str[0] + '_'+\ + score_df_2['varId'].str.split('_').str[1] + '_'+\ + score_df_2['varId'].str.split('_').str[2] + '_'+\ + score_df_2['varId'].str.split('_').str[3] + score_df_2 = score_df_2.loc[~score_df_2.duplicated()] + score_df_2 = score_df_2.groupby(by='varId', as_index=False).agg({'geneEnsId' : ','.join, 'varId_dash' : 'first'}) + score_df_2['geneEnsId'] = score_df_2['geneEnsId'].str.replace('-','').str.rstrip(',') + n_rows = score_df_2.shape[0] + n_varid = len(np.unique(score_df_2['varId'])) + + # check if varId is unique + assert n_rows == n_varid + score_df_2.index = score_df_2['varId'] + + inh_df = inh_df.loc[inh_df['varid'].isin(score_df_2['varId'])].copy() + inh_df['varId_dash'] = score_df_2.loc[inh_df['varid'], 'varId_dash'].tolist() + inh_df['geneEnsId'] = score_df_2.loc[inh_df['varid'], 'geneEnsId'].tolist() + + assert np.all(inh_df['varId_dash'].isin(rank_df.index)) + + + if mode == 'test': + return inh_df + + else: + if np.all(rank_df.loc[rank_df['is_causal']==1].index.isin(inh_df['varId_dash'])): + return inh_df + else: + sys.exit('%s: not all causal variants in inheritance file!'%(ID)) + + +def matrix_inh(inh_df, rank_file): + + # Load in rank file + rank_df = pd.read_csv(rank_file, index_col=0) + + if 'confidence' in rank_df.columns.tolist(): + rank_df = rank_df.drop(columns=['confidence','confidence level']) + features = rank_df.columns[:-4].tolist() + ['ranking'] + features_name = features[:-2] + ['rf_score','rf_rank'] + rank_df = rank_df.loc[:,features].copy() + rank_df.columns = features_name + + # Load in inheritance file + inh_df.index = inh_df['varId_dash'] + # feature engineering + inh_df.loc[inh_df['GATK']=='Inherited','GATK'] = 0 + inh_df.loc[inh_df['GATK']=='loConfDeNovo','GATK'] = 1 + inh_df.loc[inh_df['GATK']=='hiConfDeNovo','GATK'] = 2 + inh_df['GATK'] = inh_df['GATK'].astype(int) + inh_df['DeNovo'] = (inh_df['GATK']==2).astype(int) + inh_df['Father'] = ((inh_df['Pattern'].isin(['F','MF','NF','MU','UF','UU'])) | (inh_df['Pattern'].isin(['MN','NN']) & (inh_df['GATK']!=2))).astype(int) + inh_df['Mother'] = ((inh_df['Pattern'].isin(['M','MF','MN','MU','UF','UU'])) | (inh_df['Pattern'].isin(['NF','NN']) & (inh_df['GATK']!=2))).astype(int) + inh_df['Unknown'] = ((inh_df['DeNovo']==0) & (inh_df['Father']==0) & (inh_df['Mother']==0)).astype(int) + + # Merge the two + rank_df = rank_df.loc[rank_df.index.isin(inh_df.index)].copy() + added_features = ['DeNovo','Father','Mother','Unknown','geneEnsId'] + rank_df[added_features] = inh_df[added_features] + rank_df.loc[rank_df['geneEnsId'].isna(), 'geneEnsId'] = [str(i) for i in range(np.sum(rank_df['geneEnsId'].isna()))] + + # Gene level variant summary + genes = list(set((','.join(rank_df['geneEnsId'].tolist())).split(','))) ### Changed from set to list ### + gene_vars_df = pd.DataFrame({#'Gene':genes, + 'Gene_vars_Father':0, + 'Gene_vars_Mother':0, + 'Gene_vars_DeNovo':0, + 'Gene_vars_Unknown':0}, index=genes) + + genes_HMImpact = list(set((','.join(rank_df.loc[rank_df['IMPACT.from.Tier']>=3,'geneEnsId'].tolist())).split(','))) + + for g in genes_HMImpact: + gene_df = rank_df.loc[rank_df['geneEnsId'].str.contains(g)].copy() + gene_df = gene_df.loc[gene_df['IMPACT.from.Tier']>=3].copy() + gene_vars_df.loc[g, 'Gene_vars_Father'] = gene_df['Father'].sum() + gene_vars_df.loc[g, 'Gene_vars_Mother'] = gene_df['Mother'].sum() + gene_vars_df.loc[g, 'Gene_vars_DeNovo'] = gene_df['DeNovo'].sum() + gene_vars_df.loc[g, 'Gene_vars_Unknown'] = gene_df['Unknown'].sum() + + rank_df['varId'] = rank_df.index + rank_df_new = pd.DataFrame(np.ones((0,2)), columns=['varId','geneEnsId']) + + for i in range(rank_df.shape[0]): + n_lines = len(rank_df.loc[rank_df.index[i],'geneEnsId'].split(',')) + rank_df_new_tmp = rank_df.loc[np.repeat(rank_df.index[i],n_lines),['varId','geneEnsId']] + rank_df_new_tmp['geneEnsId'] = rank_df.loc[rank_df.index[i],'geneEnsId'].split(',') + + rank_df_new = pd.concat([rank_df_new, rank_df_new_tmp], axis=0) + rank_df_new.index = np.arange(rank_df_new.shape[0]) + + rank_df_new['Gene_vars_Father'] = 0 + rank_df_new['Gene_vars_Mother'] = 0 + rank_df_new['Gene_vars_DeNovo'] = 0 + rank_df_new['Gene_vars_Unknown'] = 0 + rank_df_new.loc[:,['Gene_vars_Father','Gene_vars_Mother','Gene_vars_DeNovo','Gene_vars_Unknown']] = gene_vars_df.loc[rank_df_new['geneEnsId'].tolist(), + gene_vars_df.columns.tolist()].to_numpy() + rank_df_new = rank_df_new.groupby(by='varId')[['Gene_vars_Father','Gene_vars_Mother','Gene_vars_DeNovo','Gene_vars_Unknown']].max() + rank_df_new = rank_df_new.loc[rank_df.index] + + assert np.all(rank_df.index == rank_df_new.index) + + rank_df['Gene_vars_Father'] = rank_df_new['Gene_vars_Father'] + rank_df['Gene_vars_Mother'] = rank_df_new['Gene_vars_Mother'] + rank_df['Gene_vars_DeNovo'] = rank_df_new['Gene_vars_DeNovo'] + rank_df['Gene_vars_Unknown'] = rank_df_new['Gene_vars_Unknown'] + + rank_df['No.Risk.Var'] = rank_df['No.Var.H'] + rank_df['No.Var.M'] + rank_df['chrom'] = rank_df.index.str.split('-').str[0] + rank_df['chrom'] = rank_df['chrom'].astype(int) + + rank_df['Homozygous_Recessive'] = ((rank_df['zyg']==2) & (rank_df['chrom']!=23)).astype(int) + rank_df['De_Novo'] = ((rank_df['DeNovo']==1) & (rank_df['zyg']==1)).astype(int) + rank_df['X_Linked'] = (rank_df['chrom']==23).astype(int) + + rank_df['Compound_heterozygous'] = ( + (rank_df['zyg']==1) &\ + (rank_df['No.Risk.Var']>0) &\ + (((rank_df['Father']==1) & ((rank_df['Gene_vars_Mother']+rank_df['Gene_vars_DeNovo'])>0)) |\ + ((rank_df['Mother']==1) & ((rank_df['Gene_vars_Father']+rank_df['Gene_vars_DeNovo'])>0))) + ).astype(int) + + + rank_df['Inherited_dominant'] = ( + (rank_df['zyg']==1) &\ + (rank_df['DeNovo']==0) &\ + (rank_df['X_Linked']==0) &\ + (rank_df['Compound_heterozygous']==0) + ).astype(int) + + + rank_df['Homozygous_Recessive_Risk'] = rank_df['Homozygous_Recessive'].copy() + rank_df.loc[rank_df['IMPACT.from.Tier']<3,'Homozygous_Recessive_Risk'] = 0 + + rank_df['De_Novo_Risk'] = rank_df['De_Novo'].copy() + rank_df.loc[rank_df['IMPACT.from.Tier']<3,'De_Novo_Risk'] = 0 + + rank_df['X_Linked_Risk'] = rank_df['X_Linked'].copy() + rank_df.loc[rank_df['IMPACT.from.Tier']<3,'X_Linked_Risk'] = 0 + + rank_df['Compound_heterozygous_Risk'] = rank_df['Compound_heterozygous'].copy() + rank_df.loc[rank_df['IMPACT.from.Tier']<3,'Compound_heterozygous_Risk'] = 0 + + rank_df['Inherited_dominant_Risk'] = rank_df['Inherited_dominant'].copy() + rank_df.loc[rank_df['IMPACT.from.Tier']<3,'Inherited_dominant_Risk'] = 0 + + return rank_df #.to_csv('matrix_inh/%s.txt'%ID) + + + + + + + + + + + diff --git a/bin/trio_check_inheritance.GATK.py b/bin/trio_check_inheritance.GATK.py new file mode 100755 index 0000000..9832edf --- /dev/null +++ b/bin/trio_check_inheritance.GATK.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python + +import pandas as pd +import numpy as np +import sys + + +sampleID = sys.argv[1] +vcf_file = sys.argv[2] +ref_genome = sys.argv[3] +proband_id = sys.argv[4] +paternal_id = sys.argv[5] +maternal_id = sys.argv[6] + + +Y_autosomal = {'hg19':{'cut1':10001, + 'cut2':2649520, + 'cut3':59034050, + 'cut4':59363566}, + 'hg38':{'cut1':10001, + 'cut2':2781479, + 'cut3':56887903, + 'cut4':57217415}} + + +n = 0 +with open(vcf_file,'r') as F: + for line in F: + if line.startswith('##'): + n += 1 + else: + break + +vcf_df = pd.read_csv(vcf_file, sep='\t', skiprows=n) +##I removed this flag because the vcfs we're dealing with don't have the PASS flag... +#vcf_df = vcf_df.loc[vcf_df['FILTER']=='PASS',:] + +probandID = [coln for coln in vcf_df.columns if proband_id in coln][0] +paternalID = [coln for coln in vcf_df.columns if paternal_id in coln][0] +maternalID = [coln for coln in vcf_df.columns if maternal_id in coln][0] + + +# Preprocessing +vcf_df['varid'] = vcf_df['#CHROM'].astype(str) + '_' + vcf_df['POS'].astype(str) + '_' + vcf_df['REF'] + '_' + vcf_df['ALT'] +vcf_df['Patient'] = vcf_df.loc[:,probandID].str.split(':').str[0].str.replace('|','/') +vcf_df['Mother'] = vcf_df.loc[:,maternalID].str.split(':').str[0].str.replace('|','/') +vcf_df['Father'] = vcf_df.loc[:,paternalID].str.split(':').str[0].str.replace('|','/') +vcf_df = vcf_df.loc[~vcf_df['Patient'].isin(['./.','0/0']),:] +vcf_df['Pattern'] = 'Left' +#print(vcf_df) +#print(vcf_df['Patient']) +#print(vcf_df['Patient'].str.split('/',expand=True)) +#print(vcf_df[['Patient_0','Patient_1']]) +#quit() +vcf_df[['Patient_0','Patient_1']] = vcf_df['Patient'].str.split('/',expand=True) +vcf_df[['Mother_0','Mother_1']] = vcf_df['Mother'].str.split('/',expand=True) +vcf_df[['Father_0','Father_1']] = vcf_df['Father'].str.split('/',expand=True) + + +# make GT like 1/2, 0/4 to several lines with only 0 or 1 +''' +multivar_df = vcf_df.loc[vcf_df['ALT'].str.contains(','),:] +vcf_df = vcf_df.loc[~vcf_df['ALT'].str.contains(','),vcf_df.columns[0:16]] +multivar_df.loc[:,'ALT_list'] = multivar_df['ALT'].str.split(',') +multivar_df.loc[:,'ALT_int'] = multivar_df['ALT_list'].apply(lambda x: list(range(1,len(x)+1))) + +multivar_df.loc[:,'PGT_0_list'] = multivar_df.apply(lambda x: np.isin(np.array(x.ALT_int).astype(str),x.Patient_0).astype(int), axis=1) +multivar_df.loc[:,'PGT_1_list'] = multivar_df.apply(lambda x: np.isin(np.array(x.ALT_int).astype(str),x.Patient_1).astype(int), axis=1) + +multivar_df.loc[:,'FGT_0_list'] = multivar_df.apply(lambda x: np.isin(np.array(x.ALT_int).astype(str),x.Father_0).astype(int), axis=1) +multivar_df.loc[:,'FGT_1_list'] = multivar_df.apply(lambda x: np.isin(np.array(x.ALT_int).astype(str),x.Father_1).astype(int), axis=1) + +multivar_df.loc[:,'MGT_0_list'] = multivar_df.apply(lambda x: np.isin(np.array(x.ALT_int).astype(str),x.Mother_0).astype(int), axis=1) +multivar_df.loc[:,'MGT_1_list'] = multivar_df.apply(lambda x: np.isin(np.array(x.ALT_int).astype(str),x.Mother_1).astype(int), axis=1) + +multivar_df.loc[:,'PGT_list'] = multivar_df.apply(lambda x: ['%s/%s'%(x.PGT_0_list[i],x.PGT_1_list[i]) for i in range(len(x.ALT_int))], axis=1) +multivar_df.loc[:,'FGT_list'] = multivar_df.apply(lambda x: ['%s/%s'%(x.FGT_0_list[i],x.FGT_1_list[i]) for i in range(len(x.ALT_int))], axis=1) +multivar_df.loc[:,'MGT_list'] = multivar_df.apply(lambda x: ['%s/%s'%(x.MGT_0_list[i],x.MGT_1_list[i]) for i in range(len(x.ALT_int))], axis=1) + +multivar_new = pd.DataFrame(np.ones((0,16)), columns=multivar_df.columns[0:16]) + +for i in range(multivar_df.shape[0]): + n_lines = len(multivar_df.loc[multivar_df.index[i],'ALT_list']) + multivar_new_tmp = multivar_df.iloc[np.repeat(i,n_lines),0:16] + multivar_new_tmp['ALT'] = multivar_df.loc[multivar_df.index[i],'ALT_list'] + multivar_new_tmp['Patient'] = multivar_df.loc[multivar_df.index[i],'PGT_list'] + multivar_new_tmp['Mother'] = multivar_df.loc[multivar_df.index[i],'MGT_list'] + multivar_new_tmp['Father'] = multivar_df.loc[multivar_df.index[i],'FGT_list'] + multivar_new = pd.concat([multivar_new, multivar_new_tmp], axis=0) + multivar_new.index = np.arange(multivar_new.shape[0]) + +multivar_new['varid'] = multivar_new['#CHROM'].astype(str) + '_' + multivar_new['POS'].astype(str) + '_' + multivar_new['REF'] + '_' + multivar_new['ALT'] + +vcf_df = pd.concat([vcf_df, multivar_new], axis=0) +''' + +vcf_df.index = np.arange(vcf_df.shape[0]) +vcf_df = vcf_df.loc[~vcf_df['Patient'].isin(['./.','0/0']),:] + + +print('Total number of variants:', vcf_df.shape[0]) + + +# het condition +bool_M = vcf_df['Patient'].isin(['0/1','1/0']) & (vcf_df['Mother'].str.find('1') != -1) & (vcf_df['Father'].str.find('1') == -1) +bool_F = vcf_df['Patient'].isin(['0/1','1/0']) & (vcf_df['Father'].str.find('1') != -1) & (vcf_df['Mother'].str.find('1') == -1) + +# more specific if one parent is 1/1 +bool_UI = vcf_df['Patient'].isin(['0/1','1/0']) & (vcf_df['Father'].str.find('1') != -1) & (vcf_df['Mother'].str.find('1') != -1) +bool_M = bool_M | (bool_UI & (vcf_df['Father']!='1/1') & (vcf_df['Mother']=='1/1')) +bool_F = bool_F | (bool_UI & (vcf_df['Father']=='1/1') & (vcf_df['Mother']!='1/1')) + +bool_N = vcf_df['Patient'].isin(['0/1','1/0']) & (vcf_df['Father'].str.find('1') == -1) & (vcf_df['Mother'].str.find('1') == -1) + +vcf_df.loc[bool_UI,'Pattern'] = 'UI' +vcf_df.loc[bool_M,'Pattern'] = 'M' +vcf_df.loc[bool_F,'Pattern'] = 'F' +vcf_df.loc[bool_N,'Pattern'] = 'N' + +# unkown cases +vcf_df.loc[(vcf_df['Father']=='./.') & (vcf_df['Mother']=='./.') & bool_N, 'Pattern'] = 'U' + + + +# homo condition +bool_NM = vcf_df['Patient'].isin(['1/1']) & (vcf_df['Mother'].str.find('1') != -1) & (vcf_df['Father'].str.find('1') == -1) +bool_NF = vcf_df['Patient'].isin(['1/1']) & (vcf_df['Mother'].str.find('1') == -1) & (vcf_df['Father'].str.find('1') != -1) +bool_B = vcf_df['Patient'].isin(['1/1']) & (vcf_df['Mother'].str.find('1') != -1) & (vcf_df['Father'].str.find('1') != -1) +bool_N = vcf_df['Patient'].isin(['1/1']) & (vcf_df['Mother'].str.find('1') == -1) & (vcf_df['Father'].str.find('1') == -1) + +vcf_df.loc[bool_NM,'Pattern'] = 'MN' +vcf_df.loc[bool_NF,'Pattern'] = 'NF' +vcf_df.loc[bool_B,'Pattern'] = 'MF' +vcf_df.loc[bool_N,'Pattern'] = 'NN' + +# Unkown cases - fix: if the other parent is 0/1 1/1 +vcf_df.loc[(vcf_df['Mother']=='./.') & bool_NF, 'Pattern'] = 'UF' +vcf_df.loc[(vcf_df['Father']=='./.') & bool_NM, 'Pattern'] = 'MU' +vcf_df.loc[(vcf_df['Father']=='./.') & (vcf_df['Mother']=='./.') & bool_N, 'Pattern'] = 'UU' + + + + +# Y chromosome non-psudoautosomal region +# https://en.wikipedia.org/wiki/Pseudoautosomal_region +bool_non_pseudo = (vcf_df['#CHROM'] == 'Y') & ((vcf_df['POS'] < Y_autosomal[ref_genome]['cut1']) | ((vcf_df['POS']>Y_autosomal[ref_genome]['cut2']) & (vcf_df['POS'] < Y_autosomal[ref_genome]['cut3'])) | (vcf_df['POS']>Y_autosomal[ref_genome]['cut4'])) +bool_F = vcf_df['Patient'].isin(['0/1','1/0']) & (vcf_df['Father'].str.find('1') != -1) & bool_non_pseudo +bool_N = vcf_df['Patient'].isin(['0/1','1/0']) & (vcf_df['Father'].str.find('1') == -1) & bool_non_pseudo + +vcf_df.loc[bool_F,'Pattern'] = 'F' +vcf_df.loc[bool_N,'Pattern'] = 'N' + +bool_F = vcf_df['Patient'].isin(['1/1']) & (vcf_df['Father'].str.find('1') != -1) & bool_non_pseudo +bool_N = vcf_df['Patient'].isin(['1/1']) & (vcf_df['Father'].str.find('1') == -1) & bool_non_pseudo + +vcf_df.loc[bool_F,'Pattern'] = 'F' +vcf_df.loc[bool_N,'Pattern'] = 'N' + + + +# GATK results +bool_hidenovo = vcf_df['INFO'].str.find('hiConfDeNovo=%s'%(proband_id)) != -1 +bool_lowdenovo = vcf_df['INFO'].str.find('loConfDeNovo=%s'%(proband_id)) != -1 + +vcf_df['GATK'] = 'Inherited' +vcf_df.loc[bool_hidenovo, 'GATK'] = 'hiConfDeNovo' +vcf_df.loc[bool_lowdenovo, 'GATK'] = 'loConfDeNovo' + +inheritance = vcf_df.loc[:,['varid','Patient','Mother','Father','Pattern','GATK']].copy() +inheritance.to_csv('./%s.inheritance.txt'%(sampleID), index=False) + +inheritance_summary = (inheritance['GATK'] + '_' + inheritance['Pattern']).value_counts() +inheritance_summary.to_csv('./%s.summary.txt'%(sampleID)) +print((inheritance['GATK'] + '_' + inheritance['Pattern']).value_counts()) diff --git a/bin/trio_merge_inheritance.py b/bin/trio_merge_inheritance.py new file mode 100755 index 0000000..21324ed --- /dev/null +++ b/bin/trio_merge_inheritance.py @@ -0,0 +1,17 @@ +#!/usr/bin/env python3.8 + +import sys + +from trio.inh_utils import * + +score_file = sys.argv[1] +singleton_rank = sys.argv[2] +inheritance_file = sys.argv[3] +out_path = sys.argv[4] + + +inh_df = matchID_in_inheritance(score_file, singleton_rank, inheritance_file) +inh_df = matrix_inh(inh_df, singleton_rank) +inh_df.to_csv(out_path, sep="\t") + + diff --git a/bin/trio_merge_rm.py b/bin/trio_merge_rm.py new file mode 100755 index 0000000..cfb1fc5 --- /dev/null +++ b/bin/trio_merge_rm.py @@ -0,0 +1,15 @@ +#!/usr/bin/env python3.8 + +import pandas as pd +import sys + +###merge score files with final rank matrix + +#read rank matrix and score file +feature=pd.read_csv("./"+sys.argv[1]+".trio.csv") +ndg = pd.read_csv("./"+sys.argv[1]+".trio.NDG.csv") +ndg = ndg.loc[:,["Unnamed: 0","predict","ranking"]] +ndg.columns = ["Unnamed: 0","predict (nd)","ranking (nd)"] + +feature = feature.merge(ndg, on="Unnamed: 0", how="left") +feature.to_csv("./"+sys.argv[1]+".trio.feature.csv",index=False) \ No newline at end of file diff --git a/bin/trio_rename_VCFheader.py b/bin/trio_rename_VCFheader.py new file mode 100755 index 0000000..b2e71f4 --- /dev/null +++ b/bin/trio_rename_VCFheader.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python3 + +import sys + +proband_id = sys.argv[1] +paternal_id = sys.argv[2] +maternal_id = sys.argv[3] +header_file = sys.argv[4] + +sample_list = [proband_id, paternal_id, maternal_id] + +header = open(header_file, 'r').readlines()[0] +header = header.strip('\n').split('\t')[-3:] +newheader = [] + +for sample in header: + newheader += [i for i in sample_list if i in sample] + +assert len(newheader) == 3 + +sample_name = open("./sample_ids.txt", 'w') +sample_name.write('\n'.join(newheader)) +sample_name.close() diff --git a/docs/source/nf_usage.txt b/docs/source/nf_usage.txt index 5d42773..11fdd64 100644 --- a/docs/source/nf_usage.txt +++ b/docs/source/nf_usage.txt @@ -21,7 +21,6 @@ Args: --input_variant Variant ID Formatted in 'chr-pos-alt-ref' (optional) --bed_filter Path to a BED file to perform an analysis only for regions of interest (optional) --exome_filter Enable exonic filter. Addition will filter out variants outside of exonic region (default: false) - --mode Specify the analysis mode [singleton or trio] (default: singleton) --input_ped Path to the pedgree file for trio mode Metadata Flags: diff --git a/main.nf b/main.nf index b052d79..0431901 100644 --- a/main.nf +++ b/main.nf @@ -1,873 +1,63 @@ nextflow.enable.dsl = 2 +include { + showUsage; showVersion; validateInputParams; addDependentParams +} from "./modules/local/utils" -def showUsage() { - if (params.help) { - def helpFile = file(params.usage_file) // Specify your Markdown file path here - if (helpFile.exists()) { - println helpFile.text - } else { - println """ - Sorry something went wrong, usage file not found! - Please vist our website for more info : https://ai-marrvel.readthedocs.io/en/latest/ - """ - } - exit 0 - } -} - -def showVersion() { - if (!params.version) { - return - } - - println "1.1.0" - exit 0 -} - -def validateInputParams() { - def checkPathParamMap = [ - "input_hpo": params.input_hpo, - "ref_dir" : params.ref_dir, - "ref_ver" : params.ref_ver, - "outdir" : params.outdir, - ] - - checkPathParamMap.each { paramName, paramValue -> - if (paramValue) - return - - println("Input parameter '${paramName}' not specified or is null!") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (params.input_variant && params.input_vcf) { - println("Error: Cannot use '--input_variant' with --input_vcf'") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (params.input_vcf && !params.input_vcf.endsWith(".vcf") && !params.input_vcf.endsWith(".vcf.gz")) { - println("Error: '--input_vcf' value '${params.input_vcf}' should be a VCF file (.vcf) or (.vcf.gz)") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (params.input_variant && params.input_variant.count('-') != 3) { - println("Error: '--input_variant' should be formated like 'X-47038564-T-C'") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (!file(params.ref_dir).isDirectory()) { - println("Error: '--ref_dir' should be an directory.") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (!params.ref_ver.equals("hg19") && !params.ref_ver.equals("hg38") ) { - println("Error: '--ref_ver' value ${params.ref_ver} should be either set to 'hg19' or 'hg38'.") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } -} - -def addDependentParams() { - params.ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" - - // for data dependency - params.chrmap = "${params.ref_dir}/bcf_annotate/chrmap.txt" - - params.ref_loc = "${params.ref_dir}/phrank/${params.ref_ver}/${params.ref_assembly}_symbol_to_location.txt" - params.ref_to_sym = "${params.ref_dir}/phrank/${params.ref_ver}/ensembl_to_symbol.txt" - params.ref_sorted_sym = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_symbol_sorted.txt" - - // FILTER BED - // EXONIC FILTER BED - params.ref_exonic_filter_bed = "${params.ref_dir}/filter_exonic/${params.ref_ver}.bed" - if (params.bed_filter) { - params.ref_filter_bed = params.bed_filter - } else if (params.exome_filter) { - params.ref_filter_bed = params.ref_exonic_filter_bed - } else { - params.ref_filter_bed = "/dev/null" - } - - // for phrank - params.phrank_dagfile = "${params.ref_dir}/phrank/${params.ref_ver}/child_to_parent.txt" - params.phrank_disease_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_pheno.txt" - params.phrank_gene_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_phenotype.txt" - params.phrank_disease_gene = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_gene.txt" - - // OMIM and HPO - params.omim_hgmd_phen = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HGMD_phen.tsv" - params.omim_obo = "${params.ref_dir}/omim_annotate/hp.obo" - params.omim_genemap2 = "${params.ref_dir}/omim_annotate/${params.ref_ver}/genemap2_v2022.rds" - params.omim_pheno = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HPO_OMIM.tsv" - - // GNOMAD VCF - params.ref_gnomad_genome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.genomes.vcf.gz" - params.ref_gnomad_genome_idx = "${params.ref_gnomad_genome}.tbi" - params.ref_gnomad_exome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.exomes.vcf.gz" - params.ref_gnomad_exome_idx = "${params.ref_gnomad_exome}.tbi" +include { + BUILD_REFERENCE_INDEX +} from "./modules/local/singleton" - // VEP - params.vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" - params.vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" - params.vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" +include { + VCF_PRE_PROCESS_TRIO; GENERATE_TRIO_FEATURES; PREDICTION_TRIO +} from "./modules/local/trio" - params.vep_dir_cache = "${params.ref_dir}/vep/${params.ref_ver}/" - params.vep_dir_plugins = "${params.ref_dir}/vep/${params.ref_ver}/Plugins/" - params.vep_custom_gnomad = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_gnomad_name}" - params.vep_custom_clinvar = "${params.ref_dir}/vep/${params.ref_ver}/clinvar_20220730.vcf.gz" - params.vep_custom_hgmd = "${params.ref_dir}/vep/${params.ref_ver}/HGMD_Pro_2022.2_${params.ref_ver}.vcf.gz" - params.vep_plugin_revel = "${params.ref_dir}/vep/${params.ref_ver}/new_tabbed_revel_${params.ref_assembly}.tsv.gz" // changed for hg19 - params.vep_plugin_spliceai_snv = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.snv.${params.ref_ver}.vcf.gz" - params.vep_plugin_spliceai_indel = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.indel.${params.ref_ver}.vcf.gz" - params.vep_plugin_cadd = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_cadd_name}" // changed for hg19 - params.vep_plugin_dbnsfp = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_dbnsfp_name}" - params.vep_idx = "${params.ref_dir}/vep/${params.ref_ver}/*.tbi" - - params.ref_annot_dir = "${params.ref_dir}/annotate" - params.ref_var_tier_dir = "${params.ref_dir}/var_tier" - params.ref_merge_expand_dir = "${params.ref_dir}/merge_expand" - params.ref_mod5_diffusion_dir = "${params.ref_dir}/mod5_diffusion" - params.ref_predict_new_dir = "${params.ref_dir}/predict_new" - params.ref_model_inputs_dir = "${params.ref_dir}/model_inputs" - - // Documentation - params.usage_file = "${projectDir}/docs/source/nf_usage.txt" - params.script_chunking = "${projectDir}/scripts/split_chunks.py" - params.script_annot = "${projectDir}/scripts/annotation/*.py" -} +include { + VCF_PRE_PROCESS; GENERATE_SINGLETON_FEATURES; PREDICTION +} from "./subworkflows/local/singleton" showUsage() showVersion() validateInputParams() -addDependentParams() - -process GENERATE_INPUT_VCF { - input: - val id - - output: - path "input.vcf", emit: vcf - - """ - generate_input_vcf.py $id - """ -} - -process VALIDATE_VCF { - container 'quay.io/biocontainers/vcftools:0.1.16--pl5321hdcf5f25_11' - - input: - path vcf - - output: - path "$vcf", emit: vcf - - """ - vcf-validator $vcf - """ -} - -// Process to handle the VCF file -process NORMALIZE_VCF { - input: - path vcf - - output: - path "input.vcf.gz", emit: vcf - path "input.vcf.gz.tbi", emit: tbi - - script: - """ - # Determine the file type - INPUT_VCF_TYPE="\$(file -b $vcf)" - - if echo "\${INPUT_VCF_TYPE}" | grep -q 'symbolic link to'; then - SYM_LINK="\$(readlink -f $vcf)" - INPUT_VCF_TYPE="\$(file -b \${SYM_LINK})" - fi - - if echo "\${INPUT_VCF_TYPE}" | grep -q 'BGZF'; then - echo "The file is in BGZF format, ready for tabix." - cp $vcf input.vcf.gz - elif echo "\${INPUT_VCF_TYPE}" | grep -q 'gzip compressed data'; then - echo "GZIP format detected, converting to BGZF." - gunzip -c $vcf | bgzip > input.vcf.gz - elif echo "\${INPUT_VCF_TYPE}" | grep -q 'ASCII text'; then - echo "Plain VCF file detected, compressing and indexing." - bgzip -c $vcf > input.vcf.gz - else - echo "The file $vcf does not exist or is not a recognized format." - exit 1 - fi - - # Step 2: if mode is 'trio' - if [ "${params.mode}" == "trio" ]; then - echo "Mode is 'trio': Normalizing joint VCF file." - # Normalize to handle multiallelic rows - bcftools norm --multiallelics -both \ - -Oz -o normalized.tmp.vcf.gz \ - input.vcf.gz - tabix normalized.tmp.vcf.gz - # Remove duplicates - bcftools norm --rm-dup none \ - -Oz -o input.vcf.gz \ - normalized.tmp.vcf.gz - else - echo "Mode is 'singleton': Skipping normalization." - fi - - - tabix -p vcf input.vcf.gz - """ -} - -process FILTER_BED { - input: - path vcf - path tbi - path ref_filter_bed - - output: - path "${params.run_id}.recode.vcf.gz", emit: vcf - path "${params.run_id}.recode.vcf.gz.tbi", emit: tbi - - script: - """ - if [ -f ${ref_filter_bed} ]; then - awk '{gsub(/^chr/, ""); print}' ${ref_filter_bed} > bed - bcftools filter --regions-file bed ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" - tabix -p vcf "${params.run_id}.recode.vcf.gz" - else - cp ${vcf} "${params.run_id}.recode.vcf.gz" - cp ${tbi} "${params.run_id}.recode.vcf.gz.tbi" - fi - """ -} - -process BUILD_REFERENCE_INDEX { - container "broadinstitute/gatk" - storeDir "${params.outdir}/general/reference_index/" - - output: - path "final_${params.ref_ver}.fa", emit: fasta - path "final_${params.ref_ver}.fa.fai", emit: fasta_index - path "final_${params.ref_ver}.dict", emit: fasta_dict - - script: - """ - wget --quiet http://hgdownload.soe.ucsc.edu/goldenPath/${params.ref_ver}/bigZips/${params.ref_ver}.fa.gz - gunzip ${params.ref_ver}.fa.gz - sed 's/>chr/>/g' ${params.ref_ver}.fa > num_prefix_${params.ref_ver}.fa - samtools faidx num_prefix_${params.ref_ver}.fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y M > final_${params.ref_ver}.fa - samtools faidx final_${params.ref_ver}.fa - gatk CreateSequenceDictionary -R final_${params.ref_ver}.fa - """ -} - -process CONVERT_GVCF { - container "broadinstitute/gatk" - - input: - path vcf - path tbi - path fasta - path fasta_index - path fasta_dict - path chrmap_file - - output: - path "${params.run_id}.nog.vcf.gz", emit: vcf - path "${params.run_id}.nog.vcf.gz.tbi", emit: tbi - - - script: - """ - # Define input/output paths and reference genome - reference_genome="$fasta" - input_file="$vcf" - output_file="${params.run_id}.nog.vcf.gz" - - # Step 0: Check for - zcat "\$input_file" | head -n 10000 | grep -q "" || { echo "It's not gVCF"; cp $vcf ${params.run_id}.nog.vcf.gz; cp $tbi ${params.run_id}.nog.vcf.gz.tbi; exit 0; } - - # Step 1: Annotate and remove ID field - echo "Step 1: Annotating and removing ID field..." - bcftools annotate --rename-chrs "$chrmap_file" -x ID "\$input_file" -Oz -o step1.vcf.gz - tabix step1.vcf.gz - bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y step1.vcf.gz -Oz -o step1_1.vcf.gz - - # Step 2: Sort the VCF file - echo "Step 2: Sorting the VCF file..." - bcftools sort step1_1.vcf.gz -Oz -o step2.vcf.gz - - # Step 2.1: Index step2.vcf.gz with tabix - echo "Indexing step2.vcf.gz with tabix..." - tabix -p vcf step2.vcf.gz - - # Step 3: Genotype GVCFs with GATK - echo "Step 3: Running GenotypeGVCFs with GATK..." - gatk GenotypeGVCFs -R "\$reference_genome" -V step2.vcf.gz -O step3.vcf.gz --allow-old-rms-mapping-quality-annotation-data - - # Step 4: Filter based on quality with GATK - echo "Step 4: Running VariantFiltration with GATK..." - gatk VariantFiltration -V step3.vcf.gz -O step4.vcf.gz --filter-expression "QUAL < 30.0" --filter-name "LowQual" - - # Rename the final output file - echo "Renaming the final output file..." - mv step4.vcf.gz "\$output_file" - - # Index the final output using tabix - echo "Indexing the final output with tabix..." - tabix -p vcf "\$output_file" - - # Display the number of non-comment lines (ignore lines starting with #) - lines=\$(zcat "\$output_file" | grep -v '^#' | wc -l) - echo "File: \$output_file has \$lines lines (excluding comment lines)." - - # Clean up intermediate files - echo "Cleaning up intermediate files..." - rm -f step*.vcf.gz* - """ -} - - -process FILTER_UNPASSED { - input: - path vcf - path tbi - path chrmap - - output: - path "${params.run_id}.filt.vcf.gz", emit: vcf - path "${params.run_id}.filt.vcf.gz.tbi", emit: tbi - - script: - """ - # Annotate with new chromosomes and preserve original coordinates in ID - bcftools annotate --rename-chrs $chrmap -x ID $vcf -Oz -o ${params.run_id}-annot - - # Annotate with new IDs based on CHROM, POS, REF, ALT - bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}-add-id.vcf.gz - - #run quality filters - bcftools filter ${params.run_id}-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o ${params.run_id}.filt.vcf.gz - - #check number of variants left - variant_count=\$(bcftools view -H ${params.run_id}.filt.vcf.gz | wc -l) - if [ "\${variant_count}" -gt 0 ]; then - echo "Quality filtering completed successfully. Variants passing the filters: \${variant_count}" - else - echo "The VCF file doesn't have FILTER annotation, or all variants filtered." - echo "Pipeline will proceed with unfiltered VCF file." - cp ${params.run_id}-add-id.vcf.gz ${params.run_id}.filt.vcf.gz - fi - tabix -p vcf ${params.run_id}.filt.vcf.gz - """ -} - - -process FILTER_MITO_AND_UNKOWN_CHR { - publishDir "${params.outdir}/${params.run_id}/vcf/", mode: 'copy' - input: - path vcf - path tbi - - output: - path "${params.run_id}.filt.rmMT.vcf.gz", emit: vcf - path "${params.run_id}.filt.rmMT.vcf.gz.tbi", emit: tbi - - - script: - """ - bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y $vcf -o ${params.run_id}.filt.rmMT.vcf - - bgzip ${params.run_id}.filt.rmMT.vcf - tabix ${params.run_id}.filt.rmMT.vcf.gz - """ -} - - -process VCF_TO_VARIANTS { - input: - path vcf - - output: - path "${params.run_id}-var-filt.txt", emit: var - - script: - """ - zcat $vcf | awk 'substr(\$0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > ${params.run_id}-var.txt - cat ${params.run_id}-var.txt | sed 's/chr//g' | sort -u > ${params.run_id}-var-filt.txt - """ -} - - -process VARIANTS_TO_ENSEMBL { - input: - path var - path ref - - output: - path "${params.run_id}-ensmbl.txt" - - script: - """ - location_to_gene.py $var $ref | \\ - sed 's/:/\\t/g' | sed 's/X\\t/23\\t/g' | sed 's/Y\\t/24\\t/g' | \\ - sed 's/MT\\t/25\\t/g' > ${params.run_id}-ensmbl.txt - """ -} - - -process ENSEMBL_TO_GENESYM { - input: - path ensmbl - path ref_to_sym - path ref_sorted_sym - - output: - path "${params.run_id}-gene.txt", emit: gene - - script: - """ - cat $ensmbl | sort -k5,5 | join -1 5 -2 1 - $ref_to_sym | sed 's/ /\\t/g' | cut -f2- > genesym.txt - cat genesym.txt | cut -f5 | sort -u | join -t\$'\\t' -1 1 -2 2 - $ref_sorted_sym | cut -f2 | sort -u > ${params.run_id}-gene.txt - """ -} - - -process GENESYM_TO_PHRANK { - publishDir "${params.outdir}/${params.run_id}/phrank/", mode: 'copy' - - input: - path gene - path hpo - path dagfile - path disease_annotation - path gene_annotation - path disease_gene - - output: - path "${params.run_id}.phrank.txt", emit: phrank - - script: - """ - run_phrank.py \\ - $gene $hpo $dagfile $disease_annotation $gene_annotation $disease_gene > ${params.run_id}.phrank.txt - """ -} - - -process HPO_SIM { - container 'zhandongliulab/aim-lite-r' - - input: - path hpo - path omim_hgmd_phen - path omim_obo - path omim_genemap2 - path omim_pheno - - output: - path "${params.run_id}-cz", emit: hgmd_sim - path "${params.run_id}-dx", emit: omim_sim - - script: - """ - if [[ -z \$(egrep 'HP:[0-9]{7}' $hpo) ]] ; then - echo "HP:0000001" > $hpo - fi - - phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ - ${params.run_id}-cz ${params.run_id}-dx - """ - -} - -process FILTER_PROBAND { - publishDir "${params.outdir}/${params.run_id}/vcf/", mode: 'copy' - - input: - path vcf - path tbi - path ref_gnomad_genome - path ref_gnomad_genome_idx - path ref_gnomad_exome - path ref_gnomad_exome_idx - - output: - path "${params.run_id}.filt.rmBL.vcf.gz", emit: vcf - - script: - """ - mkdir -m777 isec_tmp1 - bcftools isec -p isec_tmp1 -w 1 -Oz \\ - $vcf $ref_gnomad_genome - # tabix isec_tmp1/0000.vcf.gz - - mkdir -m777 isec_tmp2 - bcftools isec -p isec_tmp2 -w 1 -Oz \\ - $vcf $ref_gnomad_exome - # tabix isec_tmp2/0000.vcf.gz - - mkdir -m777 isec_tmp3 - bcftools isec -p isec_tmp3 -Ov \\ - isec_tmp1/0000.vcf.gz isec_tmp2/0000.vcf.gz - - mv isec_tmp3/0002.vcf ${params.run_id}.filt.rmBL.vcf - bgzip ${params.run_id}.filt.rmBL.vcf - """ - -} - -process SPLIT_VCF_BY_CHROMOSOME { - input: - path vcf - - output: - path "chr*.vcf.gz", emit: chr_vcfs - - script: - """ - # Get the list of chromosomes from the VCF file - - bcftools index ${vcf} - - bcftools query -f '%CHROM\n' ${vcf} | sort | uniq > chrom_list.txt - # Split the VCF file by chromosome - while read chrom; do - bcftools view -r \${chrom} ${vcf} -Oz -o chr\${chrom}.vcf.gz - done < chrom_list.txt - """ -} - -process ANNOTATE_BY_VEP { - tag "${vcf.simpleName}" - publishDir "${params.outdir}/${params.run_id}/vep/", mode: "copy" - - input: - path vcf - path vep_dir_cache - path vep_dir_plugins - path vep_custom_gnomad - path vep_custom_clinvar - path vep_custom_hgmd - path vep_plugin_revel - path vep_plugin_spliceai_snv - path vep_plugin_spliceai_indel - path vep_plugin_cadd - path vep_plugin_dbnsfp - path vep_idx - - output: - path "${vcf.baseName}-vep.txt", emit: vep_output - - script: - def ref_assembly = (params.ref_ver == 'hg38') ? 'GRCh38' : 'GRCh37' - """ - /opt/vep/src/ensembl-vep/vep \\ - --dir_cache ${vep_dir_cache} \\ - --dir_plugins ${vep_dir_plugins} \\ - --fork ${task.cpus} --everything --format vcf \\ - --cache --offline --tab --force_overwrite \\ - --species homo_sapiens --assembly ${ref_assembly} \\ - --custom ${vep_custom_gnomad},gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \\ - --custom ${vep_custom_clinvar},clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \\ - --custom ${vep_custom_hgmd},hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \\ - --af_gnomad --plugin REVEL,${vep_plugin_revel},ALL \\ - --plugin SpliceAI,snv=${vep_plugin_spliceai_snv},indel=${vep_plugin_spliceai_indel},cutoff=0.5 \\ - --plugin CADD,${vep_plugin_cadd},ALL \\ - --plugin dbNSFP,${vep_plugin_dbnsfp},ALL \\ - --individual all --output_file ${vcf.baseName}-vep.txt --input_file $vcf \\ - --buffer_size 50 - """ -} - -process ANNOTATE_BY_MODULES { - tag "${vep.simpleName}" - - input: - path vep - path hgmd_sim, stageAs: "hgmd_sim.tsv" - path omim_sim, stageAs: "omim_sim.tsv" - path ref_annot_dir - - output: - path "${vep.baseName}_scores.csv", emit: scores - - script: - """ - feature.py \\ - -patientHPOsimiOMIM $omim_sim \\ - -patientHPOsimiHGMD $hgmd_sim \\ - -varFile ${vep} \\ - -inFileType vepAnnotTab \\ - -patientFileType one \\ - -genomeRef ${params.ref_ver} \\ - -diseaseInh AD \\ - -modules curate,conserve - - mv scores.csv ${vep.baseName}_scores.csv - """ -} - -process ANNOTATE_TIER { - container 'zhandongliulab/aim-lite-r' - tag "${scores.simpleName}" - - input: - path scores - path phrank - path ref_annot_dir - path ref_var_tier_dir - - output: - path "${scores.simpleName}_Tier.v2.tsv", emit: tier - - script: - """ - mv $scores scores.csv - VarTierDiseaseDBFalse.R ${params.ref_ver} - mv Tier.v2.tsv ${scores.simpleName}_Tier.v2.tsv - """ -} - -process JOIN_PHRANK { - tag "${scores.simpleName}" - - input: - path scores - path phrank - path ref_merge_expand_dir - - output: - path "${scores.simpleName}_scores.txt.gz", emit: compressed_scores - - script: - """ - mv $scores scores.csv - generate_new_matrix_2.py ${params.run_id} ${params.ref_ver} - mv scores.txt.gz ${scores.simpleName}_scores.txt.gz - """ -} - -process MERGE_SCORES_BY_CHROMOSOME { - publishDir "${params.outdir}/${params.run_id}/merged", mode: "copy" - - input: - path phrank - path tier, stageAs: "*_Tier.v2.tsv" - path compressed_scores, stageAs: "*_scores.txt.gz" - - path ref_annot_dir - path ref_mod5_diffusion_dir - path ref_merge_expand_dir - - output: - path "${params.run_id}.matrix.txt", emit: merged_matrix - path "scores.txt.gz", emit: merged_compressed_scores - - script: - """ - # Merge matrices - awk 'FNR==1 && NR!=1{next;}{print}' ${tier} > Tier.v2.tsv - - # Merge compressed scores - header_written=false - - for file in ${compressed_scores}; do - if [ "\$header_written" = false ]; then - # Write the first file's content, including the header - zcat \${file} | gzip > scores.txt.gz - header_written=true - else - # Skip the header and append the rest of the data - zcat \${file} | tail -n +2 | gzip >> scores.txt.gz - fi - done - - post_processing.py ${params.run_id} ${params.ref_ver} - """ -} - -process PREDICTION { - publishDir "${params.outdir}/${params.run_id}/prediction/", mode: "copy" - - input: - path merged_matrix - path merged_compressed_scores - path ref_model_inputs_dir - - output: - path "conf_4Model/*.csv" - path "conf_4Model/integrated/*.csv" - path "shap_outputs" - - script: - """ - mkdir final_matrix_expanded - mkdir conf_4Model - - run_final.py ${params.run_id} - merge_rm.py ${params.run_id} - extraModel_main.py -id ${params.run_id} - """ -} - - -workflow VCF_PRE_PROCESS { - take: - vcf - tbi - fasta - fasta_index - fasta_dict - - main: - CONVERT_GVCF( - vcf, - tbi, - fasta, - fasta_index, - fasta_dict, - params.chrmap - ) - FILTER_UNPASSED( - CONVERT_GVCF.out.vcf, - CONVERT_GVCF.out.tbi, - params.chrmap - ) - FILTER_MITO_AND_UNKOWN_CHR( - FILTER_UNPASSED.out.vcf, - FILTER_UNPASSED.out.tbi, - ) - FILTER_BED( - FILTER_MITO_AND_UNKOWN_CHR.out.vcf, - FILTER_MITO_AND_UNKOWN_CHR.out.tbi, - moduleDir.resolve(params.ref_filter_bed), - ) - FILTER_PROBAND( - FILTER_BED.out.vcf, - FILTER_BED.out.tbi, - params.ref_gnomad_genome, - params.ref_gnomad_genome_idx, - params.ref_gnomad_exome, - params.ref_gnomad_exome_idx - ) - - emit: - vcf = FILTER_PROBAND.out.vcf -} - -workflow PHRANK_SCORING { - take: - vcf - - main: - VCF_TO_VARIANTS(vcf) - VARIANTS_TO_ENSEMBL(VCF_TO_VARIANTS.out, params.ref_loc) - ENSEMBL_TO_GENESYM(VARIANTS_TO_ENSEMBL.out, params.ref_to_sym, params.ref_sorted_sym) - GENESYM_TO_PHRANK(ENSEMBL_TO_GENESYM.out, - file(params.input_hpo), - params.phrank_dagfile, - params.phrank_disease_annotation, - params.phrank_gene_annotation, - params.phrank_disease_gene) - - emit: - phrank = GENESYM_TO_PHRANK.out -} +addDependentParams(params) workflow { - if (params.input_vcf) { - input_vcf = file(params.input_vcf) - - VALIDATE_VCF(input_vcf) - NORMALIZE_VCF(VALIDATE_VCF.out.vcf) + hpo = params.input_hpo + if (params.input_ped) { BUILD_REFERENCE_INDEX() - VCF_PRE_PROCESS( - NORMALIZE_VCF.out.vcf, - NORMALIZE_VCF.out.tbi, + VCF_PRE_PROCESS_TRIO( + file(params.input_vcf), + file(params.input_ped), BUILD_REFERENCE_INDEX.out.fasta, BUILD_REFERENCE_INDEX.out.fasta_index, BUILD_REFERENCE_INDEX.out.fasta_dict, + params.chrmap, ) + vcf = VCF_PRE_PROCESS_TRIO.out.vcf + } else if (params.input_vcf) { + VCF_PRE_PROCESS(file(params.input_vcf)) vcf = VCF_PRE_PROCESS.out.vcf } else if (params.input_variant) { GENERATE_INPUT_VCF(params.input_variant) - input_vcf = GENERATE_INPUT_VCF.out.vcf - - VALIDATE_VCF(input_vcf) - NORMALIZE_VCF(VALIDATE_VCF.out.vcf) - vcf = NORMALIZE_VCF.out.vcf + vcf = GENERATE_INPUT_VCF.out.vcf } - SPLIT_VCF_BY_CHROMOSOME(vcf) - ANNOTATE_BY_VEP( - SPLIT_VCF_BY_CHROMOSOME.out.chr_vcfs.flatten(), - params.vep_dir_cache, - params.vep_dir_plugins, - params.vep_custom_gnomad, - params.vep_custom_clinvar, - params.vep_custom_hgmd, - params.vep_plugin_revel, - params.vep_plugin_spliceai_snv, - params.vep_plugin_spliceai_indel, - params.vep_plugin_cadd, - params.vep_plugin_dbnsfp, - file(params.vep_idx) - ) - - HPO_SIM(params.input_hpo, - params.omim_hgmd_phen, - params.omim_obo, - params.omim_genemap2, - params.omim_pheno) - - ANNOTATE_BY_MODULES ( - ANNOTATE_BY_VEP.out.vep_output, - HPO_SIM.out.hgmd_sim, - HPO_SIM.out.omim_sim, - file(params.ref_annot_dir) - ) - - PHRANK_SCORING( - NORMALIZE_VCF.out.vcf, - ) - - ANNOTATE_TIER ( - ANNOTATE_BY_MODULES.out.scores, - PHRANK_SCORING.out, - file(params.ref_annot_dir), - file(params.ref_var_tier_dir), - ) - - JOIN_PHRANK ( - ANNOTATE_BY_MODULES.out.scores, - PHRANK_SCORING.out, - file(params.ref_merge_expand_dir) - ) - - MERGE_SCORES_BY_CHROMOSOME( - PHRANK_SCORING.out, - ANNOTATE_TIER.out.tier.collect(), - JOIN_PHRANK.out.compressed_scores.collect(), - file(params.ref_annot_dir), - file(params.ref_mod5_diffusion_dir), - file(params.ref_merge_expand_dir) - ) - - // Run Prediction on the final merged output - // Includes Model interpretability step to predict + GENERATE_SINGLETON_FEATURES(vcf, hpo) PREDICTION( - MERGE_SCORES_BY_CHROMOSOME.out.merged_matrix, - MERGE_SCORES_BY_CHROMOSOME.out.merged_compressed_scores, + GENERATE_SINGLETON_FEATURES.out.merged_matrix, + GENERATE_SINGLETON_FEATURES.out.merged_compressed_scores, file(params.ref_model_inputs_dir) ) + + if (params.input_ped) { + GENERATE_TRIO_FEATURES( + GENERATE_SINGLETON_FEATURES.out.merged_compressed_scores, + PREDICTION.out.default_predictions, + VCF_PRE_PROCESS_TRIO.out.inheritance, + ) + PREDICTION_TRIO( + GENERATE_SINGLETON_FEATURES.out.merged_compressed_scores, + GENERATE_TRIO_FEATURES.out.triomatrix, + ) + } } diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf new file mode 100644 index 0000000..0d2d8bf --- /dev/null +++ b/modules/local/singleton/main.nf @@ -0,0 +1,571 @@ + +process GENERATE_INPUT_VCF { + input: + val id + + output: + path "input.vcf", emit: vcf + + """ + generate_input_vcf.py $id + """ +} + +process VALIDATE_VCF { + container 'quay.io/biocontainers/vcftools:0.1.16--pl5321hdcf5f25_11' + + input: + path vcf + + output: + path "$vcf", emit: vcf + + """ + vcf-validator $vcf + """ +} + +// Process to handle the VCF file +process NORMALIZE_VCF { + input: + path vcf + + output: + path "input.vcf.gz", emit: vcf + path "input.vcf.gz.tbi", emit: tbi + + script: + """ + # Determine the file type + INPUT_VCF_TYPE="\$(file -b $vcf)" + + if echo "\${INPUT_VCF_TYPE}" | grep -q 'symbolic link to'; then + SYM_LINK="\$(readlink -f $vcf)" + INPUT_VCF_TYPE="\$(file -b \${SYM_LINK})" + fi + + if echo "\${INPUT_VCF_TYPE}" | grep -q 'BGZF'; then + echo "The file is in BGZF format, ready for tabix." + cp $vcf input.vcf.gz + elif echo "\${INPUT_VCF_TYPE}" | grep -q 'gzip compressed data'; then + echo "GZIP format detected, converting to BGZF." + gunzip -c $vcf | bgzip > input.vcf.gz + elif echo "\${INPUT_VCF_TYPE}" | grep -q 'ASCII text'; then + echo "Plain VCF file detected, compressing and indexing." + bgzip -c $vcf > input.vcf.gz + else + echo "The file $vcf does not exist or is not a recognized format." + exit 1 + fi + + tabix -p vcf input.vcf.gz + """ +} + +process FILTER_BED { + input: + path vcf + path tbi + path ref_filter_bed + + output: + path "${params.run_id}.recode.vcf.gz", emit: vcf + path "${params.run_id}.recode.vcf.gz.tbi", emit: tbi + + script: + """ + if [ -f ${ref_filter_bed} ]; then + awk '{gsub(/^chr/, ""); print}' ${ref_filter_bed} > bed + bcftools filter --regions-file bed ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" + tabix -p vcf "${params.run_id}.recode.vcf.gz" + else + cp ${vcf} "${params.run_id}.recode.vcf.gz" + cp ${tbi} "${params.run_id}.recode.vcf.gz.tbi" + fi + """ +} + +process BUILD_REFERENCE_INDEX { + container "broadinstitute/gatk" + storeDir "${params.outdir}/general/reference_index/" + + output: + path "final_${params.ref_ver}.fa", emit: fasta + path "final_${params.ref_ver}.fa.fai", emit: fasta_index + path "final_${params.ref_ver}.dict", emit: fasta_dict + + script: + """ + wget --quiet http://hgdownload.soe.ucsc.edu/goldenPath/${params.ref_ver}/bigZips/${params.ref_ver}.fa.gz + gunzip ${params.ref_ver}.fa.gz + sed 's/>chr/>/g' ${params.ref_ver}.fa > num_prefix_${params.ref_ver}.fa + samtools faidx num_prefix_${params.ref_ver}.fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y M > final_${params.ref_ver}.fa + samtools faidx final_${params.ref_ver}.fa + gatk CreateSequenceDictionary -R final_${params.ref_ver}.fa + """ +} + +process CONVERT_GVCF { + container "broadinstitute/gatk" + + input: + path vcf + path tbi + path fasta + path fasta_index + path fasta_dict + path chrmap_file + + output: + path "${params.run_id}.nog.vcf.gz", emit: vcf + path "${params.run_id}.nog.vcf.gz.tbi", emit: tbi + + + script: + """ + # Define input/output paths and reference genome + reference_genome="$fasta" + input_file="$vcf" + output_file="${params.run_id}.nog.vcf.gz" + + # Step 0: Check for + zcat "\$input_file" | head -n 10000 | grep -q "" || { echo "It's not gVCF"; cp $vcf ${params.run_id}.nog.vcf.gz; cp $tbi ${params.run_id}.nog.vcf.gz.tbi; exit 0; } + + # Step 1: Annotate and remove ID field + echo "Step 1: Annotating and removing ID field..." + bcftools annotate --rename-chrs "$chrmap_file" -x ID "\$input_file" -Oz -o step1.vcf.gz + tabix step1.vcf.gz + bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y step1.vcf.gz -Oz -o step1_1.vcf.gz + + # Step 2: Sort the VCF file + echo "Step 2: Sorting the VCF file..." + bcftools sort step1_1.vcf.gz -Oz -o step2.vcf.gz + + # Step 2.1: Index step2.vcf.gz with tabix + echo "Indexing step2.vcf.gz with tabix..." + tabix -p vcf step2.vcf.gz + + # Step 3: Genotype GVCFs with GATK + echo "Step 3: Running GenotypeGVCFs with GATK..." + gatk GenotypeGVCFs -R "\$reference_genome" -V step2.vcf.gz -O step3.vcf.gz --allow-old-rms-mapping-quality-annotation-data + + # Step 4: Filter based on quality with GATK + echo "Step 4: Running VariantFiltration with GATK..." + gatk VariantFiltration -V step3.vcf.gz -O step4.vcf.gz --filter-expression "QUAL < 30.0" --filter-name "LowQual" + + # Rename the final output file + echo "Renaming the final output file..." + mv step4.vcf.gz "\$output_file" + + # Index the final output using tabix + echo "Indexing the final output with tabix..." + tabix -p vcf "\$output_file" + + # Display the number of non-comment lines (ignore lines starting with #) + lines=\$(zcat "\$output_file" | grep -v '^#' | wc -l) + echo "File: \$output_file has \$lines lines (excluding comment lines)." + + # Clean up intermediate files + echo "Cleaning up intermediate files..." + rm -f step*.vcf.gz* + """ +} + + +process FILTER_UNPASSED { + input: + path vcf + path tbi + path chrmap + + output: + path "${params.run_id}.filt.vcf.gz", emit: vcf + path "${params.run_id}.filt.vcf.gz.tbi", emit: tbi + + script: + """ + # Annotate with new chromosomes and preserve original coordinates in ID + bcftools annotate --rename-chrs $chrmap -x ID $vcf -Oz -o ${params.run_id}-annot + + # Annotate with new IDs based on CHROM, POS, REF, ALT + bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}-add-id.vcf.gz + + #run quality filters + bcftools filter ${params.run_id}-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o ${params.run_id}.filt.vcf.gz + + #check number of variants left + variant_count=\$(bcftools view -H ${params.run_id}.filt.vcf.gz | wc -l) + if [ "\${variant_count}" -gt 0 ]; then + echo "Quality filtering completed successfully. Variants passing the filters: \${variant_count}" + else + echo "The VCF file doesn't have FILTER annotation, or all variants filtered." + echo "Pipeline will proceed with unfiltered VCF file." + cp ${params.run_id}-add-id.vcf.gz ${params.run_id}.filt.vcf.gz + fi + tabix -p vcf ${params.run_id}.filt.vcf.gz + """ +} + + +process FILTER_MITO_AND_UNKOWN_CHR { + publishDir "${params.outdir}/${params.run_id}/vcf/", mode: 'copy' + input: + path vcf + path tbi + + output: + path "${params.run_id}.filt.rmMT.vcf.gz", emit: vcf + path "${params.run_id}.filt.rmMT.vcf.gz.tbi", emit: tbi + + + script: + """ + bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y $vcf -o ${params.run_id}.filt.rmMT.vcf + + bgzip ${params.run_id}.filt.rmMT.vcf + tabix ${params.run_id}.filt.rmMT.vcf.gz + """ +} + + +process VCF_TO_VARIANTS { + input: + path vcf + + output: + path "${params.run_id}-var-filt.txt", emit: var + + script: + """ + zcat $vcf | awk 'substr(\$0, 1, 1) != "#"' | cut -f1,2,4,5 | sed 's/\t/:/g' > ${params.run_id}-var.txt + cat ${params.run_id}-var.txt | sed 's/chr//g' | sort -u > ${params.run_id}-var-filt.txt + """ +} + + +process VARIANTS_TO_ENSEMBL { + input: + path var + path ref + + output: + path "${params.run_id}-ensmbl.txt" + + script: + """ + location_to_gene.py $var $ref | \\ + sed 's/:/\\t/g' | sed 's/X\\t/23\\t/g' | sed 's/Y\\t/24\\t/g' | \\ + sed 's/MT\\t/25\\t/g' > ${params.run_id}-ensmbl.txt + """ +} + + +process ENSEMBL_TO_GENESYM { + input: + path ensmbl + path ref_to_sym + path ref_sorted_sym + + output: + path "${params.run_id}-gene.txt", emit: gene + + script: + """ + cat $ensmbl | sort -k5,5 | join -1 5 -2 1 - $ref_to_sym | sed 's/ /\\t/g' | cut -f2- > genesym.txt + cat genesym.txt | cut -f5 | sort -u | join -t\$'\\t' -1 1 -2 2 - $ref_sorted_sym | cut -f2 | sort -u > ${params.run_id}-gene.txt + """ +} + + +process GENESYM_TO_PHRANK { + publishDir "${params.outdir}/${params.run_id}/phrank/", mode: 'copy' + + input: + path gene + path hpo + path dagfile + path disease_annotation + path gene_annotation + path disease_gene + + output: + path "${params.run_id}.phrank.txt", emit: phrank + + script: + """ + run_phrank.py \\ + $gene $hpo $dagfile $disease_annotation $gene_annotation $disease_gene > ${params.run_id}.phrank.txt + """ +} + + +process HPO_SIM { + container 'zhandongliulab/aim-lite-r' + + input: + path hpo + path omim_hgmd_phen + path omim_obo + path omim_genemap2 + path omim_pheno + + output: + path "${params.run_id}-cz", emit: hgmd_sim + path "${params.run_id}-dx", emit: omim_sim + + script: + """ + if [[ -z \$(egrep 'HP:[0-9]{7}' $hpo) ]] ; then + echo "HP:0000001" > $hpo + fi + + phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ + ${params.run_id}-cz ${params.run_id}-dx + """ + +} + +process FILTER_PROBAND { + publishDir "${params.outdir}/${params.run_id}/vcf/", mode: 'copy' + + input: + path vcf + path tbi + path ref_gnomad_genome + path ref_gnomad_genome_idx + path ref_gnomad_exome + path ref_gnomad_exome_idx + + output: + path "${params.run_id}.filt.rmBL.vcf.gz", emit: vcf + + script: + """ + mkdir -m777 isec_tmp1 + bcftools isec -p isec_tmp1 -w 1 -Oz \\ + $vcf $ref_gnomad_genome + # tabix isec_tmp1/0000.vcf.gz + + mkdir -m777 isec_tmp2 + bcftools isec -p isec_tmp2 -w 1 -Oz \\ + $vcf $ref_gnomad_exome + # tabix isec_tmp2/0000.vcf.gz + + mkdir -m777 isec_tmp3 + bcftools isec -p isec_tmp3 -Ov \\ + isec_tmp1/0000.vcf.gz isec_tmp2/0000.vcf.gz + + mv isec_tmp3/0002.vcf ${params.run_id}.filt.rmBL.vcf + bgzip ${params.run_id}.filt.rmBL.vcf + """ + +} + +process SPLIT_VCF_BY_CHROMOSOME { + input: + path vcf + + output: + path "chr*.vcf.gz", emit: chr_vcfs + + script: + """ + # Get the list of chromosomes from the VCF file + + bcftools index ${vcf} + + bcftools query -f '%CHROM\n' ${vcf} | sort | uniq > chrom_list.txt + # Split the VCF file by chromosome + while read chrom; do + bcftools view -r \${chrom} ${vcf} -Oz -o chr\${chrom}.vcf.gz + done < chrom_list.txt + """ +} + +process ANNOTATE_BY_VEP { + tag "${vcf.simpleName}" + publishDir "${params.outdir}/${params.run_id}/vep/", mode: "copy" + + input: + path vcf + path vep_dir_cache + path vep_dir_plugins + path vep_custom_gnomad + path vep_custom_clinvar + path vep_custom_hgmd + path vep_plugin_revel + path vep_plugin_spliceai_snv + path vep_plugin_spliceai_indel + path vep_plugin_cadd + path vep_plugin_dbnsfp + path vep_idx + + output: + path "${vcf.baseName}-vep.txt", emit: vep_output + + script: + def ref_assembly = (params.ref_ver == 'hg38') ? 'GRCh38' : 'GRCh37' + """ + /opt/vep/src/ensembl-vep/vep \\ + --dir_cache ${vep_dir_cache} \\ + --dir_plugins ${vep_dir_plugins} \\ + --fork ${task.cpus} --everything --format vcf \\ + --cache --offline --tab --force_overwrite \\ + --species homo_sapiens --assembly ${ref_assembly} \\ + --custom ${vep_custom_gnomad},gnomADg,vcf,exact,0,AF,AF_popmax,controls_nhomalt \\ + --custom ${vep_custom_clinvar},clinvar,vcf,exact,0,CLNREVSTAT,CLNSIG,CLNSIGCONF \\ + --custom ${vep_custom_hgmd},hgmd,vcf,exact,0,CLASS,GENE,PHEN,RANKSCORE \\ + --af_gnomad --plugin REVEL,${vep_plugin_revel},ALL \\ + --plugin SpliceAI,snv=${vep_plugin_spliceai_snv},indel=${vep_plugin_spliceai_indel},cutoff=0.5 \\ + --plugin CADD,${vep_plugin_cadd},ALL \\ + --plugin dbNSFP,${vep_plugin_dbnsfp},ALL \\ + --individual all --output_file ${vcf.baseName}-vep.txt --input_file $vcf \\ + --buffer_size 50 + """ +} + +process ANNOTATE_BY_MODULES { + tag "${vep.simpleName}" + + input: + path vep + path hgmd_sim, stageAs: "hgmd_sim.tsv" + path omim_sim, stageAs: "omim_sim.tsv" + path ref_annot_dir + + output: + path "${vep.baseName}_scores.csv", emit: scores + + script: + """ + feature.py \\ + -patientHPOsimiOMIM $omim_sim \\ + -patientHPOsimiHGMD $hgmd_sim \\ + -varFile ${vep} \\ + -inFileType vepAnnotTab \\ + -patientFileType one \\ + -genomeRef ${params.ref_ver} \\ + -diseaseInh AD \\ + -modules curate,conserve + + mv scores.csv ${vep.baseName}_scores.csv + """ +} + +process ANNOTATE_TIER { + container 'zhandongliulab/aim-lite-r' + tag "${scores.simpleName}" + + input: + path scores + path phrank + path ref_annot_dir + path ref_var_tier_dir + + output: + path "${scores.simpleName}_Tier.v2.tsv", emit: tier + + script: + """ + mv $scores scores.csv + VarTierDiseaseDBFalse.R ${params.ref_ver} + mv Tier.v2.tsv ${scores.simpleName}_Tier.v2.tsv + """ +} + +process JOIN_PHRANK { + tag "${scores.simpleName}" + + input: + path scores + path phrank + path ref_merge_expand_dir + + output: + path "${scores.simpleName}_scores.txt.gz", emit: compressed_scores + + script: + """ + mv $scores scores.csv + generate_new_matrix_2.py ${params.run_id} ${params.ref_ver} + mv scores.txt.gz ${scores.simpleName}_scores.txt.gz + """ +} + +process MERGE_SCORES_BY_CHROMOSOME { + publishDir "${params.outdir}/${params.run_id}/merged", mode: "copy" + + input: + path phrank + path tier, stageAs: "*_Tier.v2.tsv" + path compressed_scores, stageAs: "*_scores.txt.gz" + + path ref_annot_dir + path ref_mod5_diffusion_dir + path ref_merge_expand_dir + + output: + path "${params.run_id}.matrix.txt", emit: merged_matrix + path "scores.txt.gz", emit: merged_compressed_scores + + script: + """ + # Merge matrices + awk 'FNR==1 && NR!=1{next;}{print}' ${tier} > Tier.v2.tsv + + # Merge compressed scores + header_written=false + + for file in ${compressed_scores}; do + if [ "\$header_written" = false ]; then + # Write the first file's content, including the header + zcat \${file} | gzip > scores.txt.gz + header_written=true + else + # Skip the header and append the rest of the data + zcat \${file} | tail -n +2 | gzip >> scores.txt.gz + fi + done + + post_processing.py ${params.run_id} ${params.ref_ver} + """ +} + +process PREDICTION { + publishDir "${params.outdir}/${params.run_id}/prediction/", mode: "copy" + + input: + path merged_matrix + path merged_compressed_scores + path ref_model_inputs_dir + + output: + path "conf_4Model/${params.run_id}_default_predictions.csv", emit: "default_predictions" + path "conf_4Model/${params.run_id}_recessive_predictions.csv" + path "conf_4Model/${params.run_id}_nd_predictions.csv" + path "conf_4Model/${params.run_id}_nd_recessive_predictions.csv" + path "conf_4Model/integrated/*.csv" + path "final_matrix_expanded/*.csv.gz" + path "shap_outputs" + + script: + """ + mkdir final_matrix_expanded + mkdir conf_4Model + + run_final.py \ + $ref_model_inputs_dir/default/final_model.job \ + $ref_model_inputs_dir/default/features.csv \ + $merged_matrix \ + ${params.run_id}.feature.csv + + # Generate final matrix expanded + merge_rm.py \ + ${params.run_id}.feature.csv \ + $merged_compressed_scores \ + final_matrix_expanded/${params.run_id}.expanded.csv.gz + + # Generate conf_4Model + extraModel_main.py -id ${params.run_id} + """ +} diff --git a/modules/local/trio/main.nf b/modules/local/trio/main.nf new file mode 100644 index 0000000..f2174b5 --- /dev/null +++ b/modules/local/trio/main.nf @@ -0,0 +1,165 @@ +process VCF_PRE_PROCESS_TRIO { + container "broadinstitute/gatk" + publishDir "${params.outdir}/${params.run_id}/vcf_trio", mode: 'copy' + + input: + path vcf + path ped + path fasta + path fasta_index + path fasta_dict + path chrmap_file + + output: + path "${params.run_id}_fixed.vcf.gz", emit: "vcf" + path "${params.run_id}.inheritance.txt", emit: "inheritance" + + script: + """ + # Extract proband, maternal, and paternal id from ped file, sample IDs in ped should be the same as in the vcf file + lastrow=\$(tail -n 1 $ped) + SAMPLE_P=\$(echo "\$lastrow" | awk '{print \$2}') + SAMPLE_F=\$(echo "\$lastrow" | awk '{print \$3}') + SAMPLE_M=\$(echo "\$lastrow" | awk '{print \$4}') + + echo "Normalize joint VCF file, split multiallelics rows" + bcftools norm --multiallelics -both \ + -Oz -o ./${params.run_id}.tmp.vcf.gz \ + $vcf + tabix -p vcf ./${params.run_id}.tmp.vcf.gz + + bcftools norm --rm-dup none \ + -Oz -o ./${params.run_id}.joint.vcf.gz \ + ./${params.run_id}.tmp.vcf.gz + + tabix -p vcf ./${params.run_id}.joint.vcf.gz + zcat ./${params.run_id}.joint.vcf.gz | grep '^#CHROM' > ./vcfheaders.txt #* extract header from vcf file + + # create ./sample_ids.txt + trio_rename_VCFheader.py \ + \$SAMPLE_P \ + \$SAMPLE_F \ + \$SAMPLE_M \ + ./vcfheaders.txt + + bcftools reheader --samples ./sample_ids.txt \ + -o ./${params.run_id}.joint2.vcf.gz \ + ./${params.run_id}.joint.vcf.gz + + mv ./${params.run_id}.joint2.vcf.gz ./${params.run_id}.joint.vcf.gz + tabix -f -p vcf ./${params.run_id}.joint.vcf.gz + + bcftools annotate --rename-chrs "$chrmap_file" -x ID ./${params.run_id}.joint.vcf.gz -Oz -o step1.vcf.gz + tabix step1.vcf.gz + bcftools view -r 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y step1.vcf.gz -Oz -o step1_1.vcf.gz + + mv step1_1.vcf.gz ./${params.run_id}.joint.vcf.gz + + # Index the final output using tabix + echo "Indexing the final output with tabix..." + tabix -f -p vcf ./${params.run_id}.joint.vcf.gz + + # De Novo Calling using VariantAnnotator + #* GATK 4.2.6.0 + echo "GATK De Novo Calling..." + gatk VariantAnnotator \ + -A PossibleDeNovo \ + -R ${fasta} \ + --pedigree $ped \ + -V ./${params.run_id}.joint.vcf.gz \ + -O ./${params.run_id}.DeNovo.g.vcf + + echo "Check variants inheritance..." + + # Extract patient VCF from joint VCF + #======================================================= + # TODOs + #======================================================= + + echo "Extract proband VCF..." + #* extract the proband variants from the joint vcf file + bcftools view -c1 -Oz -o./${params.run_id}.vcf.gz -s \${SAMPLE_P} ${params.run_id}.joint.vcf.gz + + echo "Fix VCF formatting..." + cat ${projectDir}/resources/trio_pipeline/acceptable_header.txt | grep -v '#CHROM' > header.txt + zcat ./${params.run_id}.vcf.gz | grep '#CHROM' | head -1 >> header.txt + zcat ./${params.run_id}.vcf.gz | grep -v '#' | awk ' + BEGIN {FS = "\\t"} { + \$7 = "PASS"; + \$8 = "."; + gsub(":.*", "", \$9); + gsub(":.*", "", \$10); + gsub("\\|", "/", \$10); + print \$1 "\\t" \$2 "\\t" \$3 "\\t" \$4 "\\t" \$5 "\\t" \$6 "\\t" \$7 "\\t" \$8 "\\t" \$9 "\\t" \$10 + }' > body.txt + cat header.txt body.txt | bgzip > ./${params.run_id}_fixed.vcf.gz + + # create ${params.run_id}.inheritance.txt and ${params.run_id}.summary.txt + trio_check_inheritance.GATK.py \ + ${params.run_id} \ + ./${params.run_id}.DeNovo.g.vcf \ + ${params.ref_ver} \ + \$SAMPLE_P \ + \$SAMPLE_F \ + \$SAMPLE_M + + + """ +} + +process GENERATE_TRIO_FEATURES { + publishDir "${params.outdir}/${params.run_id}/features_trio", mode: 'copy' + + input: + path merged_compressed_scores + path merged_matrix + path inheritance + + output: + path "${params.run_id}.triomatrix.tsv", emit: triomatrix + + script: + """ + trio_merge_inheritance.py \ + $merged_compressed_scores \ + $merged_matrix \ + $inheritance \ + ./${params.run_id}.triomatrix.tsv + """ +} + +process PREDICTION_TRIO { + container "zhandongliulab/aim-lite:1.0" + publishDir "${params.outdir}/${params.run_id}/prediction_trio", mode: 'copy' + + input: + path merged_compressed_scores + path triomatrix + + output: + path "*.trio.expanded.csv.gz" + path "*.trio.feature.csv" + + script: + """ + run_final.py \ + ${projectDir}/resources/trio_pipeline/rf_trio_base.job \ + ${projectDir}/resources/trio_pipeline/features.csv \ + $triomatrix \ + ./${params.run_id}.trio.csv + run_final.py \ + ${projectDir}/resources/trio_pipeline/rf_trio_NDG.job \ + ${projectDir}/resources/trio_pipeline/features_NDG.csv \ + $triomatrix \ + ./${params.run_id}.trio.NDG.csv + + + # Generate ${params.run_id}.trio.feature.csv + trio_merge_rm.py ${params.run_id} + + merge_rm.py \ + ${params.run_id}.trio.feature.csv \ + $merged_compressed_scores \ + ${params.run_id}.trio.expanded.csv.gz + """ +} diff --git a/modules/local/utils/main.nf b/modules/local/utils/main.nf new file mode 100644 index 0000000..97d1143 --- /dev/null +++ b/modules/local/utils/main.nf @@ -0,0 +1,141 @@ + +def showUsage() { + if (params.help) { + def helpFile = file(params.usage_file) // Specify your Markdown file path here + if (helpFile.exists()) { + println helpFile.text + } else { + println """ + Sorry something went wrong, usage file not found! + Please vist our website for more info : https://ai-marrvel.readthedocs.io/en/latest/ + """ + } + exit 0 + } +} + +def showVersion() { + if (!params.version) { + return + } + + println "1.1.0" + exit 0 +} + +def validateInputParams() { + def checkPathParamMap = [ + "input_hpo": params.input_hpo, + "ref_dir" : params.ref_dir, + "ref_ver" : params.ref_ver, + "outdir" : params.outdir, + ] + + checkPathParamMap.each { paramName, paramValue -> + if (paramValue) + return + + println("Input parameter '${paramName}' not specified or is null!") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (params.input_variant && params.input_vcf) { + println("Error: Cannot use '--input_variant' with --input_vcf'") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (params.input_vcf && !params.input_vcf.endsWith(".vcf") && !params.input_vcf.endsWith(".vcf.gz")) { + println("Error: '--input_vcf' value '${params.input_vcf}' should be a VCF file (.vcf) or (.vcf.gz)") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (params.input_variant && params.input_variant.count('-') != 3) { + println("Error: '--input_variant' should be formated like 'X-47038564-T-C'") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (!file(params.ref_dir).isDirectory()) { + println("Error: '--ref_dir' should be an directory.") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } + + if (!params.ref_ver.equals("hg19") && !params.ref_ver.equals("hg38") ) { + println("Error: '--ref_ver' value ${params.ref_ver} should be either set to 'hg19' or 'hg38'.") + println("To see usage and available parameters run `nextflow run main.nf --help`") + exit 1 + } +} + +def addDependentParams(params) { + params.ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" + + // for data dependency + params.chrmap = "${params.ref_dir}/bcf_annotate/chrmap.txt" + + params.ref_loc = "${params.ref_dir}/phrank/${params.ref_ver}/${params.ref_assembly}_symbol_to_location.txt" + params.ref_to_sym = "${params.ref_dir}/phrank/${params.ref_ver}/ensembl_to_symbol.txt" + params.ref_sorted_sym = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_symbol_sorted.txt" + + // FILTER BED + // EXONIC FILTER BED + params.ref_exonic_filter_bed = "${params.ref_dir}/filter_exonic/${params.ref_ver}.bed" + if (params.bed_filter) { + params.ref_filter_bed = params.bed_filter + } else if (params.exome_filter) { + params.ref_filter_bed = params.ref_exonic_filter_bed + } else { + params.ref_filter_bed = "/dev/null" + } + + // for phrank + params.phrank_dagfile = "${params.ref_dir}/phrank/${params.ref_ver}/child_to_parent.txt" + params.phrank_disease_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_pheno.txt" + params.phrank_gene_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_phenotype.txt" + params.phrank_disease_gene = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_gene.txt" + + // OMIM and HPO + params.omim_hgmd_phen = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HGMD_phen.tsv" + params.omim_obo = "${params.ref_dir}/omim_annotate/hp.obo" + params.omim_genemap2 = "${params.ref_dir}/omim_annotate/${params.ref_ver}/genemap2_v2022.rds" + params.omim_pheno = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HPO_OMIM.tsv" + + // GNOMAD VCF + params.ref_gnomad_genome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.genomes.vcf.gz" + params.ref_gnomad_genome_idx = "${params.ref_gnomad_genome}.tbi" + params.ref_gnomad_exome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.exomes.vcf.gz" + params.ref_gnomad_exome_idx = "${params.ref_gnomad_exome}.tbi" + + // VEP + params.vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" + params.vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" + params.vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" + + params.vep_dir_cache = "${params.ref_dir}/vep/${params.ref_ver}/" + params.vep_dir_plugins = "${params.ref_dir}/vep/${params.ref_ver}/Plugins/" + params.vep_custom_gnomad = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_gnomad_name}" + params.vep_custom_clinvar = "${params.ref_dir}/vep/${params.ref_ver}/clinvar_20220730.vcf.gz" + params.vep_custom_hgmd = "${params.ref_dir}/vep/${params.ref_ver}/HGMD_Pro_2022.2_${params.ref_ver}.vcf.gz" + params.vep_plugin_revel = "${params.ref_dir}/vep/${params.ref_ver}/new_tabbed_revel_${params.ref_assembly}.tsv.gz" // changed for hg19 + params.vep_plugin_spliceai_snv = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.snv.${params.ref_ver}.vcf.gz" + params.vep_plugin_spliceai_indel = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.indel.${params.ref_ver}.vcf.gz" + params.vep_plugin_cadd = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_cadd_name}" // changed for hg19 + params.vep_plugin_dbnsfp = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_dbnsfp_name}" + params.vep_idx = "${params.ref_dir}/vep/${params.ref_ver}/*.tbi" + + params.ref_annot_dir = "${params.ref_dir}/annotate" + params.ref_var_tier_dir = "${params.ref_dir}/var_tier" + params.ref_merge_expand_dir = "${params.ref_dir}/merge_expand" + params.ref_mod5_diffusion_dir = "${params.ref_dir}/mod5_diffusion" + params.ref_predict_new_dir = "${params.ref_dir}/predict_new" + params.ref_model_inputs_dir = "${params.ref_dir}/model_inputs" + + // Documentation + params.usage_file = "${projectDir}/docs/source/nf_usage.txt" + params.script_chunking = "${projectDir}/scripts/split_chunks.py" + params.script_annot = "${projectDir}/scripts/annotation/*.py" +} diff --git a/nextflow.config b/nextflow.config index b105943..d932757 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,13 +1,13 @@ params { input_vcf = null input_variant = null + input_ped = null run_id = 1 ref_ver = "hg19" ref_dir = null bed_filter = null exome_filter = false - mode = "singleton" help = false version = false @@ -16,7 +16,7 @@ params { docker.enabled = true process { - cpus = 2 + cpus = 2 memory = { 25.GB * task.attempt } errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } maxRetries = 5 diff --git a/resources/trio_pipeline/acceptable_header.txt b/resources/trio_pipeline/acceptable_header.txt new file mode 100644 index 0000000..ce32661 --- /dev/null +++ b/resources/trio_pipeline/acceptable_header.txt @@ -0,0 +1,198 @@ +##fileformat=VCFv4.2 +##FORMAT= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +##contig= +#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT PATIENT diff --git a/resources/trio_pipeline/features.csv b/resources/trio_pipeline/features.csv new file mode 100755 index 0000000..8ba592e --- /dev/null +++ b/resources/trio_pipeline/features.csv @@ -0,0 +1 @@ +diffuse_Phrank_STRING,hgmdSymptomScore,omimSymMatchFlag,hgmdSymMatchFlag,clinVarSymMatchFlag,omimGeneFound,omimVarFound,hgmdGeneFound,hgmdVarFound,clinVarVarFound,clinVarGeneFound,clinvarNumP,clinvarNumLP,clinvarNumLB,clinvarNumB,dgvVarFound,decipherVarFound,curationScoreHGMD,curationScoreOMIM,curationScoreClinVar,conservationScoreDGV,omimSymptomSimScore,hgmdSymptomSimScore,GERPpp_RS,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,phyloP100way_vertebrate,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELof,gnomadGeneOELofUpper,IMPACT,CADD_phred,CADD_PHRED,DANN_score,REVEL_score,fathmm_MKL_coding_score,conservationScoreGnomad,conservationScoreOELof,Polyphen2_HDIV_score,Polyphen2_HVAR_score,SIFT_score,zyg,FATHMM_score,M_CAP_score,MutationAssessor_score,ESP6500_AA_AF,ESP6500_EA_AF,hom,hgmd_rs,spliceAImax,nc_ClinVar_Exp,nc_HGMD_Exp,nc_isPLP,nc_isBLB,c_isPLP,c_isBLB,nc_CLNREVSTAT,c_CLNREVSTAT,nc_RANKSCORE,c_RANKSCORE,CLASS,phrank,isB/LB,isP/LP,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,c_ClinVar_Exp_Del_to_Missense,c_ClinVar_Exp_Different_pChange,c_ClinVar_Exp_Same_pChange,c_HGMD_Exp_Del_to_Missense,c_HGMD_Exp_Different_pChange,c_HGMD_Exp_Same_pChange,c_HGMD_Exp_Stop_Loss,c_HGMD_Exp_Start_Loss,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,AD.matched,AR.matched,recessive,dominant,simple_repeat,Homozygous_Recessive_Risk,De_Novo_Risk,X_Linked_Risk,Compound_heterozygous_Risk,Inherited_dominant_Risk diff --git a/resources/trio_pipeline/features_NDG.csv b/resources/trio_pipeline/features_NDG.csv new file mode 100644 index 0000000..b8731d5 --- /dev/null +++ b/resources/trio_pipeline/features_NDG.csv @@ -0,0 +1 @@ +conservationScoreDGV,gnomadAF,gnomadAFg,LRT_score,LRT_Omega,gnomadGeneZscore,gnomadGenePLI,gnomadGeneOELofUpper,IMPACT,CADD_PHRED,conservationScoreGnomad,conservationScoreOELof,zyg,ESP6500_AA_AF,ESP6500_EA_AF,hom,spliceAImax,cons_transcript_ablation,cons_splice_acceptor_variant,cons_splice_donor_variant,cons_stop_gained,cons_frameshift_variant,cons_stop_lost,cons_start_lost,cons_transcript_amplification,cons_inframe_insertion,cons_inframe_deletion,cons_missense_variant,cons_protein_altering_variant,cons_splice_region_variant,cons_splice_donor_5th_base_variant,cons_splice_donor_region_variant,IMPACT.from.Tier,TierAD,TierAR,TierAR.adj,No.Var.HM,No.Var.H,No.Var.M,No.Var.L,simple_repeat,Homozygous_Recessive_Risk,De_Novo_Risk,X_Linked_Risk,Compound_heterozygous_Risk,Inherited_dominant_Risk diff --git a/resources/trio_pipeline/rf_trio_NDG.job b/resources/trio_pipeline/rf_trio_NDG.job new file mode 100644 index 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Date: Fri, 4 Apr 2025 11:48:26 -0500 Subject: [PATCH 25/80] trim trailing spaces --- modules/local/singleton/main.nf | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 0d2d8bf..0491d0e 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -190,7 +190,7 @@ process FILTER_UNPASSED { # Annotate with new IDs based on CHROM, POS, REF, ALT bcftools annotate --set-id +'%CHROM\\_%POS\\_%REF\\_%FIRST_ALT' ${params.run_id}-annot -Oz -o ${params.run_id}-add-id.vcf.gz - #run quality filters + #run quality filters bcftools filter ${params.run_id}-add-id.vcf.gz -i'FILTER == "PASS"' -Oz -o ${params.run_id}.filt.vcf.gz #check number of variants left @@ -363,7 +363,7 @@ process FILTER_PROBAND { process SPLIT_VCF_BY_CHROMOSOME { input: - path vcf + path vcf output: path "chr*.vcf.gz", emit: chr_vcfs @@ -447,7 +447,7 @@ process ANNOTATE_BY_MODULES { -genomeRef ${params.ref_ver} \\ -diseaseInh AD \\ -modules curate,conserve - + mv scores.csv ${vep.baseName}_scores.csv """ } @@ -533,12 +533,12 @@ process MERGE_SCORES_BY_CHROMOSOME { process PREDICTION { publishDir "${params.outdir}/${params.run_id}/prediction/", mode: "copy" - + input: - path merged_matrix - path merged_compressed_scores + path merged_matrix + path merged_compressed_scores path ref_model_inputs_dir - + output: path "conf_4Model/${params.run_id}_default_predictions.csv", emit: "default_predictions" path "conf_4Model/${params.run_id}_recessive_predictions.csv" @@ -547,12 +547,12 @@ process PREDICTION { path "conf_4Model/integrated/*.csv" path "final_matrix_expanded/*.csv.gz" path "shap_outputs" - + script: """ mkdir final_matrix_expanded mkdir conf_4Model - + run_final.py \ $ref_model_inputs_dir/default/final_model.job \ $ref_model_inputs_dir/default/features.csv \ From 9c740f9e652735f0dc99889851cb229b5d740230 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 12:23:22 -0500 Subject: [PATCH 26/80] Use the word of prediction for run_final result --- bin/extraModel/generate_bivar_data.py | 2 +- bin/extraModel_main.py | 2 +- bin/trio_merge_rm.py | 2 +- modules/local/singleton/main.nf | 4 ++-- modules/local/trio/main.nf | 6 +++--- 5 files changed, 8 insertions(+), 8 deletions(-) diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index fa2b33c..2df4e4f 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -21,7 +21,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): os.mkdir(tmp_folder) # read feature matrix for single var - feature_fn = f"{sample_id}.feature.csv" + feature_fn = f"{sample_id}.default_prediction.csv" # feature_df = [] # for feature_fn in feature_fns: if "csv" in feature_fn: diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index 5e2c7a0..76dde9f 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -83,7 +83,7 @@ def assign_ranking(df): def AIM(data_folder, sample_id): - feature_fn = f"{sample_id}.feature.csv" + feature_fn = f"{sample_id}.default_prediction.csv" if not os.path.exists(feature_fn): print(f"{feature_fn} does not exist.") diff --git a/bin/trio_merge_rm.py b/bin/trio_merge_rm.py index cfb1fc5..640ae7c 100755 --- a/bin/trio_merge_rm.py +++ b/bin/trio_merge_rm.py @@ -12,4 +12,4 @@ ndg.columns = ["Unnamed: 0","predict (nd)","ranking (nd)"] feature = feature.merge(ndg, on="Unnamed: 0", how="left") -feature.to_csv("./"+sys.argv[1]+".trio.feature.csv",index=False) \ No newline at end of file +feature.to_csv("./"+sys.argv[1]+".trio.prediction.csv",index=False) \ No newline at end of file diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 0491d0e..7522d2e 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -557,11 +557,11 @@ process PREDICTION { $ref_model_inputs_dir/default/final_model.job \ $ref_model_inputs_dir/default/features.csv \ $merged_matrix \ - ${params.run_id}.feature.csv + ${params.run_id}.default_prediction.csv # Generate final matrix expanded merge_rm.py \ - ${params.run_id}.feature.csv \ + ${params.run_id}.default_prediction.csv \ $merged_compressed_scores \ final_matrix_expanded/${params.run_id}.expanded.csv.gz diff --git a/modules/local/trio/main.nf b/modules/local/trio/main.nf index f2174b5..c20211b 100644 --- a/modules/local/trio/main.nf +++ b/modules/local/trio/main.nf @@ -138,7 +138,7 @@ process PREDICTION_TRIO { output: path "*.trio.expanded.csv.gz" - path "*.trio.feature.csv" + path "*.trio.prediction.csv" script: """ @@ -154,11 +154,11 @@ process PREDICTION_TRIO { ./${params.run_id}.trio.NDG.csv - # Generate ${params.run_id}.trio.feature.csv + # Generate ${params.run_id}.trio.prediction.csv trio_merge_rm.py ${params.run_id} merge_rm.py \ - ${params.run_id}.trio.feature.csv \ + ${params.run_id}.trio.prediction.csv \ $merged_compressed_scores \ ${params.run_id}.trio.expanded.csv.gz """ From 79211f883ce695dd3dc307a12471fe440337644e Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Fri, 4 Apr 2025 13:27:40 -0500 Subject: [PATCH 27/80] Create oldpython.Dockerfile to run old sklearn models --- Dockerfiles/oldpython.Dockerfile | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 Dockerfiles/oldpython.Dockerfile diff --git a/Dockerfiles/oldpython.Dockerfile b/Dockerfiles/oldpython.Dockerfile new file mode 100644 index 0000000..3b9abd6 --- /dev/null +++ b/Dockerfiles/oldpython.Dockerfile @@ -0,0 +1,7 @@ +FROM python:3.8 + +RUN pip install scikit-learn==1.1.2 +RUN pip install pandas +RUN pip install scipy +RUN pip install xgboost + From 8f860877addd454e827580e40c494d35ab92d7fb Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 14:03:03 -0500 Subject: [PATCH 28/80] use oldpython docker container for old models --- bin/predict_new/utilities.py | 337 ----------------------------------- modules/local/trio/main.nf | 2 +- 2 files changed, 1 insertion(+), 338 deletions(-) diff --git a/bin/predict_new/utilities.py b/bin/predict_new/utilities.py index 61a453f..b75ab26 100755 --- a/bin/predict_new/utilities.py +++ b/bin/predict_new/utilities.py @@ -1,298 +1,7 @@ -from math import ceil -import os import pandas as pd -import numpy as np from scipy.stats import rankdata -import sys -import xgboost as xgb -from tqdm import trange -from time import time -import json -from matplotlib import pyplot as plt -from sklearn.ensemble import RandomForestClassifier as RF -import itertools from predict_new.confidence import * - -def get_training_data(fns, label_col): - """Read in training data, drop features with specified mode in the meta['feature_mode'] json. - Allowed modes are ['all', 'raw', 'var_disase_db_off', 'gene_disase_db_off'] - """ - np.random.seed(0) - ### READ PARAMETERS - f = open("meta.json") - meta = json.load(f) - feature_mode = meta["feature_mode"] - feature_meta = pd.read_csv(meta["feature_meta_fn"], sep=",") - ### READ training samples - t0 = time() - print("[TrainVal-Train] Start reading training data.") - dfs = [] - for i in trange(len(fns)): - fn = fns[i] - df = pd.read_csv(fn, index_col=0, sep="\t") - dfs.append(df) - dfs = pd.concat(dfs) - - t1 = time() - print(f"[TrainVal-Train] Completed reading training data in {(t1-t0):.2f} seconds.") - train_Y = dfs[label_col].to_numpy().astype(int) - neg_pos_ratios = [(train_Y == 0).sum() / (train_Y == 1).sum()] - - ## Drop features based on feature mode - if feature_mode == "all": - features = feature_meta.Feature.tolist() - elif feature_mode == "raw": - features = feature_meta.loc[feature_meta.Engineered != 1, :].Feature.tolist() - elif feature_mode == "var_disase_db_off": - features = feature_meta.loc[ - feature_meta.DiseaseDB_VariantLevel != 1, : - ].Feature.tolist() - elif feature_mode == "gene_disase_db_off": - features = feature_meta.loc[ - feature_meta.DiseaseDB_GeneLevel != 1, : - ].Feature.tolist() - else: - print( - f"Wrong feature mode entered, should be one of\ - ['all', 'raw', 'var_disease_db_off', 'gene_disease_db_off'],\ - your input is {feature_mode}." - ) - sys.exit(0) - train_X = dfs[features] - print("[TrainVal-Train] Training data obtained.") - return train_X, train_Y, np.mean(neg_pos_ratios) - - -def tune_params(fns, classifier="xgb"): - ### READ PARAMETERS - f = open("meta.json") - meta = json.load(f) - train_label = meta["train_label"] - test_label = meta["test_label"] - train_val_frac = meta["train_val_frac"] - sep2 = "\t" - - train_fns_sampled = list(np.random.choice(fns, int(train_val_frac * len(fns)))) - val_fns_sampled = list(set(fns) - set(train_fns_sampled)) - - print( - f"[TrainVal-Train] train {len(train_fns_sampled)} samples with label {train_label},\ - validate {len(val_fns_sampled)} samples with label {test_label}." - ) - - para_train_dfs = [ - pd.read_csv(train_fn, usecols=[train_label], sep=sep2) - for train_fn in train_fns_sampled - ] - para_train_dfs = pd.concat(para_train_dfs) - if classifier == "xgb": - ## tune parameters - n_estimators = [200, 300] - learning_rates = list(np.logspace(0.001, 0.2, num=10, endpoint=True)) - max_depths = [None] - params = list(itertools.product(n_estimators, learning_rates, max_depths)) - elif classifier == "rf": - n_estimators = [100, 200, 300, 400] - max_depths = [None] - params = list(itertools.product(n_estimators, max_depths)) - - train_X, train_Y, neg_pos_ratio = get_training_data(train_fns_sampled, train_label) - perf_dfs = [] - for param in params: - t0 = time() - if classifier == "xgb": - n_est, lr, md = param - print( - f"[TrainVal-Train] n_trees: {n_est}, lr: {lr}, max_depth: {md}, n/p ratio: {neg_pos_ratio}." - ) - clf = train_XGBoost(train_X, train_Y, n_est, lr, md, neg_pos_ratio) - - elif classifier == "rf": - n_est, md = param - print( - f"[TrainVal-Train] n_trees: {n_est}, max_depth: {md}, n/p ratio: {neg_pos_ratio}." - ) - clf, _, _ = train_minibatch_RF(train_X, train_Y, n_est, md, neg_pos_ratio) - t1 = time() - print(f"[TrainVal-Train] done in {(t1-t0):.2f} seconds.") - rank_dfs = rank_test_patients( - clf, val_fns_sampled, train_X.columns.tolist(), test_label - ) - obj_df = objective(rank_dfs) - if classifier == "xgb": - perf_df = pd.DataFrame( - data=[ - [ - n_est, - md, - lr, - obj_df["objective"], - obj_df["var_top1_acc"], - obj_df["var_top5_acc"], - obj_df["var_top10_acc"], - ] - ], - columns=[ - "n_estimator", - "max_depth", - "learning_rate", - "objective", - "var_top1", - "var_top5", - "var_top10", - ], - ) - else: - perf_df = pd.DataFrame( - data=[ - [ - n_est, - md, - obj_df["objective"], - obj_df["var_top1_acc"], - obj_df["var_top5_acc"], - obj_df["var_top10_acc"], - ] - ], - columns=[ - "n_estimator", - "max_depth", - "objective", - "var_top1", - "var_top5", - "var_top10", - ], - ) - perf_dfs.append(perf_df) - t2 = time() - print( - f"[TrainVal-Validate] done in {(t2-t1):.2f} seconds, objective = {obj_df['objective']}." - ) - perf_dfs = pd.concat(perf_dfs).sort_values( - ["objective", "var_top1", "var_top5", "var_top10"], ascending=False - ) - return perf_dfs - - -def quantify_perf(test_perfs, topK=5, mode="variant_level"): - if mode == "sample_level": - sample_perfs = test_perfs.groupby("identifier")[["ranking"]].min() - else: - sample_perfs = test_perfs.copy() - - topK_acc = (sample_perfs["ranking"] <= topK).sum() / sample_perfs.shape[0] - return topK_acc - - -def plot_topK_performance(rank_dfs): - f = plt.figure(figsize=(10, 4)) - topks = range(1, 101) - var_accs = [] - sample_accs = [] - - for i in topks: - var_accs.append(quantify_perf(rank_dfs, topK=i)) - sample_accs.append(quantify_perf(rank_dfs, topK=i, mode="sample_level")) - df = pd.DataFrame( - {"top_k": topks, "var_accuracy": var_accs, "sample_accuracy": sample_accs} - ) - ax1 = plt.subplot(1, 2, 1) - ax1.plot(topks, var_accs, linestyle="--", marker="o", color="blue") - ax1.set_xlabel("Top K", fontsize=14) - ax1.set_ylabel("Variant Accuracy", fontsize=14) - ax2 = plt.subplot(1, 2, 2) - ax2.plot(topks, sample_accs, linestyle="--", marker="o", color="green") - ax2.set_xlabel("Top K", fontsize=14) - ax2.set_ylabel("Sample Accuracy", fontsize=14) - plt.subplots_adjust(wspace=0.3) - return f, df - - -def objective(rank_dfs): - ### READ PARAMETERS - f = open("meta.json") - meta = json.load(f) - alpha = meta["objective_params"]["alpha"] - beta = meta["objective_params"]["beta"] - gamma = meta["objective_params"]["gamma"] - f.close() - ### Get accuracy and calculate objective - vl1 = quantify_perf(rank_dfs, topK=1) - vl5 = quantify_perf(rank_dfs, topK=5) - vl10 = quantify_perf(rank_dfs, topK=10) - sl1 = quantify_perf(rank_dfs, topK=1, mode="sample_level") - sl5 = quantify_perf(rank_dfs, topK=5, mode="sample_level") - sl10 = quantify_perf(rank_dfs, topK=10, mode="sample_level") - obj_score = alpha * vl1 + beta * (vl5 - vl1) + gamma * (vl10 - vl5) - result = { - "var_top1_acc": vl1, - "var_top5_acc": vl5, - "var_top10_acc": vl10, - "sample_top1_acc": sl1, - "sample_top5_acc": sl5, - "sample_top10_acc": sl10, - "objective": obj_score, - } - return result - - -def train_XGBoost(X, Y, n_estimator, lr, max_depth, ratio): - xgb.config_context(verbosity=0) - bst = xgb.XGBClassifier( - max_depth=max_depth, - n_estimators=n_estimator, - learning_rate=lr, - scale_pos_weight=ratio, - verbosity=0, - n_jobs=-1, - ) - bst.fit(X, Y) - return bst - - -def train_RF(X, Y, n_estimator=None, max_depth=None, ratio=None, model=None): - if model is None: - model = RF( - class_weight={0: 1, 1: ratio}, - random_state=2021, - n_jobs=-1, - n_estimators=n_estimator, - max_depth=max_depth, - warm_start=True, - ) - model.fit(X, Y) - return model - - -def train_minibatch_RF(fns, n_estimator, max_depth, label_col, batch=500): - k = ceil(len(fns) / batch) - model = RF( - random_state=2021, - n_jobs=-1, - n_estimators=n_estimator, - max_depth=max_depth, - warm_start=True, - ) - train_causals = [] - for i in range(k): - if i < k - 1: - batch_fns = fns[i * batch : (i + 1) * batch] - else: - batch_fns = fns[i * batch :] - trainX, trainY, neg_pos_ratio = get_training_data( - batch_fns, label_col=label_col - ) - features = trainX.columns.tolist() - model.set_params( - **{"class_weight": {0: 1, 1: neg_pos_ratio}, "n_estimators": n_estimator} - ) - model.fit(trainX, trainY) - n_estimator += 10 - train_causals += trainX.index[np.where(trainY == 1)[0]].tolist() - return model, features, train_causals - - def rank_patient(model, fn, features, train_causals=None): df = pd.read_csv( fn, sep="\t", index_col=0 @@ -315,49 +24,3 @@ def rank_patient(model, fn, features, train_causals=None): pred_df["ranking"] = max_rankings pred_df["identifier"] = identifier return pred_df - - -def rank_test_patients( - model, - fns, - features, - label_col, - causal_distr=None, - out_folder=None, - train_causals=None, -): - print(out_folder) - causal_rank_dfs = [] - for i in trange(len(fns)): - fn = fns[i] - - if not os.path.exists(fn): - continue - - identifier = fn.split("/")[-1].split("_")[0] - rank_df = rank_patient(model, fn, features, train_causals) - - if causal_distr is not None: - rank_df = assign_confidence_score( - causal_distr, rank_df, label_col="is_strong" - ) - - if out_folder is not None: - if not os.path.exists(out_folder): - os.mkdir(out_folder) - - rank_df.to_csv(f"{out_folder}/{identifier}_variant_rank.csv") - - causal_rank = get_causal_rank(rank_df, label_col) - - if causal_rank is not None: - causal_rank_dfs.append(causal_rank) - - causal_rank_dfs = pd.concat(causal_rank_dfs) - return causal_rank_dfs - - -def get_causal_rank(pred_df, label_col): - if (pred_df[label_col] == 1).sum() == 0: - return None - return pred_df.loc[pred_df[label_col] == 1, :] diff --git a/modules/local/trio/main.nf b/modules/local/trio/main.nf index c20211b..0991b3f 100644 --- a/modules/local/trio/main.nf +++ b/modules/local/trio/main.nf @@ -129,7 +129,7 @@ process GENERATE_TRIO_FEATURES { } process PREDICTION_TRIO { - container "zhandongliulab/aim-lite:1.0" + container "zhandongliulab/aim-lite-oldpython" publishDir "${params.outdir}/${params.run_id}/prediction_trio", mode: 'copy' input: From 25a95514e4cb90d907427335d71022316fa4570f Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Fri, 4 Apr 2025 14:04:15 -0500 Subject: [PATCH 29/80] Use slimmer dockerfile for oldpython --- Dockerfiles/oldpython.Dockerfile | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/Dockerfiles/oldpython.Dockerfile b/Dockerfiles/oldpython.Dockerfile index 3b9abd6..1f2a02f 100644 --- a/Dockerfiles/oldpython.Dockerfile +++ b/Dockerfiles/oldpython.Dockerfile @@ -1,7 +1,5 @@ -FROM python:3.8 +FROM python:3.8-slim RUN pip install scikit-learn==1.1.2 RUN pip install pandas -RUN pip install scipy -RUN pip install xgboost From 8cc67129ec80bf863acb87b3718adee341a044e6 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 14:43:04 -0500 Subject: [PATCH 30/80] Build reference index on the top main --- main.nf | 8 +++++++- subworkflows/local/singleton/main.nf | 10 ++++------ 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/main.nf b/main.nf index 0431901..2b41986 100644 --- a/main.nf +++ b/main.nf @@ -35,7 +35,13 @@ workflow { ) vcf = VCF_PRE_PROCESS_TRIO.out.vcf } else if (params.input_vcf) { - VCF_PRE_PROCESS(file(params.input_vcf)) + BUILD_REFERENCE_INDEX() + VCF_PRE_PROCESS( + file(params.input_vcf), + BUILD_REFERENCE_INDEX.out.fasta, + BUILD_REFERENCE_INDEX.out.fasta_index, + BUILD_REFERENCE_INDEX.out.fasta_dict, + ) vcf = VCF_PRE_PROCESS.out.vcf } else if (params.input_variant) { GENERATE_INPUT_VCF(params.input_variant) diff --git a/subworkflows/local/singleton/main.nf b/subworkflows/local/singleton/main.nf index 754754a..9d704bf 100644 --- a/subworkflows/local/singleton/main.nf +++ b/subworkflows/local/singleton/main.nf @@ -3,7 +3,7 @@ include { } from "../../../modules/local/utils" include { - VALIDATE_VCF; NORMALIZE_VCF; BUILD_REFERENCE_INDEX; GENERATE_INPUT_VCF; + VALIDATE_VCF; NORMALIZE_VCF; GENERATE_INPUT_VCF; VCF_TO_VARIANTS; VARIANTS_TO_ENSEMBL; ENSEMBL_TO_GENESYM; GENESYM_TO_PHRANK; CONVERT_GVCF; FILTER_UNPASSED; FILTER_MITO_AND_UNKOWN_CHR; FILTER_BED; FILTER_PROBAND; SPLIT_VCF_BY_CHROMOSOME; ANNOTATE_BY_VEP; HPO_SIM; ANNOTATE_BY_MODULES; @@ -16,19 +16,17 @@ addDependentParams(params) workflow VCF_PRE_PROCESS { take: input_vcf + fasta + fasta_index + fasta_dict main: - BUILD_REFERENCE_INDEX() - VALIDATE_VCF(input_vcf) NORMALIZE_VCF(VALIDATE_VCF.out.vcf) CONVERT_GVCF( NORMALIZE_VCF.out.vcf, NORMALIZE_VCF.out.tbi, - BUILD_REFERENCE_INDEX.out.fasta, - BUILD_REFERENCE_INDEX.out.fasta_index, - BUILD_REFERENCE_INDEX.out.fasta_dict, params.chrmap ) FILTER_UNPASSED( From 1a58dc31f2769817c825609b3405cbc8980f1198 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Fri, 4 Apr 2025 15:23:40 -0500 Subject: [PATCH 31/80] use non slim version of oldpython to support ps for nextflow --- Dockerfiles/oldpython.Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfiles/oldpython.Dockerfile b/Dockerfiles/oldpython.Dockerfile index 1f2a02f..845b477 100644 --- a/Dockerfiles/oldpython.Dockerfile +++ b/Dockerfiles/oldpython.Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.8-slim +FROM python:3.8 RUN pip install scikit-learn==1.1.2 RUN pip install pandas From d7e7d444de83bc5ef260c6b657a6e2919dac1f4f Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 15:17:08 -0500 Subject: [PATCH 32/80] Add profiles and manifest Co-authored-by: ZaahidShaik --- nextflow.config | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/nextflow.config b/nextflow.config index d932757..420c7cd 100644 --- a/nextflow.config +++ b/nextflow.config @@ -25,3 +25,27 @@ process { cache = "lenient" } +profiles { + docker { + process.beforeScript = 'echo "Running Docker Profile."' + singularity.enabled = false + docker.enabled = true + } + singularity { + process.beforeScript = 'echo "Running Singularity Profile."' + docker.enabled = false + singularity.enabled = true + } +} + +manifest { + author = 'LiuzLab' + name = 'AI_MARRVEL' + version = '1.1.0' + + description = """AI-MARRVEL (AIM) is an AI system for rare genetic disease diagnosis.""" + homePage = 'https://github.com/LiuzLab/AI_MARRVEL' + defaultBranch = 'main' + nextflowVersion = '>=23.10.1' + doi = '10.1056/AIoa2300009' +} From d55c357dd0c25ba8a34b0e4c64882beb7e18f2bf Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 15:26:41 -0500 Subject: [PATCH 33/80] Add debug profile Co-authored-by: ZaahidShaik --- .gitignore | 5 +++-- nextflow.config | 5 +++++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index c8f5e51..c097d1d 100644 --- a/.gitignore +++ b/.gitignore @@ -16,8 +16,9 @@ test_output/ .nextflow/ work/ out/ -trace-*txt -report-*html +trace-*.txt +report-*.html +timeline-*.html params.yaml # Ignore R history and session files diff --git a/nextflow.config b/nextflow.config index 420c7cd..2602c55 100644 --- a/nextflow.config +++ b/nextflow.config @@ -26,6 +26,11 @@ process { } profiles { + debug { + timeline.enabled = true + trace.enabled = true + report.enabled = true + } docker { process.beforeScript = 'echo "Running Docker Profile."' singularity.enabled = false From c3fa65ad5cc6960e51706d7d018d9f1b080cb175 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 15:31:11 -0500 Subject: [PATCH 34/80] fix minor Co-authored-by: ZaahidShaik --- nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nextflow.config b/nextflow.config index 2602c55..da8d503 100644 --- a/nextflow.config +++ b/nextflow.config @@ -45,7 +45,7 @@ profiles { manifest { author = 'LiuzLab' - name = 'AI_MARRVEL' + name = 'LiuzLab/AI_MARRVEL' version = '1.1.0' description = """AI-MARRVEL (AIM) is an AI system for rare genetic disease diagnosis.""" From 3486837cc6b5eadc9a3339dfb1c5c2bd5a8e2245 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 16:13:45 -0500 Subject: [PATCH 35/80] Use nf core schema for validation and help message Co-authored-by: ZaahidShaik --- docs/source/nf_usage.txt | 44 -------------------- main.nf | 7 ++-- modules/local/utils/main.nf | 66 +----------------------------- nextflow.config | 25 +++++++++++- nextflow_schema.json | 81 +++++++++++++++++++++++++++++++++++++ 5 files changed, 110 insertions(+), 113 deletions(-) delete mode 100644 docs/source/nf_usage.txt create mode 100644 nextflow_schema.json diff --git a/docs/source/nf_usage.txt b/docs/source/nf_usage.txt deleted file mode 100644 index 11fdd64..0000000 --- a/docs/source/nf_usage.txt +++ /dev/null @@ -1,44 +0,0 @@ -Usage: - - nextflow run main.nf [All Args] [Metadata Flags] - -Example: - - nextflow run main.nf --ref_dir path/to/aim_data_dependencies \ - --input_vcf path/to/sample.vcf.gz \ - --input_hpo path/to/sample/hpos.txt \ - --outdir path/to/sample/Output \ - --run_id [Sample ID] \ - --ref_ver [hg19/hg38] - -Args: - --input_vcf Path to input VCF file. - --input_hpo Path to input HPO file. - --ref_dir Path to aim pipeline dependencies directory. - --outdir Output directory. - --run_id Unique identifier for this run. (default: 1) - --ref_ver Reference genome version [hg38 or hg19] (default: hg19) - --input_variant Variant ID Formatted in 'chr-pos-alt-ref' (optional) - --bed_filter Path to a BED file to perform an analysis only for regions of interest (optional) - --exome_filter Enable exonic filter. Addition will filter out variants outside of exonic region (default: false) - --input_ped Path to the pedgree file for trio mode - -Metadata Flags: - --help Display the usage information. - --version Display the version of the AI Marrvel pipeline. - -Reference Files: - --ref_loc Path to reference location file - --ref_to_sym Path to reference to symbol file - --ref_sorted_sym Path to reference sorted symbol file - --ref_exonic_filter_bed Path to exonic filter BED file - -VEP Annotation: - --vep_dir_cache Path to VEP cache directory - --vep_dir_plugins Path to VEP plugins directory - --vep_custom_gnomad Path to custom gnomAD file for VEP - --vep_custom_clinvar Path to custom ClinVar file for VEP - --vep_custom_hgmd Path to custom HGMD file for VEP - -For detailed information about each process, -please refer to the documentation. https://ai-marrvel.readthedocs.io/en/latest/ diff --git a/main.nf b/main.nf index 2b41986..5ffd63e 100644 --- a/main.nf +++ b/main.nf @@ -1,7 +1,9 @@ nextflow.enable.dsl = 2 +include { validateParameters } from 'plugin/nf-schema' + include { - showUsage; showVersion; validateInputParams; addDependentParams + showVersion; addDependentParams } from "./modules/local/utils" include { @@ -16,9 +18,8 @@ include { VCF_PRE_PROCESS; GENERATE_SINGLETON_FEATURES; PREDICTION } from "./subworkflows/local/singleton" -showUsage() showVersion() -validateInputParams() +validateParameters() addDependentParams(params) workflow { diff --git a/modules/local/utils/main.nf b/modules/local/utils/main.nf index 97d1143..c4d2a67 100644 --- a/modules/local/utils/main.nf +++ b/modules/local/utils/main.nf @@ -1,76 +1,12 @@ - -def showUsage() { - if (params.help) { - def helpFile = file(params.usage_file) // Specify your Markdown file path here - if (helpFile.exists()) { - println helpFile.text - } else { - println """ - Sorry something went wrong, usage file not found! - Please vist our website for more info : https://ai-marrvel.readthedocs.io/en/latest/ - """ - } - exit 0 - } -} - def showVersion() { if (!params.version) { return } - println "1.1.0" + println workflow.manifest.version exit 0 } -def validateInputParams() { - def checkPathParamMap = [ - "input_hpo": params.input_hpo, - "ref_dir" : params.ref_dir, - "ref_ver" : params.ref_ver, - "outdir" : params.outdir, - ] - - checkPathParamMap.each { paramName, paramValue -> - if (paramValue) - return - - println("Input parameter '${paramName}' not specified or is null!") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (params.input_variant && params.input_vcf) { - println("Error: Cannot use '--input_variant' with --input_vcf'") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (params.input_vcf && !params.input_vcf.endsWith(".vcf") && !params.input_vcf.endsWith(".vcf.gz")) { - println("Error: '--input_vcf' value '${params.input_vcf}' should be a VCF file (.vcf) or (.vcf.gz)") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (params.input_variant && params.input_variant.count('-') != 3) { - println("Error: '--input_variant' should be formated like 'X-47038564-T-C'") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (!file(params.ref_dir).isDirectory()) { - println("Error: '--ref_dir' should be an directory.") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } - - if (!params.ref_ver.equals("hg19") && !params.ref_ver.equals("hg38") ) { - println("Error: '--ref_ver' value ${params.ref_ver} should be either set to 'hg19' or 'hg38'.") - println("To see usage and available parameters run `nextflow run main.nf --help`") - exit 1 - } -} - def addDependentParams(params) { params.ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" diff --git a/nextflow.config b/nextflow.config index da8d503..6acc85d 100644 --- a/nextflow.config +++ b/nextflow.config @@ -2,8 +2,10 @@ params { input_vcf = null input_variant = null input_ped = null + input_hpo = null + outdir = "./out" - run_id = 1 + run_id = "run1" ref_ver = "hg19" ref_dir = null bed_filter = null @@ -54,3 +56,24 @@ manifest { nextflowVersion = '>=23.10.1' doi = '10.1056/AIoa2300009' } + +plugins { + id 'nf-schema@2.3.0' +} +validation { + help { + enabled = true + command = """ + nextflow run /path/to/AI_MARRVEL/main.nf \\ + --ref_dir /path/to/dependencies/ \\ + --input_vcf /path/to/input.vcf \\ + --input_hpo /path/to/input.hpos.txt \\ + --outdir /path/to/output \\ + --run_id \\ + --ref_ver \\ + -profile + """ + fullParameter = "help_full" + showHiddenParameter = "show_hidden" + } +} diff --git a/nextflow_schema.json b/nextflow_schema.json new file mode 100644 index 0000000..a5f5c7b --- /dev/null +++ b/nextflow_schema.json @@ -0,0 +1,81 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://raw.githubusercontent.com/LiuzLab/AI_MARRVEL//nextflow_schema.json", + "title": "LiuzLab/AI_MARRVEL pipeline parameters", + "description": "AI-MARRVEL (AIM) is an AI system for rare genetic disease diagnosis.", + "type": "object", + "$defs": { + "generic_options": { + "title": "Generic options", + "type": "object", + "fa_icon": "fas fa-file-import", + "description": "Less common options for the pipeline, typically set in a config file.", + "help_text": "These options are common to all nf-core pipelines and allow you to customise some of the core preferences for how the pipeline runs.\n\nTypically these options would be set in a Nextflow config file loaded for all pipeline runs, such as `~/.nextflow/config`.", + "properties": { + "version": { + "type": "boolean", + "description": "Display version and exit.", + "fa_icon": "fas fa-question-circle", + "hidden": true + } + } + } + }, + "allOf": [ + { + "$ref": "#/$defs/generic_options" + } + ], + "properties": { + "input_vcf": { + "type": "string", + "description": "Path to input VCF file.", + "pattern": "^\\S+\\.vcf(.gz)?$", + "format": "file-path" + }, + "input_variant": { + "type": "string", + "pattern": "^(\\S+)-(\\S+)-(\\S+)-(\\S+)$" + }, + "input_ped": { + "type": "string", + "description": "Path to the pedgree file for trio mode", + "format": "file-path" + }, + "input_hpo": { + "type": "string", + "description": "Path to input HPO file.", + "format": "file-path" + }, + "outdir": { + "type": "string", + "default": "./out", + "description": "Output directory." + }, + "run_id": { + "type": "string", + "default": "run1", + "description": "Unique identifier for this run." + }, + "ref_ver": { + "type": "string", + "default": "hg19", + "enum": ["hg19", "hg38"], + "description": "Reference genome version" + }, + "ref_dir": { + "type": "string", + "description": "Path to aim pipeline dependencies directory.", + "format": "directory-path" + }, + "bed_filter": { + "type": "string", + "description": "Path to a BED file to perform an analysis only for regions of interest" + }, + "exome_filter": { + "type": "boolean", + "description": "Enable exonic filter. Addition will filter out variants outside of exonic region" + } + }, + "required": ["outdir", "run_id", "ref_ver", "ref_dir"] +} From da12f2b89a34010153fa02264bbaac47c36d6e0d Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 16:46:08 -0500 Subject: [PATCH 36/80] Minimum setting for nf-core pipelines lint Co-authored-by: ZaahidShaik --- .nf-core.yml | 61 ++++++++++++++++++++++++++++++++++++++++++++++++++++ modules.json | 5 +++++ 2 files changed, 66 insertions(+) create mode 100644 .nf-core.yml create mode 100644 modules.json diff --git a/.nf-core.yml b/.nf-core.yml new file mode 100644 index 0000000..fa1a0d9 --- /dev/null +++ b/.nf-core.yml @@ -0,0 +1,61 @@ +repository_type: pipeline +nf_core_version: 3.2.0 + +lint: + pipeline_name_conventions: False # Ignore: Naming does not adhere to nf-core conventions: Contains uppercase letters + schema_lint: False # Ignore: Parameter input not found in schema + actions_awsfulltest: False + pipeline_todos: False + nextflow_config: False + multiqc_config: False + files_unchanged: False + files_exist: + - .gitattributes + - .editorconfig + - CHANGELOG.md + - CITATIONS.md + - CODE_OF_CONDUCT.md + - .github/.dockstore.yml + - .github/CONTRIBUTING.md + - .github/ISSUE_TEMPLATE/config.yml + - .github/ISSUE_TEMPLATE/feature_request.yml + - .github/PULL_REQUEST_TEMPLATE.md + - .github/workflows/awstest.yml + - .github/workflows/awsfulltest.yml + - .github/workflows/branch.yml + - .github/workflows/linting_comment.yml + - .github/workflows/linting.yml + - .github/workflows/ci.yml + - assets/multiqc_config.yml + - assets/email_template.html + - assets/email_template.txt + - assets/sendmail_template.txt + - assets/nf-core-AI_MARRVEL_logo_light.png + - conf/igenomes.config + - conf/igenomes_ignored.config + - conf/test.config + - conf/test_full.config + - docs/images/nf-core-AI_MARRVEL_logo_light.png + - docs/images/nf-core-AI_MARRVEL_logo_dark.png + - docs/output.md + - docs/README.md + - docs/README.md + - docs/usage.md + - ro-crate-metadata.json + +template: + org: nf-core + name: aim + description: testing template + author: zahid shaik + version: 1.0.0dev + force: true + outdir: . + skip_features: + - codespaces + - fastqc + - license + - adaptivecard + - seqera_platform + - rocrate + is_nfcore: false \ No newline at end of file diff --git a/modules.json b/modules.json new file mode 100644 index 0000000..547c8d9 --- /dev/null +++ b/modules.json @@ -0,0 +1,5 @@ +{ + "name": "LiuzLab/AI_MARRVEL", + "homePage": "https://github.com/LiuzLab/AI_MARRVEL", + "repos": {} +} From b4d662471876b56e09c07c12910e6752a990afd8 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 16:47:01 -0500 Subject: [PATCH 37/80] change nf schema json id Co-authored-by: ZaahidShaik --- nextflow_schema.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nextflow_schema.json b/nextflow_schema.json index a5f5c7b..b1f5e38 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -1,6 +1,6 @@ { "$schema": "https://json-schema.org/draft/2020-12/schema", - "$id": "https://raw.githubusercontent.com/LiuzLab/AI_MARRVEL//nextflow_schema.json", + "$id": "https://raw.githubusercontent.com/LiuzLab/AI_MARRVEL/main/nextflow_schema.json", "title": "LiuzLab/AI_MARRVEL pipeline parameters", "description": "AI-MARRVEL (AIM) is an AI system for rare genetic disease diagnosis.", "type": "object", From 409e331f1ad92471ae9574df0f667ed9bf702850 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 4 Apr 2025 16:54:12 -0500 Subject: [PATCH 38/80] Fix few of linting error Co-authored-by: ZaahidShaik --- .github/ISSUE_TEMPLATE/bug_report.md | 25 ---------------- .github/ISSUE_TEMPLATE/bug_report.yml | 42 +++++++++++++++++++++++++++ .prettierignore | 11 +++++++ .prettierrc.yml | 1 + README.md | 5 +++- 5 files changed, 58 insertions(+), 26 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/bug_report.yml create mode 100644 .prettierignore create mode 100644 .prettierrc.yml diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 53b08ae..0000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: bug -assignees: '' - ---- - -**Describe the bug** -A clear and concise description of what the bug is. - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Input data** -Share the data if possible. If not, describe it as much as you can. - -**Genome build** - -**Screenshots or pipeline output** -If applicable, add screenshots to help explain your problem. - -**Additional context** -Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 0000000..bbbac7e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,42 @@ +name: Bug report +description: Report something that is broken or incorrect +labels: bug +body: + - type: textarea + id: description + attributes: + label: Description of the bug + description: A clear and concise description of what the bug is. + validations: + required: true + + - type: textarea + id: command_used + attributes: + label: Command used and terminal output + description: Steps to reproduce the behaviour. Please paste the command you used to launch the pipeline and the output from your terminal. + render: console + placeholder: | + $ nextflow run ... + + Some output where something broke + + - type: textarea + id: files + attributes: + label: Relevant files + description: | + Please drag and drop the relevant files here. Create a `.zip` archive if the extension is not allowed. + Your verbose log file `.nextflow.log` is often useful _(this is a hidden file in the directory where you launched the pipeline)_ as well as custom Nextflow configuration files. + + - type: textarea + id: system + attributes: + label: System information + description: | + * Nextflow version _(eg. 23.04.0)_ + * Hardware _(eg. HPC, Desktop, Cloud)_ + * Executor _(eg. slurm, local, awsbatch)_ + * Container engine: _(e.g. Docker, Singularity, Conda, Podman, Shifter, Charliecloud, or Apptainer)_ + * OS _(eg. CentOS Linux, macOS, Linux Mint)_ + * Version of LiuzLab/ai_marrvel _(eg. 1.1, 1.5, 1.8.2)_ diff --git a/.prettierignore b/.prettierignore new file mode 100644 index 0000000..5ca594e --- /dev/null +++ b/.prettierignore @@ -0,0 +1,11 @@ +email_template.html +slackreport.json +.nextflow* +work/ +data/ +results/ +.DS_Store +testing/ +testing* +*.pyc +bin/ diff --git a/.prettierrc.yml b/.prettierrc.yml new file mode 100644 index 0000000..c81f9a7 --- /dev/null +++ b/.prettierrc.yml @@ -0,0 +1 @@ +printWidth: 120 diff --git a/README.md b/README.md index 110578e..f278a3d 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,10 @@ - Documentation Status + Documentation Status + + + Nextflow

    From d19ce3b6593a47eaec6845b1c629088127be62de Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 10:29:53 -0500 Subject: [PATCH 39/80] Make it pass all lints Co-authored-by: ZaahidShaik --- README.md | 6 +-- conf/base.config | 10 ++++ conf/modules.config | 11 ++++ nextflow.config | 12 +---- nextflow_schema.json | 116 +++++++++++++++++++++++-------------------- 5 files changed, 88 insertions(+), 67 deletions(-) create mode 100644 conf/base.config create mode 100644 conf/modules.config diff --git a/README.md b/README.md index f278a3d..8e63b66 100644 --- a/README.md +++ b/README.md @@ -12,9 +12,6 @@ Documentation Status - - Nextflow -

    **AI-MARRVEL (AIM)** is an AI system for rare genetic disease diagnosis. @@ -46,6 +43,9 @@ AIM utilizes various databases for variant annotation, all of which have been co ``` ### Get the software + +[![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A523.10.1-23aa62.svg)](https://www.nextflow.io/) + AIM is released as a Nextflow pipeline for easy distribution. To get it: ```bash $ git clone -b nextflow_conversion https://github.com/LiuzLab/AI_MARRVEL diff --git a/conf/base.config b/conf/base.config new file mode 100644 index 0000000..58b8e8c --- /dev/null +++ b/conf/base.config @@ -0,0 +1,10 @@ + +process { + cpus = 2 + memory = { 25.GB * task.attempt } + errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } + maxRetries = 5 + + container = "zhandongliulab/aim-lite:1.1" + cache = "lenient" +} diff --git a/conf/modules.config b/conf/modules.config new file mode 100644 index 0000000..1d2f354 --- /dev/null +++ b/conf/modules.config @@ -0,0 +1,11 @@ +/* +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Config file for defining DSL2 per module options and publishing paths +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + Available keys to override module options: + ext.args = Additional arguments appended to command in module. + ext.args2 = Second set of arguments appended to command in module (multi-tool modules). + ext.args3 = Third set of arguments appended to command in module (multi-tool modules). + ext.prefix = File name prefix for output files. +---------------------------------------------------------------------------------------- +*/ \ No newline at end of file diff --git a/nextflow.config b/nextflow.config index 6acc85d..b66aed6 100644 --- a/nextflow.config +++ b/nextflow.config @@ -15,17 +15,7 @@ params { version = false } -docker.enabled = true - -process { - cpus = 2 - memory = { 25.GB * task.attempt } - errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } - maxRetries = 5 - - container = "zhandongliulab/aim-lite:1.1" - cache = "lenient" -} +includeConfig "./conf/base.config" profiles { debug { diff --git a/nextflow_schema.json b/nextflow_schema.json index b1f5e38..175d8a0 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -5,6 +5,65 @@ "description": "AI-MARRVEL (AIM) is an AI system for rare genetic disease diagnosis.", "type": "object", "$defs": { + "input": { + "title": "Input", + "type": "object", + "description": "", + "default": "", + "properties": { + "input_vcf": { + "type": "string", + "description": "Path to input VCF file.", + "pattern": "^\\S+\\.vcf(.gz)?$", + "format": "file-path" + }, + "input_variant": { + "type": "string", + "description": "Input variant in the format 'chrom-pos-ref-alt', e.g., '1-123456-A-T'.", + "pattern": "^(\\S+)-(\\S+)-(\\S+)-(\\S+)$" + }, + "input_hpo": { + "type": "string", + "description": "Path to input HPO file.", + "format": "file-path" + }, + "input_ped": { + "type": "string", + "description": "Path to the pedgree file for trio mode", + "format": "file-path" + }, + "outdir": { + "type": "string", + "default": "./out", + "description": "Output directory." + }, + "ref_dir": { + "type": "string", + "description": "Path to aim pipeline dependencies directory.", + "format": "directory-path" + }, + "run_id": { + "type": "string", + "default": "run1", + "description": "Unique identifier for this run." + }, + "ref_ver": { + "type": "string", + "default": "hg19", + "enum": ["hg19", "hg38"], + "description": "Reference genome version" + }, + "bed_filter": { + "type": "string", + "description": "Path to a BED file to perform an analysis only for regions of interest" + }, + "exome_filter": { + "type": "boolean", + "description": "Enable exonic filter. Addition will filter out variants outside of exonic region" + } + }, + "required": ["outdir", "ref_dir", "run_id", "ref_ver"] + }, "generic_options": { "title": "Generic options", "type": "object", @@ -23,59 +82,10 @@ }, "allOf": [ { - "$ref": "#/$defs/generic_options" - } - ], - "properties": { - "input_vcf": { - "type": "string", - "description": "Path to input VCF file.", - "pattern": "^\\S+\\.vcf(.gz)?$", - "format": "file-path" - }, - "input_variant": { - "type": "string", - "pattern": "^(\\S+)-(\\S+)-(\\S+)-(\\S+)$" - }, - "input_ped": { - "type": "string", - "description": "Path to the pedgree file for trio mode", - "format": "file-path" + "$ref": "#/$defs/input" }, - "input_hpo": { - "type": "string", - "description": "Path to input HPO file.", - "format": "file-path" - }, - "outdir": { - "type": "string", - "default": "./out", - "description": "Output directory." - }, - "run_id": { - "type": "string", - "default": "run1", - "description": "Unique identifier for this run." - }, - "ref_ver": { - "type": "string", - "default": "hg19", - "enum": ["hg19", "hg38"], - "description": "Reference genome version" - }, - "ref_dir": { - "type": "string", - "description": "Path to aim pipeline dependencies directory.", - "format": "directory-path" - }, - "bed_filter": { - "type": "string", - "description": "Path to a BED file to perform an analysis only for regions of interest" - }, - "exome_filter": { - "type": "boolean", - "description": "Enable exonic filter. Addition will filter out variants outside of exonic region" + { + "$ref": "#/$defs/generic_options" } - }, - "required": ["outdir", "run_id", "ref_ver", "ref_dir"] + ] } From 61a2710dee0dd00e13074262a98bd9173b23486d Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 10:33:38 -0500 Subject: [PATCH 40/80] add precommit config Co-authored-by: ZaahidShaik --- .gitignore | 2 +- .pre-commit-config.yaml | 13 +++++++++++++ 2 files changed, 14 insertions(+), 1 deletion(-) create mode 100644 .pre-commit-config.yaml diff --git a/.gitignore b/.gitignore index c097d1d..7c4b2bd 100644 --- a/.gitignore +++ b/.gitignore @@ -25,7 +25,7 @@ params.yaml .Rhistory .RData -# Remove some files from model interpreter +# Ignore some files from model interpreter */model_interpreter/results */model_interpreter/templates */model_interpreter/main.nf diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..9e9f0e1 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,13 @@ +repos: + - repo: https://github.com/pre-commit/mirrors-prettier + rev: "v3.1.0" + hooks: + - id: prettier + additional_dependencies: + - prettier@3.2.5 + + - repo: https://github.com/editorconfig-checker/editorconfig-checker.python + rev: "3.0.3" + hooks: + - id: editorconfig-checker + alias: ec From 7f5b7d574c9999db10dd72173e439939b2329af7 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 10:35:28 -0500 Subject: [PATCH 41/80] Add workflow actions Co-authored-by: ZaahidShaik --- .github/workflows/nf-core-lint.yml | 83 ++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 .github/workflows/nf-core-lint.yml diff --git a/.github/workflows/nf-core-lint.yml b/.github/workflows/nf-core-lint.yml new file mode 100644 index 0000000..c73170b --- /dev/null +++ b/.github/workflows/nf-core-lint.yml @@ -0,0 +1,83 @@ +name: nf-core linting + +on: + push: + branches: + - main + - nf_core + pull_request: + branches: + - main + +jobs: + pre-commit: + name: Run Pre-Commit Hooks + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: "3.12" + architecture: "x64" + + - name: Install pre-commit + run: pip install pre-commit + + - name: Run pre-commit + run: pre-commit run --all-files + + nf-core: + name: Run nf-core lint + runs-on: ubuntu-latest + strategy: + matrix: + NXF_VER: + - "23.10.1" + + steps: + - name: Check out pipeline code + uses: actions/checkout@v4 + + - name: Install Nextflow + uses: nf-core/setup-nextflow@v2 + with: + version: "${{ matrix.NXF_VER }}" + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + architecture: "x64" + + - name: read .nf-core.yml + uses: pietrobolcato/action-read-yaml@1.1.0 + id: read_yml + with: + config: ${{ github.workspace }}/.nf-core.yml + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install nf-core==${{ steps.read_yml.outputs['nf_core_version'] }} + - name: Run nf-core pipelines lint + if: ${{ github.base_ref != 'master' }} + env: + GITHUB_COMMENTS_URL: ${{ github.event.pull_request.comments_url }} + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_PR_COMMIT: ${{ github.event.pull_request.head.sha }} + run: nf-core -l lint_log.txt pipelines lint --dir ${GITHUB_WORKSPACE} --markdown lint_results.md + + - name: Save PR number + if: ${{ always() }} + run: echo ${{ github.event.pull_request.number }} > PR_number.txt + + - name: Upload linting log artifact + uses: actions/upload-artifact@v4 + with: + name: nf-core-lint-logs + path: | + lint_log.txt + lint_results.md + PR_number.txt From ecb8c4ea08e89b21ad2004a77544b01703169fea Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 10:39:02 -0500 Subject: [PATCH 42/80] Trigger lint for all branch push Co-authored-by: ZaahidShaik --- .github/workflows/nf-core-lint.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/nf-core-lint.yml b/.github/workflows/nf-core-lint.yml index c73170b..2a97eaa 100644 --- a/.github/workflows/nf-core-lint.yml +++ b/.github/workflows/nf-core-lint.yml @@ -3,11 +3,12 @@ name: nf-core linting on: push: branches: - - main - - nf_core + # - main + - "**" pull_request: branches: - main + - develop jobs: pre-commit: From 50cdc60a7e93d15210dbe3bbc478079e075fe968 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 10:41:53 -0500 Subject: [PATCH 43/80] pass the pre-commit hooks Co-authored-by: ZaahidShaik --- .github/workflows/test.yml | 2 -- .nf-core.yml | 2 +- .readthedocs.yaml | 2 +- README.md | 28 +++++++++++++++------------- pyrightconfig.json | 18 ++++++++---------- 5 files changed, 25 insertions(+), 27 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index b2d4f39..bf3978b 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -20,7 +20,6 @@ jobs: aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} aws-region: ${{ secrets.AWS_REGION }} - - name: Install jq run: sudo apt-get install jq @@ -81,7 +80,6 @@ jobs: aws sqs delete-queue --queue-url ${{ steps.step_queue.outputs.queue_url }} echo "Queue deleted: ${{ steps.step_queue.outputs.queue_url }}" - - name: Close Pull Request on Failure if: ${{ failure() }} && github.event_name == 'pull_request' run: | diff --git a/.nf-core.yml b/.nf-core.yml index fa1a0d9..8dcbc6f 100644 --- a/.nf-core.yml +++ b/.nf-core.yml @@ -58,4 +58,4 @@ template: - adaptivecard - seqera_platform - rocrate - is_nfcore: false \ No newline at end of file + is_nfcore: false diff --git a/.readthedocs.yaml b/.readthedocs.yaml index fd115c3..809f5c0 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,4 +10,4 @@ python: - requirements: docs/source/requirements.txt sphinx: - configuration: docs/source/conf.py \ No newline at end of file + configuration: docs/source/conf.py diff --git a/README.md b/README.md index 8e63b66..49b06b9 100644 --- a/README.md +++ b/README.md @@ -14,22 +14,20 @@

    -**AI-MARRVEL (AIM)** is an AI system for rare genetic disease diagnosis. - -It takes as input patient VCF and phenotype (formatted with HPO) to predict the causal variant(s). -In making prediction, it takes variant annotation from [MARRVEL](https://marrvel.org/) database and more, -and generates **prediction score** + **confidence score** as output. +**AI-MARRVEL (AIM)** is an AI system for rare genetic disease diagnosis. +It takes as input patient VCF and phenotype (formatted with HPO) to predict the causal variant(s). +In making prediction, it takes variant annotation from [MARRVEL](https://marrvel.org/) database and more, +and generates **prediction score** + **confidence score** as output. You can use AI-MARRVEL from our [website](https://ai.marrvel.org/) or follow the [documentation](https://aimarrvel.readthedocs.io/en/main/) to run locally. - :new: Our paper is now published in [NEJM AI](https://ai.nejm.org/doi/full/10.1056/AIoa2300009)! - ## Quick Start ### Install Required Data Dependencies + AIM utilizes various databases for variant annotation, all of which have been compiled and are available for download. We use AWS S3 for data access, and the data can be downloaded by following these steps: 1. **Install the AWS CLI**: Follow the instructions provided in the [AWS CLI Installation Guide](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html). @@ -37,16 +35,17 @@ AIM utilizes various databases for variant annotation, all of which have been co ```bash $ cd ``` -3. Use the following command to sync the S3 bucket to your local directory: - ```bash - $ aws s3 sync s3://aim-data-dependencies-2.0-public . --no-sign-request - ``` +3. Use the following command to sync the S3 bucket to your local directory: + ```bash + $ aws s3 sync s3://aim-data-dependencies-2.0-public . --no-sign-request + ``` ### Get the software [![Nextflow](https://img.shields.io/badge/nextflow%20DSL2-%E2%89%A523.10.1-23aa62.svg)](https://www.nextflow.io/) AIM is released as a Nextflow pipeline for easy distribution. To get it: + ```bash $ git clone -b nextflow_conversion https://github.com/LiuzLab/AI_MARRVEL $ cd AI_MARRVEL @@ -54,6 +53,7 @@ $ nextflow run main.nf --version ``` ### Run with your sample + ```bash $ nextflow run main.nf --ref_dir --input_vcf @@ -66,9 +66,11 @@ $ nextflow run main.nf --ref_dir ``` Alternatively, the pipeline can be executed with a parameter file (yaml) + ```bash $ nextflow run main.nf -params-file params.yaml ``` + NOTE: You need to create `params.yaml` by copying [params.yaml.example](params.yaml.example) file and follow the instruction. For more information on usage and parameters which are open for modification, please use `--help` option as shown below. @@ -77,13 +79,13 @@ For more information on usage and parameters which are open for modification, pl $ nextflow run main.nf --help ``` - ## License + AI-MARRVEL is licensed under GPL-3.0. You are welcomed to use it for research purpose. For business purpose, please contact us for licensing. - ## Disclaimer + - Some of the data and software included in the distribution may be subject to third-party constraints. Users of the data and software are solely responsible for establishing the nature of and complying with any such restrictions. - AI-MARRVEL provides this data and software in good faith, but make no warranty, express or implied, nor assume any legal liability or responsibility for any purpose for which they are used. diff --git a/pyrightconfig.json b/pyrightconfig.json index 90fa59d..587cfad 100644 --- a/pyrightconfig.json +++ b/pyrightconfig.json @@ -1,12 +1,10 @@ { - "include": [ - "bin" - ], - - "defineConstant": { - "DEBUG": true - }, - - "reportMissingImports": "error", - "reportMissingTypeStubs": false, + "include": ["bin"], + + "defineConstant": { + "DEBUG": true + }, + + "reportMissingImports": "error", + "reportMissingTypeStubs": false } From 02c58b59909abfe93716130577c073ceed57d1ce Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 10:57:02 -0500 Subject: [PATCH 44/80] remove duplicated entry Co-authored-by: ZaahidShaik --- .nf-core.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.nf-core.yml b/.nf-core.yml index 8dcbc6f..64ee119 100644 --- a/.nf-core.yml +++ b/.nf-core.yml @@ -39,7 +39,6 @@ lint: - docs/images/nf-core-AI_MARRVEL_logo_dark.png - docs/output.md - docs/README.md - - docs/README.md - docs/usage.md - ro-crate-metadata.json From 1e4185c20ac91e3bb028d6e92c038fa61fdeffd4 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 11:16:33 -0500 Subject: [PATCH 45/80] show the version to 1.1.0 on the readme badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 49b06b9..9b374c2 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@

    - + Documentation Status From 491e06c90afe5cda3c475a9c7b3046671c3b3e92 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 11:21:22 -0500 Subject: [PATCH 46/80] revive docker enabled = true for the default setting --- nextflow.config | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nextflow.config b/nextflow.config index b66aed6..13d11d5 100644 --- a/nextflow.config +++ b/nextflow.config @@ -15,7 +15,7 @@ params { version = false } -includeConfig "./conf/base.config" +docker.enabled = true profiles { debug { From 1f47a720402d4c162e3577d11a742a1403446fc3 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 12:16:14 -0500 Subject: [PATCH 47/80] fix minors --- conf/base.config | 1 + main.nf | 5 +++-- modules/local/singleton/main.nf | 6 +++--- nextflow.config | 2 +- 4 files changed, 8 insertions(+), 6 deletions(-) diff --git a/conf/base.config b/conf/base.config index 58b8e8c..9f7d470 100644 --- a/conf/base.config +++ b/conf/base.config @@ -1,3 +1,4 @@ +docker.enabled = true process { cpus = 2 diff --git a/main.nf b/main.nf index 5ffd63e..8c8b2c3 100644 --- a/main.nf +++ b/main.nf @@ -7,7 +7,7 @@ include { } from "./modules/local/utils" include { - BUILD_REFERENCE_INDEX + BUILD_REFERENCE_INDEX; GENERATE_INPUT_VCF; NORMALIZE_VCF } from "./modules/local/singleton" include { @@ -46,7 +46,8 @@ workflow { vcf = VCF_PRE_PROCESS.out.vcf } else if (params.input_variant) { GENERATE_INPUT_VCF(params.input_variant) - vcf = GENERATE_INPUT_VCF.out.vcf + NORMALIZE_VCF(GENERATE_INPUT_VCF.out.vcf) + vcf = NORMALIZE_VCF.out.vcf } GENERATE_SINGLETON_FEATURES(vcf, hpo) diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 7522d2e..18241ad 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -541,9 +541,9 @@ process PREDICTION { output: path "conf_4Model/${params.run_id}_default_predictions.csv", emit: "default_predictions" - path "conf_4Model/${params.run_id}_recessive_predictions.csv" - path "conf_4Model/${params.run_id}_nd_predictions.csv" - path "conf_4Model/${params.run_id}_nd_recessive_predictions.csv" + path "conf_4Model/${params.run_id}_recessive_predictions.csv", optional: true + path "conf_4Model/${params.run_id}_nd_predictions.csv", optional: true + path "conf_4Model/${params.run_id}_nd_recessive_predictions.csv", optional: true path "conf_4Model/integrated/*.csv" path "final_matrix_expanded/*.csv.gz" path "shap_outputs" diff --git a/nextflow.config b/nextflow.config index 13d11d5..c5cfa54 100644 --- a/nextflow.config +++ b/nextflow.config @@ -15,7 +15,7 @@ params { version = false } -docker.enabled = true +includeConfig 'conf/base.config' profiles { debug { From 88dfa84196f68f5a49e3d989db29c0f71b17339f Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 12:33:47 -0500 Subject: [PATCH 48/80] use underscore instead of hyphen for input_variant --- bin/generate_input_vcf.py | 4 ++-- nextflow_schema.json | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/bin/generate_input_vcf.py b/bin/generate_input_vcf.py index 93f9765..719cbbe 100755 --- a/bin/generate_input_vcf.py +++ b/bin/generate_input_vcf.py @@ -18,11 +18,11 @@ ##FORMAT= ##FILTER= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE -$chrom $pos . $ref $alt . . . GT:AD:DP:GQ:PL 0/1:7,5:12:99:142,0,214 +$chrom $pos ${chrom}_${pos}_${ref}_${alt} $ref $alt . . . GT:AD:DP:GQ:PL 0/1:7,5:12:99:142,0,214 """.strip()) def main(variant): - chrom, pos, ref, alt = variant.split("-") + chrom, pos, ref, alt = variant.split("_") with open("input.vcf", "w", encoding="ascii") as text_file: text_file.write(vcf_template.substitute( chrom=chrom, diff --git a/nextflow_schema.json b/nextflow_schema.json index 175d8a0..4b3af7c 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -20,7 +20,7 @@ "input_variant": { "type": "string", "description": "Input variant in the format 'chrom-pos-ref-alt', e.g., '1-123456-A-T'.", - "pattern": "^(\\S+)-(\\S+)-(\\S+)-(\\S+)$" + "pattern": "^(\\S+)_(\\S+)_(\\S+)_(\\S+)$" }, "input_hpo": { "type": "string", From 58204bad7ff05d3af94e77a964db9fa85a9affee Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 12:57:14 -0500 Subject: [PATCH 49/80] fix minor mistake --- subworkflows/local/singleton/main.nf | 3 +++ 1 file changed, 3 insertions(+) diff --git a/subworkflows/local/singleton/main.nf b/subworkflows/local/singleton/main.nf index 9d704bf..7f95cba 100644 --- a/subworkflows/local/singleton/main.nf +++ b/subworkflows/local/singleton/main.nf @@ -27,6 +27,9 @@ workflow VCF_PRE_PROCESS { CONVERT_GVCF( NORMALIZE_VCF.out.vcf, NORMALIZE_VCF.out.tbi, + fasta, + fasta_index, + fasta_dict, params.chrmap ) FILTER_UNPASSED( From 7c09afe1403a8e378982c3ae6b76636a6470dcdd Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 8 Apr 2025 12:57:14 -0500 Subject: [PATCH 50/80] fix minor mistake --- subworkflows/local/singleton/main.nf | 3 +++ 1 file changed, 3 insertions(+) diff --git a/subworkflows/local/singleton/main.nf b/subworkflows/local/singleton/main.nf index 9d704bf..7f95cba 100644 --- a/subworkflows/local/singleton/main.nf +++ b/subworkflows/local/singleton/main.nf @@ -27,6 +27,9 @@ workflow VCF_PRE_PROCESS { CONVERT_GVCF( NORMALIZE_VCF.out.vcf, NORMALIZE_VCF.out.tbi, + fasta, + fasta_index, + fasta_dict, params.chrmap ) FILTER_UNPASSED( From 658776157965859a1ffcf60d8779e0c6af964df7 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Wed, 9 Apr 2025 15:10:45 -0500 Subject: [PATCH 51/80] Do Singleton Preprocess for Trio and Remove sorted gene to sym --- bin/generate_new_matrix_2.py | 1 - main.nf | 8 +++++++- modules/local/singleton/main.nf | 6 ++++-- modules/local/utils/main.nf | 1 - subworkflows/local/singleton/main.nf | 2 +- 5 files changed, 12 insertions(+), 6 deletions(-) diff --git a/bin/generate_new_matrix_2.py b/bin/generate_new_matrix_2.py index b1685df..783cf52 100755 --- a/bin/generate_new_matrix_2.py +++ b/bin/generate_new_matrix_2.py @@ -20,7 +20,6 @@ def main(): ### get original coordinates ### merged["varId"] = merged["varId"].apply(lambda x: x.split("_E")[0]) - path_phrank = sys.argv[1] + ".phrank.txt" phr = pd.read_csv(path_phrank, sep="\t", names=["ENSG", "phrank"]) merged = merged.merge(phr, left_on="geneEnsId", right_on="ENSG", how="left") diff --git a/main.nf b/main.nf index 2b41986..bb69ed5 100644 --- a/main.nf +++ b/main.nf @@ -33,7 +33,13 @@ workflow { BUILD_REFERENCE_INDEX.out.fasta_dict, params.chrmap, ) - vcf = VCF_PRE_PROCESS_TRIO.out.vcf + VCF_PRE_PROCESS( + VCF_PRE_PROCESS_TRIO.out.vcf, + BUILD_REFERENCE_INDEX.out.fasta, + BUILD_REFERENCE_INDEX.out.fasta_index, + BUILD_REFERENCE_INDEX.out.fasta_dict, + ) + vcf = VCF_PRE_PROCESS.out.vcf } else if (params.input_vcf) { BUILD_REFERENCE_INDEX() VCF_PRE_PROCESS( diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 7522d2e..e680277 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -264,15 +264,17 @@ process ENSEMBL_TO_GENESYM { input: path ensmbl path ref_to_sym - path ref_sorted_sym output: path "${params.run_id}-gene.txt", emit: gene script: """ + # Generate sorted gene to symbol file + sort -t \$'\t' -k2,2 $ref_to_sym > sorted_ref_to_sym.txt + cat $ensmbl | sort -k5,5 | join -1 5 -2 1 - $ref_to_sym | sed 's/ /\\t/g' | cut -f2- > genesym.txt - cat genesym.txt | cut -f5 | sort -u | join -t\$'\\t' -1 1 -2 2 - $ref_sorted_sym | cut -f2 | sort -u > ${params.run_id}-gene.txt + cat genesym.txt | cut -f5 | sort -u | join -t\$'\\t' -1 1 -2 2 - sorted_ref_to_sym.txt | cut -f2 | sort -u > ${params.run_id}-gene.txt """ } diff --git a/modules/local/utils/main.nf b/modules/local/utils/main.nf index 97d1143..2152eb6 100644 --- a/modules/local/utils/main.nf +++ b/modules/local/utils/main.nf @@ -79,7 +79,6 @@ def addDependentParams(params) { params.ref_loc = "${params.ref_dir}/phrank/${params.ref_ver}/${params.ref_assembly}_symbol_to_location.txt" params.ref_to_sym = "${params.ref_dir}/phrank/${params.ref_ver}/ensembl_to_symbol.txt" - params.ref_sorted_sym = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_symbol_sorted.txt" // FILTER BED // EXONIC FILTER BED diff --git a/subworkflows/local/singleton/main.nf b/subworkflows/local/singleton/main.nf index 7f95cba..742a437 100644 --- a/subworkflows/local/singleton/main.nf +++ b/subworkflows/local/singleton/main.nf @@ -66,7 +66,7 @@ workflow PHRANK_SCORING { main: VCF_TO_VARIANTS(vcf) VARIANTS_TO_ENSEMBL(VCF_TO_VARIANTS.out, params.ref_loc) - ENSEMBL_TO_GENESYM(VARIANTS_TO_ENSEMBL.out, params.ref_to_sym, params.ref_sorted_sym) + ENSEMBL_TO_GENESYM(VARIANTS_TO_ENSEMBL.out, params.ref_to_sym) GENESYM_TO_PHRANK(ENSEMBL_TO_GENESYM.out, file(params.input_hpo), params.phrank_dagfile, From 6753f943d883701feb15af78cd8894d14281f527 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Thu, 10 Apr 2025 10:28:24 -0500 Subject: [PATCH 52/80] remove template part cuz it is not necessary --- .nf-core.yml | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/.nf-core.yml b/.nf-core.yml index 64ee119..b590482 100644 --- a/.nf-core.yml +++ b/.nf-core.yml @@ -41,20 +41,3 @@ lint: - docs/README.md - docs/usage.md - ro-crate-metadata.json - -template: - org: nf-core - name: aim - description: testing template - author: zahid shaik - version: 1.0.0dev - force: true - outdir: . - skip_features: - - codespaces - - fastqc - - license - - adaptivecard - - seqera_platform - - rocrate - is_nfcore: false From 1e9357021ab0c8a7acbdec4be616e40d8387691b Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Wed, 16 Apr 2025 10:27:03 -0500 Subject: [PATCH 53/80] Use empty file instead of dev null --- assets/NO_FILE | 0 modules/local/utils/main.nf | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 assets/NO_FILE diff --git a/assets/NO_FILE b/assets/NO_FILE new file mode 100644 index 0000000..e69de29 diff --git a/modules/local/utils/main.nf b/modules/local/utils/main.nf index b3d52db..4af9bf7 100644 --- a/modules/local/utils/main.nf +++ b/modules/local/utils/main.nf @@ -24,7 +24,7 @@ def addDependentParams(params) { } else if (params.exome_filter) { params.ref_filter_bed = params.ref_exonic_filter_bed } else { - params.ref_filter_bed = "/dev/null" + params.ref_filter_bed = moduleDir.resolve("./assets/NO_FILE") } // for phrank From 9e3e1a6e12e88cc0354b8a07edc1ef0239a8399e Mon Sep 17 00:00:00 2001 From: Zhijian Yu Date: Thu, 24 Apr 2025 14:41:12 -0500 Subject: [PATCH 54/80] updated the exon filter generator to work with hgmd2024 data --- .../version2_exon-only/gri_v2.R | 541 +++++++++--------- 1 file changed, 271 insertions(+), 270 deletions(-) diff --git a/utils/GRI_bed_filter/version2_exon-only/gri_v2.R b/utils/GRI_bed_filter/version2_exon-only/gri_v2.R index 2afaf57..e607d58 100644 --- a/utils/GRI_bed_filter/version2_exon-only/gri_v2.R +++ b/utils/GRI_bed_filter/version2_exon-only/gri_v2.R @@ -3,20 +3,22 @@ # + Cinvar mutations (50 flanking bp) # + spliceAI data (50 flanking bp) # -# gencode data: +# 1. gencode data: # https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/gencode.v46.basic.annotation.gtf.gz # https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/GRCh37_mapping/gencode.v46lift37.basic.annotation.gtf.gz # -# HGMD data: -# HGMD_Pro_2022.2_hg38.vcf.gz HGMD_Pro_2022.2_hg19.vcf.gz from Hwan +# 2. HGMD data: +# HGMD_Pro_2024.3_hg38.vcf HGMD_Pro_2024.3_hg19.vcf # -# Clinvar data: +# 3. Clinvar data: # https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/ # https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/ # -# spliceAI data v1.3 is on ravenclaw server: -# /mnt/ravenclaw_local/zhijiany/workdir/exome_filter +# 4. spliceAI data v1.3 is on skyriver server: +# /home/zhijiany/data-transfer/AIM/spliceAI-2019/exome_filter +### Notes: 1. Please prepare/download all the data files before running the script +### 2. Decompress the gz files to avoid error when loading the data library(VariantAnnotation) library(BSgenome.Hsapiens.UCSC.hg38) @@ -26,275 +28,274 @@ library(dplyr) library(readr) library(data.table) +generate_gri_v2 <- function(genome_version = "hg38") { + # data preparation -------------------------------------------------------- + ## construct splice data file name + snv <- paste0("snv.", genome_version, ".vcf") + indel <- paste0("indel.", genome_version, ".vcf") + columnnames <- unlist(strsplit("CHROM POS ID REF ALT QUAL FILTER INFO", "\t")) -generate_gri_v2 <- function(genome_version = "hg38") { - # data preparation -------------------------------------------------------- - - ##construct splice data file name - snv <- paste0("snv.", genome_version, ".vcf") - indel <- paste0("indel.", genome_version, ".vcf") - columnnames = unlist(strsplit("CHROM POS ID REF ALT QUAL FILTER INFO", "\t")) - - ##load spliceAI data - splice_snv <- read_table(snv, - comment = "#", - col_names = columnnames, - col_types = "cncccccc") - - splice_indel <- read_table(indel, - comment = "#", - col_names = columnnames, - col_types = "cncccccc") - - - ## hgmd local file name - hgmd_file = paste0("HGMD_Pro_2022.2_", genome_version, ".vcf.gz") - - ## use different data based on genome version - if (genome_version == "hg38") { - ##load chr size - hg38 <- BSgenome.Hsapiens.UCSC.hg38 - size <- seqlengths(hg38) - chr_size <- - data.frame(seqnames = names(size), chr_end = as.integer(size))[c(1:24), ] - - ##load gencode data - gtf_data <- rtracklayer::import("gencode.v46.basic.annotation.gtf") - - ##load hgmd data - hgmd_data <- readVcf(hgmd_file, genome = "hg38") - - ##load clinvar data - clinvar_data <- readVcf("clinvar.vcf.gz", genome = "hg38") - - - } else if (genome_version == "hg19") { - ##load chr size - hg19 <- BSgenome.Hsapiens.UCSC.hg19 - size <- seqlengths(hg19) - chr_size <- - data.frame(seqnames = names(size), chr_end = as.integer(size))[c(1:24), ] - - ##load gencode data - gtf_data <- rtracklayer::import("gencode.v46lift37.basic.annotation.gtf") - - ##load hgmd data - hgmd_data <- readVcf(hgmd_file, genome = "hg19") - - ##load clinvar data - clinvar_data <- readVcf("clinvar.vcf.gz", genome = "hg19") - } - - - ## function to check bed file coverage - coverage_bed <- function(bed_file, chromosome_size = chr_size) { - ## calculate hg chr bp length - seq.size <- sum(chromosome_size[, 2]) - ## calculate bed size - bed.size <- sum(bed_file[, 4]) - ## get percentage - size.per <- bed.size / seq.size - print(paste( - paste0("bed file coverage is: ", size.per * 100, "% ;"), - bed.size, - "bp out of", - seq.size, - "bp" - )) - } - - # 1. include gencode v46 annotation ---------------------------- - - ## convert gtf data to data.table and add chr size - gtf_dt <- as.data.table(gtf_data) - gtf_dt_size <- - merge(gtf_dt, chr_size, by = "seqnames") # no MT chr - - ## select exon only - exons <- - gtf_dt_size[type == "exon"] - - ## extract gene location info and save into bed format - ## add source column - bed_coding <- exons |> - dplyr::select( - chr = seqnames, - start = start, - end = end, - name = gene_name, - strand = strand, - ) |> - mutate(source = "GencodeV46.exon") - - ## merge overlapped region - gr <- GRanges( - seqnames = bed_coding$chr, - ranges = IRanges(start = bed_coding$start, end = bed_coding$end) - ) - - merged_gr <- reduce(gr) - bed_coding.dt <- as.data.table(merged_gr) - coverage_bed(bed_coding.dt) #about 4% coverage - - - # 2. include hgmd 2022 variants ----------------------------------------- - - ## extract fields and convert to data.table - info_dt <- as.data.table(info(hgmd_data)) - range_dt <- as.data.table(rowRanges(hgmd_data)) - - ## merged useful columns - hgmd <- cbind(range_dt, info_dt) |> - dplyr::select(seqnames, start, end, GENE, STRAND, CLASS, MUT) |> - mutate(source = "HGMD_2022") - - ## exclude "rejected variants" in HGMD database - hgmd_noR <- hgmd[-which(hgmd$CLASS == "R"), ] - hgmd_noR <- hgmd_noR |> - dplyr::mutate(seqnames = paste0("chr", seqnames)) - - ## add 50bp flanking region - hgmd_noR <- merge(hgmd_noR, chr_size, by = "seqnames") - hgmd_noR_flank <- hgmd_noR |> - dplyr::mutate( - final_start = ifelse(start > 50, start - 50, 1), - final_end = ifelse((end + 50) > chr_end, chr_end, end + 50), - .after = seqnames + ## load spliceAI data + splice_snv <- read_table(snv, + comment = "#", + col_names = columnnames, + col_types = "cncccccc" + ) + + splice_indel <- read_table(indel, + comment = "#", + col_names = columnnames, + col_types = "cncccccc" + ) + + + ## hgmd local file name + hgmd_file <- paste0("HGMD_Pro_2024.3_", genome_version, ".vcf") + + ## use different data based on genome version + if (genome_version == "hg38") { + ## load chr size + hg38 <- BSgenome.Hsapiens.UCSC.hg38 + size <- seqlengths(hg38) + chr_size <- + data.frame(seqnames = names(size), chr_end = as.integer(size))[c(1:24), ] + + ## load gencode data + gtf_data <- rtracklayer::import("gencode.v46.basic.annotation.gtf") + + ## load hgmd data + hgmd_data <- readVcf(hgmd_file, genome = "hg38") + + ## load clinvar data + clinvar_data <- readVcf("clinvar.vcf.gz", genome = "hg38") + } else if (genome_version == "hg19") { + ## load chr size + hg19 <- BSgenome.Hsapiens.UCSC.hg19 + size <- seqlengths(hg19) + chr_size <- + data.frame(seqnames = names(size), chr_end = as.integer(size))[c(1:24), ] + + ## load gencode data + gtf_data <- rtracklayer::import("gencode.v46lift37.basic.annotation.gtf") + + ## load hgmd data + hgmd_data <- readVcf(hgmd_file, genome = "hg19") + + ## load clinvar data + clinvar_data <- readVcf("clinvar.vcf.gz", genome = "hg19") + } + + + ## function to check bed file coverage + coverage_bed <- function(bed_file, chromosome_size = chr_size) { + ## calculate hg chr bp length + seq.size <- sum(chromosome_size[, 2]) + ## calculate bed size + bed.size <- sum(bed_file[, 4]) + ## get percentage + size.per <- bed.size / seq.size + print(paste( + paste0("bed file coverage is: ", size.per * 100, "% ;"), + bed.size, + "bp out of", + seq.size, + "bp" + )) + } + + # 1. include gencode v46 annotation ---------------------------- + + ## convert gtf data to data.table and add chr size + gtf_dt <- as.data.table(gtf_data) + gtf_dt_size <- + merge(gtf_dt, chr_size, by = "seqnames") # no MT chr + + ## select exon only + exons <- + gtf_dt_size[type == "exon"] + + ## extract gene location info and save into bed format + ## add source column + bed_coding <- exons |> + dplyr::select( + chr = seqnames, + start = start, + end = end, + name = gene_name, + strand = strand, + ) |> + mutate(source = "GencodeV46.exon") + + ## merge overlapped region + gr <- GRanges( + seqnames = bed_coding$chr, + ranges = IRanges(start = bed_coding$start, end = bed_coding$end) + ) + + merged_gr <- reduce(gr) + bed_coding.dt <- as.data.table(merged_gr) + coverage_bed(bed_coding.dt) # about 4% coverage + + + # 2. include hgmd 2022 variants ----------------------------------------- + + ## extract fields and convert to data.table + info_dt <- as.data.table(info(hgmd_data)) + range_dt <- as.data.table(rowRanges(hgmd_data)) + + ## merged useful columns + hgmd <- cbind(range_dt, info_dt) |> + dplyr::select(seqnames, start, end, GENE, STRAND, CLASS, MUT) |> + mutate(source = "HGMD_2024") + + ## exclude "rejected variants" in HGMD database + hgmd_noR <- hgmd[-which(hgmd$CLASS == "R"), ] + hgmd_noR <- hgmd_noR |> + dplyr::mutate(seqnames = paste0("chr", seqnames)) + + ## add 50bp flanking region + hgmd_noR <- merge(hgmd_noR, chr_size, by = "seqnames") + hgmd_noR_flank <- hgmd_noR |> + dplyr::mutate( + final_start = ifelse(start > 50, start - 50, 1), + final_end = ifelse((end + 50) > chr_end, chr_end, end + 50), + .after = seqnames + ) + + ## generate bed + bed_hgmd <- hgmd_noR_flank |> + dplyr::select( + chr = seqnames, + start = final_start, + end = final_end, + name = GENE, + strand = STRAND, + source + ) + + ## merge bed files from step1 and step2, and reduce them into non-overlapping regions + bed_gencode_hgmd <- rbind(bed_coding, bed_hgmd, fill = TRUE) |> + arrange(chr, start, end) + + gr <- GRanges( + seqnames = bed_gencode_hgmd$chr, + ranges = IRanges(start = bed_gencode_hgmd$start, end = bed_gencode_hgmd$end) + ) + + merged_gr <- reduce(gr) + bed_gencode_hgmd.dt <- as.data.table(merged_gr) + coverage_bed(bed_gencode_hgmd.dt) # about 4.16% coverage + + # 3. Include clinvar variants---------------------------------------- + + ## obtain pathogenic variants + info_df <- + as.data.frame(clinvar_data@info) # faster to load as dataframe than datatable + range_df <- as.data.frame(clinvar_data@rowRanges) + clinvar <- cbind(range_df, info_df) + clinvar_patho <- clinvar |> + filter(grepl("pathogenic", CLNSIG)) + + ## remove MT chromosome + ## rename seqnames + clinvar_patho <- clinvar_patho |> + filter(seqnames != "MT") + clinvar_patho <- clinvar_patho |> + mutate(seqnames = paste0("chr", seqnames)) + + ## add chromosome size column + clinvar_patho <- merge(clinvar_patho, chr_size, by = "seqnames") + + ## add flanking region + clinvar_patho_flank <- clinvar_patho |> + mutate( + final_start = ifelse(start > 50, start - 50, 1), + final_end = ifelse((end + 50) > chr_end, chr_end, end + 50), + .after = seqnames + ) + + ## generate bed + bed_clinvar <- clinvar_patho_flank |> + dplyr::select( + chr = seqnames, + start = final_start, + end = final_end, + name = GENEINFO, + strand = strand + ) |> + mutate(source = "Clinvar_latest") # the Clinvar link provides latest vcf file + + ## merge and reduce + bed_gencode_hgmd_clinvar <- + rbind(bed_gencode_hgmd, bed_clinvar) |> + arrange(chr, start, end) + + gr <- GRanges( + seqnames = bed_gencode_hgmd_clinvar$chr, + ranges = IRanges(start = bed_gencode_hgmd_clinvar$start, end = bed_gencode_hgmd_clinvar$end) ) - - ## generate bed - bed_hgmd <- hgmd_noR_flank |> - dplyr::select( - chr = seqnames, - start = final_start, - end = final_end, - name = GENE, - strand = STRAND, - source + + merged_gr <- reduce(gr) + bed_gencode_hgmd_clinvar.dt <- as.data.table(merged_gr) + + ## check bed coverage + coverage_bed(bed_gencode_hgmd_clinvar.dt) # coverage about 4.22% + + + + # 4. Include spliceAI data ---------------------------------------------------- + # Format: ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL"> + + # merge snv and indel and add 50 flanking bp + splice <- rbind(splice_snv, splice_indel) |> + mutate(seqnames = paste0("chr", CHROM)) + splice <- merge(splice, chr_size, by = "seqnames") + + splice_flank <- splice |> + mutate( + chr = seqnames, + start = ifelse(POS - 50 < 1, 1, POS - 50), + end = ifelse(POS + 50 > chr_end, chr_end, POS + 50), + source = "spliceAI_v1.3" + ) |> + dplyr::select(chr, start, end, source) + + ## merge and reduce + bed_gencode_hgmd_clinvar_splice <- + rbind(bed_gencode_hgmd_clinvar, splice_flank, fill = TRUE) |> + arrange(chr, start, end) + + gr <- GRanges( + seqnames = bed_gencode_hgmd_clinvar_splice$chr, + ranges = IRanges(start = bed_gencode_hgmd_clinvar_splice$start, end = bed_gencode_hgmd_clinvar_splice$end) ) - - ## merge bed files from step1 and step2, and reduce them into non-overlapping regions - bed_gencode_hgmd <- rbind(bed_coding, bed_hgmd, fill = TRUE) |> - arrange(chr, start, end) - - gr <- GRanges( - seqnames = bed_gencode_hgmd$chr, - ranges = IRanges(start = bed_gencode_hgmd$start, end = bed_gencode_hgmd$end) - ) - - merged_gr <- reduce(gr) - bed_gencode_hgmd.dt <- as.data.table(merged_gr) - coverage_bed(bed_gencode_hgmd.dt) #about 4.16% coverage - - # 3. Include clinvar variants---------------------------------------- - - ## obtain pathogenic variants - info_df <- - as.data.frame(clinvar_data@info) #faster to load as dataframe than datatable - range_df <- as.data.frame(clinvar_data@rowRanges) - clinvar <- cbind(range_df, info_df) - clinvar_patho <- clinvar |> - filter(grepl("pathogenic", CLNSIG)) - - ## remove MT chromosome - ## rename seqnames - clinvar_patho <- clinvar_patho |> - filter(seqnames != "MT") - clinvar_patho <- clinvar_patho |> - mutate(seqnames = paste0("chr", seqnames)) - - ## add chromosome size column - clinvar_patho <- merge(clinvar_patho, chr_size, by = "seqnames") - - ## add flanking region - clinvar_patho_flank <- clinvar_patho |> - mutate( - final_start = ifelse(start > 50, start - 50, 1), - final_end = ifelse((end + 50) > chr_end, chr_end, end + 50), - .after = seqnames + + merged_gr <- reduce(gr) + splice.dt <- as.data.table(merged_gr) + print("Final coverage is below:") + coverage_bed(splice.dt) # coverage is about (4.36% without 50bp flanks) 5.75% + + # convert start position from 1-based to 0-based of bed format ------------ + splice.dt <- splice.dt |> + mutate(start = pmax(0, start - 1)) + + bed_gencode_hgmd_clinvar_splice <- bed_gencode_hgmd_clinvar_splice |> + mutate(start = pmax(0, start - 1)) + + # save the file + # write the bed file + file_name <- paste0("exon_only.", genome_version, ".bed") + write_tsv(splice.dt[, c(1:3)], file = file_name, col_names = FALSE) + + # save the initial bed files which are not reduced, which contains the source column to indicate where that region is from + file_name <- paste0("exon_only_source.", genome_version, ".bed") + write_tsv(bed_gencode_hgmd_clinvar_splice, + file = file_name, + col_names = FALSE ) - - ## generate bed - bed_clinvar <- clinvar_patho_flank |> - dplyr::select( - chr = seqnames, - start = final_start, - end = final_end, - name = GENEINFO, - strand = strand - ) |> - mutate(source = "Clinvar_latest") #the Clinvar link provides latest vcf file - - ## merge and reduce - bed_gencode_hgmd_clinvar <- - rbind(bed_gencode_hgmd, bed_clinvar) |> - arrange(chr, start, end) - - gr <- GRanges( - seqnames = bed_gencode_hgmd_clinvar$chr, - ranges = IRanges(start = bed_gencode_hgmd_clinvar$start, end = bed_gencode_hgmd_clinvar$end) - ) - - merged_gr <- reduce(gr) - bed_gencode_hgmd_clinvar.dt <- as.data.table(merged_gr) - - ## check bed coverage - coverage_bed(bed_gencode_hgmd_clinvar.dt) #coverage about 4.22% - - - - # 4. Include spliceAI data ---------------------------------------------------- - #Format: ALLELE|SYMBOL|DS_AG|DS_AL|DS_DG|DS_DL|DP_AG|DP_AL|DP_DG|DP_DL"> - - #merge snv and indel and add 50 flanking bp - splice <- rbind(splice_snv, splice_indel) |> - mutate(seqnames = paste0("chr", CHROM)) - splice <- merge(splice, chr_size, by = "seqnames") - - splice_flank <- splice |> - mutate( - chr = seqnames, - start = ifelse(POS - 50 < 1, 1, POS - 50), - end = ifelse(POS + 50 > chr_end, chr_end, POS + 50), - source = "spliceAI_v1.3" - ) |> - dplyr::select(chr, start, end, source) - - ## merge and reduce - bed_gencode_hgmd_clinvar_splice <- - rbind(bed_gencode_hgmd_clinvar, splice_flank, fill = TRUE) |> - arrange(chr, start, end) - - gr <- GRanges( - seqnames = bed_gencode_hgmd_clinvar_splice$chr, - ranges = IRanges(start = bed_gencode_hgmd_clinvar_splice$start, end = bed_gencode_hgmd_clinvar_splice$end) - ) - - merged_gr <- reduce(gr) - splice.dt <- as.data.table(merged_gr) - coverage_bed(splice.dt) #coverage is about (4.36% without 50bp flanks) 5.75% - - # convert start position from 1-based to 0-based of bed format ------------ - splice.dt <- splice.dt |> - mutate(start = pmax(0, start - 1)) - - bed_gencode_hgmd_clinvar_splice <- bed_gencode_hgmd_clinvar_splice |> - mutate(start = pmax(0, start - 1)) - - #save the file - # write the bed file - file_name <- paste0("exon_only.", genome_version, ".bed") - write_tsv(splice.dt[, c(1:3)], file = file_name, col_names = FALSE) - - # save the initial bed files which are not reduced, which contains the source column to indicate where that region is from - file_name <- paste0("exon_only_source.", genome_version, ".bed") - write_tsv(bed_gencode_hgmd_clinvar_splice, - file = file_name, - col_names = FALSE) - } # Apply generate_gri_geneonly function to generate bed files -------------- -generate_gri_v2("hg19") -generate_gri_v2("hg38") +# generate_gri_v2("hg19") +generate_gri_v2("hg38") \ No newline at end of file From 9cf9564ba56305bc42e877723952deef1fb169e7 Mon Sep 17 00:00:00 2001 From: Seon Young Kim Date: Thu, 8 May 2025 16:47:21 -0500 Subject: [PATCH 55/80] fix(NORMALIZE_VCF): handle Unicode file format --- modules/local/singleton/main.nf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 9bf54d7..d617378 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -50,7 +50,7 @@ process NORMALIZE_VCF { elif echo "\${INPUT_VCF_TYPE}" | grep -q 'gzip compressed data'; then echo "GZIP format detected, converting to BGZF." gunzip -c $vcf | bgzip > input.vcf.gz - elif echo "\${INPUT_VCF_TYPE}" | grep -q 'ASCII text'; then + elif echo "\${INPUT_VCF_TYPE}" | grep -q -e 'ASCII text' -e 'Unicode text' -e 'Variant Call Format'; then echo "Plain VCF file detected, compressing and indexing." bgzip -c $vcf > input.vcf.gz else From 6d21a68945715cfa629ba964bbfa67a8187f91af Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 25 Apr 2025 10:28:44 -0500 Subject: [PATCH 56/80] Add wildcard for work*and out* in .gitignore --- .gitignore | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 7c4b2bd..c3379da 100644 --- a/.gitignore +++ b/.gitignore @@ -14,8 +14,8 @@ test_output/ # Nextflow Related .nextflow* .nextflow/ -work/ -out/ +work*/ +out*/ trace-*.txt report-*.html timeline-*.html From c82af4b97db87a644cb6ea2e35b82febb97a59cf Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 9 May 2025 22:20:27 -0500 Subject: [PATCH 57/80] Accept input_phenopacket --- .gitignore | 1 + Dockerfile | 28 ++--------------- bin/generate_input_vcf.py | 26 ++++++++++----- conf/base.config | 4 ++- main.nf | 47 +++++++++++++++++++--------- modules/local/singleton/main.nf | 34 ++++++++++++++++++-- nextflow.config | 1 + nextflow_schema.json | 6 ++++ requirements.txt | 2 +- subworkflows/local/singleton/main.nf | 6 ++-- 10 files changed, 101 insertions(+), 54 deletions(-) diff --git a/.gitignore b/.gitignore index c3379da..bb6c39e 100644 --- a/.gitignore +++ b/.gitignore @@ -16,6 +16,7 @@ test_output/ .nextflow/ work*/ out*/ +dag-*.html trace-*.txt report-*.html timeline-*.html diff --git a/Dockerfile b/Dockerfile index a6f4524..d95e234 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,18 +1,7 @@ -# List of dependencies installed -# bcftools v1.9-1-deb_cv1 -# ensemblorg/ensembl-vep:release_104.3 -# python 2.7 -# python 3.8.13 -# R 4.2.1 - -# dc7a542b7e1f -#FROM ubuntu:18.04 -FROM ensemblorg/ensembl-vep:release_104.3 -USER root -ENV DEBIAN_FRONTEND=noninteractive +FROM python:3.8 # Install dependencies -RUN apt-get update && apt-get install -y \ +RUN apt update && apt install -y \ curl \ git \ build-essential \ @@ -22,20 +11,12 @@ RUN apt-get update && apt-get install -y \ libbz2-dev \ liblzma-dev \ tabix \ - python2.7 \ - python3.8 \ - python3.8-dev \ - python3.8-distutils \ - python3-apt - -RUN curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py -RUN python3.8 get-pip.py + jq # Install python 3.8 dependencies COPY requirements.txt /opt/requirements.txt RUN pip3 install --upgrade pip RUN pip3 install -r /opt/requirements.txt -RUN pip3 install bgzip # Install bcftools RUN curl -OL https://github.com/samtools/bcftools/releases/download/1.20/bcftools-1.20.tar.bz2 @@ -52,6 +33,3 @@ RUN tar -xf /opt/bcftools-1.20.tar.bz2 -C /opt/ && \ RUN curl -OL https://github.com/arq5x/bedtools2/releases/download/v2.30.0/bedtools.static.binary RUN mv bedtools.static.binary /run/bedtools RUN chmod a+x /run/bedtools - - - diff --git a/bin/generate_input_vcf.py b/bin/generate_input_vcf.py index 719cbbe..5393928 100755 --- a/bin/generate_input_vcf.py +++ b/bin/generate_input_vcf.py @@ -4,11 +4,11 @@ import argparse parser = argparse.ArgumentParser() -parser.add_argument("variant", type=str) +parser.add_argument("variants_file", type=str) args = parser.parse_args() -vcf_template = string.Template(""" +vcf_header = """ ##fileformat=VCFv4.2 ##FORMAT= ##FORMAT= @@ -18,18 +18,30 @@ ##FORMAT= ##FILTER= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE +""".strip() + +vcf_row_template = string.Template(""" $chrom $pos ${chrom}_${pos}_${ref}_${alt} $ref $alt . . . GT:AD:DP:GQ:PL 0/1:7,5:12:99:142,0,214 """.strip()) -def main(variant): - chrom, pos, ref, alt = variant.split("_") - with open("input.vcf", "w", encoding="ascii") as text_file: - text_file.write(vcf_template.substitute( +def main(variants_file): + with open(variants_file, "r") as f: + variants = [line.strip() for line in f if line.strip()] + + vcf_rows = [] + for variant in variants: + chrom, pos, ref, alt = variant.split("_") + vcf_row = vcf_row_template.substitute( chrom=chrom, pos=pos, ref=ref, alt=alt, - )) + ) + vcf_rows.append(vcf_row) + + with open("input.vcf", "w", encoding="ascii") as text_file: + text_file.write(vcf_header + "\n") + text_file.write("\n".join(vcf_rows)) if __name__ == "__main__": main(**vars(args)) diff --git a/conf/base.config b/conf/base.config index 9f7d470..6ab9d0c 100644 --- a/conf/base.config +++ b/conf/base.config @@ -1,4 +1,6 @@ docker.enabled = true +docker.fixOwnership = true +docker.runOptions = '-u $(id -u):$(id -g)' process { cpus = 2 @@ -6,6 +8,6 @@ process { errorStrategy = { task.exitStatus in [143,137,104,134,139,140] ? 'retry' : 'finish' } maxRetries = 5 - container = "zhandongliulab/aim-lite:1.1" + container = "zhandongliulab/aim-lite:1.2" cache = "lenient" } diff --git a/main.nf b/main.nf index 423af6e..085446b 100644 --- a/main.nf +++ b/main.nf @@ -7,7 +7,7 @@ include { } from "./modules/local/utils" include { - BUILD_REFERENCE_INDEX; GENERATE_INPUT_VCF; NORMALIZE_VCF + BUILD_REFERENCE_INDEX; PHENOPACKET_TO_VARIANTS_AND_HPOS; GENERATE_INPUT_VCF; GENERATE_INPUT_VARIANTS } from "./modules/local/singleton" include { @@ -23,8 +23,23 @@ validateParameters() addDependentParams(params) workflow { - hpo = params.input_hpo - if (params.input_ped) { + skip_preprocess_vcf_flag = false + + if (params.input_phenopacket) { + skip_preprocess_vcf_flag = true + + PHENOPACKET_TO_VARIANTS_AND_HPOS(params.input_phenopacket) + GENERATE_INPUT_VCF(PHENOPACKET_TO_VARIANTS_AND_HPOS.out.variants) + vcf = GENERATE_INPUT_VCF.out.vcf + hpo = PHENOPACKET_TO_VARIANTS_AND_HPOS.out.hpo + } else if (params.input_variant) { + skip_preprocess_vcf_flag = true + + GENERATE_INPUT_VARIANTS(params.input_variant) + GENERATE_INPUT_VCF(GENERATE_INPUT_VARIANTS.out.variants) + vcf = GENERATE_INPUT_VCF.out.vcf + hpo = params.input_hpo + } else if (params.input_ped && params.input_vcf) { BUILD_REFERENCE_INDEX() VCF_PRE_PROCESS_TRIO( file(params.input_vcf), @@ -34,26 +49,28 @@ workflow { BUILD_REFERENCE_INDEX.out.fasta_dict, params.chrmap, ) - VCF_PRE_PROCESS( - VCF_PRE_PROCESS_TRIO.out.vcf, - BUILD_REFERENCE_INDEX.out.fasta, - BUILD_REFERENCE_INDEX.out.fasta_index, - BUILD_REFERENCE_INDEX.out.fasta_dict, - ) - vcf = VCF_PRE_PROCESS.out.vcf + vcf = VCF_PRE_PROCESS_TRIO.out.vcf + hpo = params.input_hpo } else if (params.input_vcf) { + vcf = file(params.input_vcf) + hpo = params.input_hpo + } else { + error "No input VCF or phenopacket or variant provided." + } + + if (!hpo) { + hpo = file(moduleDir.resolve("./assets/NO_FILE")) + } + + if (!skip_preprocess_vcf_flag) { BUILD_REFERENCE_INDEX() VCF_PRE_PROCESS( - file(params.input_vcf), + vcf, BUILD_REFERENCE_INDEX.out.fasta, BUILD_REFERENCE_INDEX.out.fasta_index, BUILD_REFERENCE_INDEX.out.fasta_dict, ) vcf = VCF_PRE_PROCESS.out.vcf - } else if (params.input_variant) { - GENERATE_INPUT_VCF(params.input_variant) - NORMALIZE_VCF(GENERATE_INPUT_VCF.out.vcf) - vcf = NORMALIZE_VCF.out.vcf } GENERATE_SINGLETON_FEATURES(vcf, hpo) diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index d617378..e20fe67 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -1,13 +1,40 @@ +process PHENOPACKET_TO_VARIANTS_AND_HPOS { + input: + path phenopacket_json + + output: + path "${params.run_id}.variants.txt", emit: variants + path "${params.run_id}.hpos.txt", emit: hpo + + """ + jq -r '.phenotypicFeatures[].type.id' $phenopacket_json > ${params.run_id}.hpos.txt + jq -r '.interpretations[].diagnosis.genomicInterpretations[].variantInterpretation.variationDescriptor.vcfRecord | "\\(.chrom | sub("^chr";""))_\\(.pos)_\\(.ref)_\\(.alt)"' $phenopacket_json > ${params.run_id}.variants.unsorted.txt + sort -t'_' -k1,1V -k2,2n -k3,3 -k4,4 ${params.run_id}.variants.unsorted.txt > ${params.run_id}.variants.txt + """ +} + +process GENERATE_INPUT_VARIANTS { + input: + val input_variant + + output: + path "${params.run_id}.variants.txt", emit: variants + + """ + echo $input_variant > ${params.run_id}.variants.txt + """ +} process GENERATE_INPUT_VCF { input: - val id + val variants output: - path "input.vcf", emit: vcf + path "input.vcf.gz", emit: vcf """ - generate_input_vcf.py $id + generate_input_vcf.py $variants + bgzip input.vcf """ } @@ -385,6 +412,7 @@ process SPLIT_VCF_BY_CHROMOSOME { } process ANNOTATE_BY_VEP { + container 'ensemblorg/ensembl-vep:release_104.3' tag "${vcf.simpleName}" publishDir "${params.outdir}/${params.run_id}/vep/", mode: "copy" diff --git a/nextflow.config b/nextflow.config index c5cfa54..727e38c 100644 --- a/nextflow.config +++ b/nextflow.config @@ -1,5 +1,6 @@ params { input_vcf = null + input_phenopacket = null input_variant = null input_ped = null input_hpo = null diff --git a/nextflow_schema.json b/nextflow_schema.json index 4b3af7c..cbc92d9 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -17,6 +17,12 @@ "pattern": "^\\S+\\.vcf(.gz)?$", "format": "file-path" }, + "input_phenopacket": { + "type": "string", + "description": "Path to input phenopacket JSON file.", + "pattern": "^\\S+\\.json?$", + "format": "file-path" + }, "input_variant": { "type": "string", "description": "Input variant in the format 'chrom-pos-ref-alt', e.g., '1-123456-A-T'.", diff --git a/requirements.txt b/requirements.txt index b128ddd..006e382 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ pandas==1.4.3 phrank scipy -xgboost +xgboost<=2.1.4 tqdm matplotlib requests diff --git a/subworkflows/local/singleton/main.nf b/subworkflows/local/singleton/main.nf index 742a437..3f25326 100644 --- a/subworkflows/local/singleton/main.nf +++ b/subworkflows/local/singleton/main.nf @@ -62,13 +62,14 @@ workflow VCF_PRE_PROCESS { workflow PHRANK_SCORING { take: vcf + hpo main: VCF_TO_VARIANTS(vcf) VARIANTS_TO_ENSEMBL(VCF_TO_VARIANTS.out, params.ref_loc) ENSEMBL_TO_GENESYM(VARIANTS_TO_ENSEMBL.out, params.ref_to_sym) GENESYM_TO_PHRANK(ENSEMBL_TO_GENESYM.out, - file(params.input_hpo), + hpo, params.phrank_dagfile, params.phrank_disease_annotation, params.phrank_gene_annotation, @@ -112,7 +113,7 @@ workflow GENERATE_SINGLETON_FEATURES { file(params.ref_annot_dir) ) - PHRANK_SCORING(vcf) + PHRANK_SCORING(vcf, hpo) ANNOTATE_TIER ( ANNOTATE_BY_MODULES.out.scores, PHRANK_SCORING.out, @@ -133,6 +134,7 @@ workflow GENERATE_SINGLETON_FEATURES { file(params.ref_mod5_diffusion_dir), file(params.ref_merge_expand_dir) ) + emit: merged_matrix = MERGE_SCORES_BY_CHROMOSOME.out.merged_matrix merged_compressed_scores = MERGE_SCORES_BY_CHROMOSOME.out.merged_compressed_scores From 386bbf46580ff69b93146d5e9f6bb36a9ad58034 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 16 May 2025 13:50:07 -0500 Subject: [PATCH 58/80] Exclude phenotypes which are marked to be excluded for phenopacket format --- modules/local/singleton/main.nf | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index e20fe67..454adbe 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -7,7 +7,7 @@ process PHENOPACKET_TO_VARIANTS_AND_HPOS { path "${params.run_id}.hpos.txt", emit: hpo """ - jq -r '.phenotypicFeatures[].type.id' $phenopacket_json > ${params.run_id}.hpos.txt + jq -r '.phenotypicFeatures[] | select(.excluded != true) | .type.id' $phenopacket_json > ${params.run_id}.hpos.txt jq -r '.interpretations[].diagnosis.genomicInterpretations[].variantInterpretation.variationDescriptor.vcfRecord | "\\(.chrom | sub("^chr";""))_\\(.pos)_\\(.ref)_\\(.alt)"' $phenopacket_json > ${params.run_id}.variants.unsorted.txt sort -t'_' -k1,1V -k2,2n -k3,3 -k4,4 ${params.run_id}.variants.unsorted.txt > ${params.run_id}.variants.txt """ @@ -48,7 +48,7 @@ process VALIDATE_VCF { path "$vcf", emit: vcf """ - vcf-validator $vcf + # vcf-validator $vcf """ } From f4e2708c084914b754cc5607a1ab6a7516b4e85c Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 16 May 2025 13:53:03 -0500 Subject: [PATCH 59/80] fix minor --- modules/local/singleton/main.nf | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 454adbe..1be4ce3 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -48,7 +48,7 @@ process VALIDATE_VCF { path "$vcf", emit: vcf """ - # vcf-validator $vcf + vcf-validator $vcf """ } From 29c611da3ef101c77f617db04212e05a6244a424 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Tue, 20 May 2025 19:43:15 +0000 Subject: [PATCH 60/80] Accept storedir parameter --- README.md | 1 + modules/local/singleton/main.nf | 2 +- nextflow.config | 1 + 3 files changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 9b374c2..1d4def1 100644 --- a/README.md +++ b/README.md @@ -59,6 +59,7 @@ $ nextflow run main.nf --ref_dir --input_vcf --input_hpo --outdir + --storedir --bed_filter # Optional --run_id [Sample Id] # Optional, default: 1 --ref_ver [Reference genome: hg19/hg38] # Optional, default: hg19 diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index d617378..596daf5 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -87,7 +87,7 @@ process FILTER_BED { process BUILD_REFERENCE_INDEX { container "broadinstitute/gatk" - storeDir "${params.outdir}/general/reference_index/" + storeDir "${params.storedir}/general/reference_index/" output: path "final_${params.ref_ver}.fa", emit: fasta diff --git a/nextflow.config b/nextflow.config index c5cfa54..d671604 100644 --- a/nextflow.config +++ b/nextflow.config @@ -4,6 +4,7 @@ params { input_ped = null input_hpo = null outdir = "./out" + storedir = "./store" run_id = "run1" ref_ver = "hg19" From 9e5a87369420b6ad1aae0f4d0e6fb6aa0a039896 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Tue, 20 May 2025 15:50:58 -0500 Subject: [PATCH 61/80] Minor Fix. Confirmed it's working --- .gitignore | 10 +++++----- nextflow_schema.json | 5 +++++ 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index 7c4b2bd..0827f38 100644 --- a/.gitignore +++ b/.gitignore @@ -1,10 +1,9 @@ # General **/__pycache__ +**/.DS_Store +.vscode/ bcftools-1.9.tar.bz2 run/bedtools -.vscode/ -.DS_Store -*/.DS_Store test_data/ test_ref/ test_output/ @@ -14,8 +13,9 @@ test_output/ # Nextflow Related .nextflow* .nextflow/ -work/ -out/ +work*/ +out*/ +store*/ trace-*.txt report-*.html timeline-*.html diff --git a/nextflow_schema.json b/nextflow_schema.json index 4b3af7c..d4d0c99 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -37,6 +37,11 @@ "default": "./out", "description": "Output directory." }, + "storedir": { + "type": "string", + "default": "./store", + "description": "Output directory." + }, "ref_dir": { "type": "string", "description": "Path to aim pipeline dependencies directory.", From f2f108f8d401997570972a3c6cccf941ad731761 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Thu, 22 May 2025 16:06:09 -0500 Subject: [PATCH 62/80] Use Original Variant ID for Row Name --- bin/VarTierDiseaseDBFalse.R | 5 ++++- .../utils_for_marrvel_flatfile_module_3.py | 2 +- bin/extraModel/generate_bivar_data.py | 6 +++--- bin/fillna_tier.py | 10 +++++++--- bin/merge_rm.py | 3 ++- bin/mod5_diffusion.py | 6 +++--- bin/post_processing.py | 1 + bin/simple_repeat_anno.py | 13 +++++++------ bin/trio/inh_utils.py | 13 ++++++------- main.nf | 10 +++++----- modules/local/singleton/main.nf | 7 ++++--- 11 files changed, 43 insertions(+), 33 deletions(-) diff --git a/bin/VarTierDiseaseDBFalse.R b/bin/VarTierDiseaseDBFalse.R index 1e0c613..27c42fa 100755 --- a/bin/VarTierDiseaseDBFalse.R +++ b/bin/VarTierDiseaseDBFalse.R @@ -4,6 +4,7 @@ library(tidyr) library(readr) library(tibble) library(dplyr) +library(stringr) library(data.table) # set adj.k value to adjust Tier adj.k <- 1.05 @@ -22,7 +23,7 @@ genemap2.Inh.F <- readr::read_tsv(omim_inheritance) # load the input #### anno.columns <- c( - "varId_dash", + "varId", "zyg", "geneSymbol", "geneEnsId", @@ -39,6 +40,8 @@ anno.columns <- c( anno.orig <- readr::read_csv(in.f) anno <- anno.orig[, anno.columns] +anno <- anno %>% mutate(varId = str_split(varId, "_-", simplify = TRUE)[, 1]) # intergenic_variant +anno <- anno %>% mutate(varId = str_split(varId, "_E", simplify = TRUE)[, 1]) # transcript # rename the col colnames(anno) <- c( diff --git a/bin/annotation/utils_for_marrvel_flatfile_module_3.py b/bin/annotation/utils_for_marrvel_flatfile_module_3.py index 3e2d7fa..7a497ab 100755 --- a/bin/annotation/utils_for_marrvel_flatfile_module_3.py +++ b/bin/annotation/utils_for_marrvel_flatfile_module_3.py @@ -118,7 +118,7 @@ def getAnnotateInfoRow_3_1(row, genomeRef): varObj.ref = ref varObj.alt = alt varObj.varId_dash = "-".join([str(chrom), str(start), ref, alt]) - # print('varId dash:', varObj.varId_dash) + # NOTE(JL): varId_dash should have been renamed. the start value is needed for simple_repeat varId = "_".join([str(chrom), str(pos), ref, alt, transcriptId]) varObj.varId = varId if "ZYG" in row: diff --git a/bin/extraModel/generate_bivar_data.py b/bin/extraModel/generate_bivar_data.py index 2df4e4f..720f86c 100755 --- a/bin/extraModel/generate_bivar_data.py +++ b/bin/extraModel/generate_bivar_data.py @@ -94,8 +94,8 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): # Calculate variant distance recessive_feature_df['var_dist'] = ( - recessive_feature_df.varId1.str.split('-').apply(lambda x: x[1]).astype(float) - - recessive_feature_df.varId2.str.split('-').apply(lambda x: x[1]).astype(float) + recessive_feature_df.varId1.str.split('_').apply(lambda x: x[1]).astype(float) + - recessive_feature_df.varId2.str.split('_').apply(lambda x: x[1]).astype(float) ).abs() # Calculate label @@ -113,7 +113,7 @@ def process_sample(data_folder, sample_id, default_pred, labeling=False): ) # Create pair id as the legacy did - recessive_feature_df = recessive_feature_df.set_index(recessive_feature_df.varId1 + '_' + recessive_feature_df.varId2) + recessive_feature_df = recessive_feature_df.set_index(recessive_feature_df.varId1 + '-' + recessive_feature_df.varId2) # Drop the intermediate columns recessive_feature_df = recessive_feature_df.drop( diff --git a/bin/fillna_tier.py b/bin/fillna_tier.py index 3aecc4a..e3a3eec 100644 --- a/bin/fillna_tier.py +++ b/bin/fillna_tier.py @@ -21,6 +21,7 @@ def getValFromStr(valStr: str, select: str = "min"): def feature_engineering(score_file, tier_file): variable_name = [ + "varId", "varId_dash", "hgmdSymptomScore", "omimSymMatchFlag", @@ -102,10 +103,11 @@ def feature_engineering(score_file, tier_file): patient = score_file.copy() patient = patient[variable_name] + patient["varId"] = patient["varId"].str.split("_-").str[0] # integenic_variant patient = patient.fillna("-") indel_index = [ - len(i.split("-")[-1]) != 1 or len(i.split("-")[-2]) != 1 - for i in patient["varId_dash"].to_list() + len(i.split("_")[-1]) != 1 or len(i.split("_")[-2]) != 1 + for i in patient["varId"].to_list() ] patient.loc[patient["phrank"] == "-", "phrank"] = 0 @@ -921,7 +923,7 @@ def feature_engineering(score_file, tier_file): "ESP6500_EA_AF", ], ] - patient = patient.groupby(["varId_dash"], sort=False)[variable_name[1:]].max() + patient = patient.groupby(["varId"], sort=False)[variable_name[1:]].max() patient.loc[ :, [ @@ -969,6 +971,8 @@ def feature_engineering(score_file, tier_file): tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ :, ["TierAD", "TierAR", "TierAR.adj"] ] + tier["Uploaded_variation"] = tier["Uploaded_variation"].str.split('_E').str[0] + tier = tier.groupby(["Uploaded_variation"], sort=False)[tier_vars].max() tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ :, ["TierAD", "TierAR", "TierAR.adj"] diff --git a/bin/merge_rm.py b/bin/merge_rm.py index 44417ee..5c267d4 100755 --- a/bin/merge_rm.py +++ b/bin/merge_rm.py @@ -32,6 +32,7 @@ # get original coordinates annot["varId"] = annot["varId"].apply(lambda x: x.split("_E")[0]) +annot["varId"] = annot["varId"].apply(lambda x: x.split("_-")[0]) # rename for final output annot = annot.rename( @@ -39,7 +40,7 @@ ) # merge -test = df.merge(annot, right_on="varId_dash", left_on="Unnamed: 0", how="left") +test = df.merge(annot, right_on="origId", left_on="Unnamed: 0", how="left") test.to_csv( output_path, diff --git a/bin/mod5_diffusion.py b/bin/mod5_diffusion.py index ea4fe07..019b027 100755 --- a/bin/mod5_diffusion.py +++ b/bin/mod5_diffusion.py @@ -148,7 +148,7 @@ def diffuseSample(ID, Anno_df, Phrank_folder): scores = stats.rankdata(score_ordered, "max") / len(score_ordered) m12_df["diffuse_Phrank_STRING"] = scores - m12_df = m12_df.drop_duplicates(subset=["varId_dash"]) - m12_df = m12_df[["varId_dash", "diffuse_Phrank_STRING"]] - m12_df = m12_df.set_index("varId_dash", drop=True) + m12_df = m12_df.drop_duplicates(subset=["varId"]) + m12_df = m12_df[["varId", "diffuse_Phrank_STRING"]] + m12_df = m12_df.set_index("varId", drop=True) return m12_df diff --git a/bin/post_processing.py b/bin/post_processing.py index 5880e68..b86efee 100755 --- a/bin/post_processing.py +++ b/bin/post_processing.py @@ -37,6 +37,7 @@ def main(): iff = simple_repeat_anno( sys.argv[1], iff, f"merge_expand/{sys.argv[2]}/simpleRepeats.{sys.argv[2]}.bed" ) + iff = iff.drop(columns=["varId_dash"]) ### delete intermediate files ### # os.remove(sys.argv[1]+".bed") diff --git a/bin/simple_repeat_anno.py b/bin/simple_repeat_anno.py index 52b68b7..59a1c29 100755 --- a/bin/simple_repeat_anno.py +++ b/bin/simple_repeat_anno.py @@ -9,11 +9,11 @@ def simple_repeat_anno(sample_id, feature_file, sp_bed_file): sample = feature_file sample_bed = pd.DataFrame( { - "chr": sample.index.str.split("-").str[0], - "start": sample.index.str.split("-").str[1], - "end": sample.index.str.split("-").str[1], - "ref": sample.index.str.split("-").str[2], - "alt": sample.index.str.split("-").str[3], + "chr": sample.varId_dash.str.split("-").str[0], + "start": sample.varId_dash.str.split("-").str[1], + "end": sample.varId_dash.str.split("-").str[1], + "ref": sample.varId_dash.str.split("-").str[2], + "alt": sample.varId_dash.str.split("-").str[3], } ) sample_bed.to_csv("%s.bed" % (sample_id), index=False, header=False, sep="\t") @@ -37,7 +37,8 @@ def simple_repeat_anno(sample_id, feature_file, sp_bed_file): + "-" + sample_sp[4].astype(str) ) - sample["simple_repeat"] = sample.index.isin( + # NOTE(JL): This takes O(n^2) time complexity + sample["simple_repeat"] = sample.varId_dash.isin( sample_sp["var_dash"].to_numpy() ).astype(int) diff --git a/bin/trio/inh_utils.py b/bin/trio/inh_utils.py index d4d9dc3..3690cad 100644 --- a/bin/trio/inh_utils.py +++ b/bin/trio/inh_utils.py @@ -24,13 +24,13 @@ def matchID_in_inheritance(score_file, singleton_rank, inheritance_file, mode='t inh_df['varid'] = inh_df['varid'].str.replace('^chr', '', regex=True) - score_df_2 = score_df.loc[:,['varId','varId_dash','geneEnsId']].copy() + score_df_2 = score_df.loc[:,['varId','geneEnsId']].copy() score_df_2['varId'] = score_df_2['varId'].str.split('_').str[0] + '_'+\ score_df_2['varId'].str.split('_').str[1] + '_'+\ score_df_2['varId'].str.split('_').str[2] + '_'+\ score_df_2['varId'].str.split('_').str[3] score_df_2 = score_df_2.loc[~score_df_2.duplicated()] - score_df_2 = score_df_2.groupby(by='varId', as_index=False).agg({'geneEnsId' : ','.join, 'varId_dash' : 'first'}) + score_df_2 = score_df_2.groupby(by='varId', as_index=False).agg({'geneEnsId' : ','.join}) score_df_2['geneEnsId'] = score_df_2['geneEnsId'].str.replace('-','').str.rstrip(',') n_rows = score_df_2.shape[0] n_varid = len(np.unique(score_df_2['varId'])) @@ -40,17 +40,16 @@ def matchID_in_inheritance(score_file, singleton_rank, inheritance_file, mode='t score_df_2.index = score_df_2['varId'] inh_df = inh_df.loc[inh_df['varid'].isin(score_df_2['varId'])].copy() - inh_df['varId_dash'] = score_df_2.loc[inh_df['varid'], 'varId_dash'].tolist() inh_df['geneEnsId'] = score_df_2.loc[inh_df['varid'], 'geneEnsId'].tolist() - assert np.all(inh_df['varId_dash'].isin(rank_df.index)) + assert np.all(inh_df['varid'].isin(rank_df.index)) if mode == 'test': return inh_df else: - if np.all(rank_df.loc[rank_df['is_causal']==1].index.isin(inh_df['varId_dash'])): + if np.all(rank_df.loc[rank_df['is_causal']==1].index.isin(inh_df['varid'])): return inh_df else: sys.exit('%s: not all causal variants in inheritance file!'%(ID)) @@ -69,7 +68,7 @@ def matrix_inh(inh_df, rank_file): rank_df.columns = features_name # Load in inheritance file - inh_df.index = inh_df['varId_dash'] + inh_df.index = inh_df['varid'] # feature engineering inh_df.loc[inh_df['GATK']=='Inherited','GATK'] = 0 inh_df.loc[inh_df['GATK']=='loConfDeNovo','GATK'] = 1 @@ -132,7 +131,7 @@ def matrix_inh(inh_df, rank_file): rank_df['Gene_vars_Unknown'] = rank_df_new['Gene_vars_Unknown'] rank_df['No.Risk.Var'] = rank_df['No.Var.H'] + rank_df['No.Var.M'] - rank_df['chrom'] = rank_df.index.str.split('-').str[0] + rank_df['chrom'] = rank_df.index.str.split('_').str[0] rank_df['chrom'] = rank_df['chrom'].astype(int) rank_df['Homozygous_Recessive'] = ((rank_df['zyg']==2) & (rank_df['chrom']!=23)).astype(int) diff --git a/main.nf b/main.nf index 085446b..95e9940 100644 --- a/main.nf +++ b/main.nf @@ -7,7 +7,7 @@ include { } from "./modules/local/utils" include { - BUILD_REFERENCE_INDEX; PHENOPACKET_TO_VARIANTS_AND_HPOS; GENERATE_INPUT_VCF; GENERATE_INPUT_VARIANTS + BUILD_REFERENCE_INDEX; BUILD_REFERENCE_INDEX as BUILD_REFERENCE_INDEX_TRIO; PHENOPACKET_TO_VARIANTS_AND_HPOS; GENERATE_INPUT_VCF; GENERATE_INPUT_VARIANTS } from "./modules/local/singleton" include { @@ -40,13 +40,13 @@ workflow { vcf = GENERATE_INPUT_VCF.out.vcf hpo = params.input_hpo } else if (params.input_ped && params.input_vcf) { - BUILD_REFERENCE_INDEX() + BUILD_REFERENCE_INDEX_TRIO() VCF_PRE_PROCESS_TRIO( file(params.input_vcf), file(params.input_ped), - BUILD_REFERENCE_INDEX.out.fasta, - BUILD_REFERENCE_INDEX.out.fasta_index, - BUILD_REFERENCE_INDEX.out.fasta_dict, + BUILD_REFERENCE_INDEX_TRIO.out.fasta, + BUILD_REFERENCE_INDEX_TRIO.out.fasta_index, + BUILD_REFERENCE_INDEX_TRIO.out.fasta_dict, params.chrmap, ) vcf = VCF_PRE_PROCESS_TRIO.out.vcf diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 1be4ce3..7e28580 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -332,7 +332,7 @@ process HPO_SIM { container 'zhandongliulab/aim-lite-r' input: - path hpo + path hpo, name: "input_hpo.txt" path omim_hgmd_phen path omim_obo path omim_genemap2 @@ -344,11 +344,12 @@ process HPO_SIM { script: """ + cp $hpo hpo.txt if [[ -z \$(egrep 'HP:[0-9]{7}' $hpo) ]] ; then - echo "HP:0000001" > $hpo + echo "HP:0000001" > hpo.txt fi - phenoSim.R $hpo $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ + phenoSim.R hpo.txt $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ ${params.run_id}-cz ${params.run_id}-dx """ From 4642d27ec3a2b5e0b3a17b8c78c7efe79f106782 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 23 May 2025 10:38:28 -0500 Subject: [PATCH 63/80] Minor Fix --- bin/fillna_tier.py | 1 - modules/local/singleton/main.nf | 8 ++++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/bin/fillna_tier.py b/bin/fillna_tier.py index e3a3eec..72f279c 100644 --- a/bin/fillna_tier.py +++ b/bin/fillna_tier.py @@ -971,7 +971,6 @@ def feature_engineering(score_file, tier_file): tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ :, ["TierAD", "TierAR", "TierAR.adj"] ] - tier["Uploaded_variation"] = tier["Uploaded_variation"].str.split('_E').str[0] tier = tier.groupby(["Uploaded_variation"], sort=False)[tier_vars].max() tier.loc[:, ["TierAD", "TierAR", "TierAR.adj"]] = -tier.loc[ diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 6f862e5..aca91ec 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -332,7 +332,7 @@ process HPO_SIM { container 'zhandongliulab/aim-lite-r' input: - path hpo, name: "input_hpo.txt" + path hpo, name: "input.hpos.txt" path omim_hgmd_phen path omim_obo path omim_genemap2 @@ -344,12 +344,12 @@ process HPO_SIM { script: """ - cp $hpo hpo.txt + cp $hpo input.copied.hpos.txt if [[ -z \$(egrep 'HP:[0-9]{7}' $hpo) ]] ; then - echo "HP:0000001" > hpo.txt + echo "HP:0000001" > input.copied.hpos.txt fi - phenoSim.R hpo.txt $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ + phenoSim.R input.copied.hpos.txt $omim_hgmd_phen $omim_obo $omim_genemap2 $omim_pheno \\ ${params.run_id}-cz ${params.run_id}-dx """ From 29fc079cc213dd6e3e9c04e891a4f576b48f8922 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 23 May 2025 10:49:24 -0500 Subject: [PATCH 64/80] Fix Bug Issue #18 --- modules/local/trio/main.nf | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/modules/local/trio/main.nf b/modules/local/trio/main.nf index 0991b3f..e3631a5 100644 --- a/modules/local/trio/main.nf +++ b/modules/local/trio/main.nf @@ -17,10 +17,25 @@ process VCF_PRE_PROCESS_TRIO { script: """ # Extract proband, maternal, and paternal id from ped file, sample IDs in ped should be the same as in the vcf file - lastrow=\$(tail -n 1 $ped) - SAMPLE_P=\$(echo "\$lastrow" | awk '{print \$2}') - SAMPLE_F=\$(echo "\$lastrow" | awk '{print \$3}') - SAMPLE_M=\$(echo "\$lastrow" | awk '{print \$4}') + + # 1) Make sure there are exactly 3 non-empty lines + row_count=\$(awk 'NF' "$ped" | wc -l) + if [ "\$row_count" -ne 3 ]; then + echo "ERROR: Expected 3 rows in '$ped' (found \$row_count)" >&2 + exit 1 + fi + + # 2) Find the proband row (both parent fields != 0) + # PED format: 1.FAMID 2.INDIV 3.PID 4.MID 5.SEX 6.PHENOTYPE + proband_line=\$(awk '\$3 != "0" && \$4 != "0" { print; exit }' "$ped") + if [ -z "\$proband_line" ]; then + echo "ERROR: No row with both paternal (field 3) and maternal (field 4) IDs found" >&2 + exit 1 + fi + + # 3) Parse out the IDs + # We only care about fields 2,3,4 here. + read -r _ SAMPLE_P SAMPLE_F SAMPLE_M _ <<< "\$proband_line" echo "Normalize joint VCF file, split multiallelics rows" bcftools norm --multiallelics -both \ From 3eae3b4ca9ffc9935efe330ff8e0b73c55ce6aac Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 23 May 2025 14:39:05 -0500 Subject: [PATCH 65/80] Publish manifest.json including version information --- main.nf | 3 ++- modules/local/singleton/main.nf | 17 +++++++++++++++++ 2 files changed, 19 insertions(+), 1 deletion(-) diff --git a/main.nf b/main.nf index 95e9940..28a9de0 100644 --- a/main.nf +++ b/main.nf @@ -7,7 +7,7 @@ include { } from "./modules/local/utils" include { - BUILD_REFERENCE_INDEX; BUILD_REFERENCE_INDEX as BUILD_REFERENCE_INDEX_TRIO; PHENOPACKET_TO_VARIANTS_AND_HPOS; GENERATE_INPUT_VCF; GENERATE_INPUT_VARIANTS + GENERATE_MANIFEST_JSON; BUILD_REFERENCE_INDEX; BUILD_REFERENCE_INDEX as BUILD_REFERENCE_INDEX_TRIO; PHENOPACKET_TO_VARIANTS_AND_HPOS; GENERATE_INPUT_VCF; GENERATE_INPUT_VARIANTS } from "./modules/local/singleton" include { @@ -25,6 +25,7 @@ addDependentParams(params) workflow { skip_preprocess_vcf_flag = false + GENERATE_MANIFEST_JSON() if (params.input_phenopacket) { skip_preprocess_vcf_flag = true diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index aca91ec..fc5a5fc 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -1,3 +1,20 @@ +import groovy.json.JsonOutput + +process GENERATE_MANIFEST_JSON { + publishDir "${params.outdir}/${params.run_id}/manifest/", mode: 'copy' + + output: + path "manifest.json" + + script: + + """ +cat <> manifest.json +${JsonOutput.prettyPrint(JsonOutput.toJson(workflow.manifest))} +EOF + """ +} + process PHENOPACKET_TO_VARIANTS_AND_HPOS { input: path phenopacket_json From 950beb2abfa9eba77d6bfa48be4ae0324384c8fb Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 4 Jun 2025 10:19:54 -0500 Subject: [PATCH 66/80] Revert "18 vcf pre process trio requires pedigree file to have proband information at the last row" --- modules/local/trio/main.nf | 23 ++++------------------- 1 file changed, 4 insertions(+), 19 deletions(-) diff --git a/modules/local/trio/main.nf b/modules/local/trio/main.nf index e3631a5..0991b3f 100644 --- a/modules/local/trio/main.nf +++ b/modules/local/trio/main.nf @@ -17,25 +17,10 @@ process VCF_PRE_PROCESS_TRIO { script: """ # Extract proband, maternal, and paternal id from ped file, sample IDs in ped should be the same as in the vcf file - - # 1) Make sure there are exactly 3 non-empty lines - row_count=\$(awk 'NF' "$ped" | wc -l) - if [ "\$row_count" -ne 3 ]; then - echo "ERROR: Expected 3 rows in '$ped' (found \$row_count)" >&2 - exit 1 - fi - - # 2) Find the proband row (both parent fields != 0) - # PED format: 1.FAMID 2.INDIV 3.PID 4.MID 5.SEX 6.PHENOTYPE - proband_line=\$(awk '\$3 != "0" && \$4 != "0" { print; exit }' "$ped") - if [ -z "\$proband_line" ]; then - echo "ERROR: No row with both paternal (field 3) and maternal (field 4) IDs found" >&2 - exit 1 - fi - - # 3) Parse out the IDs - # We only care about fields 2,3,4 here. - read -r _ SAMPLE_P SAMPLE_F SAMPLE_M _ <<< "\$proband_line" + lastrow=\$(tail -n 1 $ped) + SAMPLE_P=\$(echo "\$lastrow" | awk '{print \$2}') + SAMPLE_F=\$(echo "\$lastrow" | awk '{print \$3}') + SAMPLE_M=\$(echo "\$lastrow" | awk '{print \$4}') echo "Normalize joint VCF file, split multiallelics rows" bcftools norm --multiallelics -both \ From e7f650e972a9eb28fb2d19e9fb187e852159bda1 Mon Sep 17 00:00:00 2001 From: Jaeyeon Lee Date: Wed, 4 Jun 2025 10:21:17 -0500 Subject: [PATCH 67/80] Revert "Revert "18 vcf pre process trio requires pedigree file to have proband information at the last row"" --- modules/local/trio/main.nf | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/modules/local/trio/main.nf b/modules/local/trio/main.nf index 0991b3f..e3631a5 100644 --- a/modules/local/trio/main.nf +++ b/modules/local/trio/main.nf @@ -17,10 +17,25 @@ process VCF_PRE_PROCESS_TRIO { script: """ # Extract proband, maternal, and paternal id from ped file, sample IDs in ped should be the same as in the vcf file - lastrow=\$(tail -n 1 $ped) - SAMPLE_P=\$(echo "\$lastrow" | awk '{print \$2}') - SAMPLE_F=\$(echo "\$lastrow" | awk '{print \$3}') - SAMPLE_M=\$(echo "\$lastrow" | awk '{print \$4}') + + # 1) Make sure there are exactly 3 non-empty lines + row_count=\$(awk 'NF' "$ped" | wc -l) + if [ "\$row_count" -ne 3 ]; then + echo "ERROR: Expected 3 rows in '$ped' (found \$row_count)" >&2 + exit 1 + fi + + # 2) Find the proband row (both parent fields != 0) + # PED format: 1.FAMID 2.INDIV 3.PID 4.MID 5.SEX 6.PHENOTYPE + proband_line=\$(awk '\$3 != "0" && \$4 != "0" { print; exit }' "$ped") + if [ -z "\$proband_line" ]; then + echo "ERROR: No row with both paternal (field 3) and maternal (field 4) IDs found" >&2 + exit 1 + fi + + # 3) Parse out the IDs + # We only care about fields 2,3,4 here. + read -r _ SAMPLE_P SAMPLE_F SAMPLE_M _ <<< "\$proband_line" echo "Normalize joint VCF file, split multiallelics rows" bcftools norm --multiallelics -both \ From f7c8ab9ee7f8cba23b53f8d973a16d0146339234 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Mon, 16 Jun 2025 14:27:59 -0500 Subject: [PATCH 68/80] Ignore Low Impact Transcripts --- bin/feature.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/bin/feature.py b/bin/feature.py index b1cd200..7318429 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -260,6 +260,10 @@ def main(): varDf = pd.read_csv( args.varFile, sep="\t", skiprows=numHeaderSkip, ) + + # Ignore Low Impact Trascripts. + varDf = varDf.groupby("#Uploaded_variation", group_keys=False).apply(lambda g: g[g.IMPACT.isin(["HIGH", "MODERATE"])] if g.IMPACT.isin(["HIGH", "MODERATE"]).any() else g).reset_index(drop=True) + # varDf=varDf[0:10] # #do this if need to have a small test print("shape:", varDf.shape) From 591bc56b3e5c90abd41266fdc6da055ce6dd7388 Mon Sep 17 00:00:00 2001 From: ZaahidShaik Date: Thu, 26 Jun 2025 10:20:40 -0500 Subject: [PATCH 69/80] Save git hash and data_dependencies hash in result directory. --- main.nf | 2 +- modules/local/singleton/main.nf | 13 +++++++++++++ utils/GRI_bed_filter/README.md | 2 +- 3 files changed, 15 insertions(+), 2 deletions(-) diff --git a/main.nf b/main.nf index 28a9de0..9c1ef9d 100644 --- a/main.nf +++ b/main.nf @@ -25,7 +25,7 @@ addDependentParams(params) workflow { skip_preprocess_vcf_flag = false - GENERATE_MANIFEST_JSON() + GENERATE_MANIFEST_JSON(params.ref_dir) if (params.input_phenopacket) { skip_preprocess_vcf_flag = true diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index fc5a5fc..42ddb49 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -3,8 +3,14 @@ import groovy.json.JsonOutput process GENERATE_MANIFEST_JSON { publishDir "${params.outdir}/${params.run_id}/manifest/", mode: 'copy' + input: + path ref_dir + output: path "manifest.json" + path "git.hash" + path "data_dependencies_tree.hash" + path "data_dependencies_tree.txt" script: @@ -12,6 +18,13 @@ process GENERATE_MANIFEST_JSON { cat <> manifest.json ${JsonOutput.prettyPrint(JsonOutput.toJson(workflow.manifest))} EOF + + (cd $ref_dir && find . -type f -printf '%p\\t%s\\n') \\ + | sort \\ + | tee data_dependencies_tree.txt \\ + | md5sum >> data_dependencies_tree.hash + + git -C ${projectDir} rev-parse HEAD >> git.hash """ } diff --git a/utils/GRI_bed_filter/README.md b/utils/GRI_bed_filter/README.md index fbe52d8..f66004c 100644 --- a/utils/GRI_bed_filter/README.md +++ b/utils/GRI_bed_filter/README.md @@ -20,4 +20,4 @@ ##### BED file format: 1. chr \| start \| end\ -2. The 1st column contains prefix of "chr" for chromosomes, for example, "chr1, chr2, .... chr Y". +2. The 1st column contains prefix of "chr" for chromosomes, for example, "chr1, chr2, .... chr Y". \ No newline at end of file From ac86eda822e33b30300e29777b3371f07c086c1d Mon Sep 17 00:00:00 2001 From: ZaahidShaik Date: Fri, 27 Jun 2025 15:28:07 -0500 Subject: [PATCH 70/80] Add input parameter to enable impact filter. --- bin/feature.py | 11 ++++++++--- modules/local/singleton/main.nf | 3 ++- nextflow.config | 1 + nextflow_schema.json | 4 ++++ 4 files changed, 15 insertions(+), 4 deletions(-) diff --git a/bin/feature.py b/bin/feature.py index 7318429..01c699f 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -88,6 +88,9 @@ def main(): parser.add_argument( "-genomeRef", "--genomeRef", help="Proivde genome ref: hg19, hg38" ) + parser.add_argument( + "-enableLIT", "--enableLIT", help="Boolean flag to ignore/enable low impact transcripts" + ) args = parser.parse_args() # check the user args checkUserArgs(args) @@ -96,6 +99,7 @@ def main(): print("modules:", args.modules) moduleList = args.modules.split(",") print("modules list:", moduleList) + print("low impact transcripts enabled: ", args.enableLIT) # start time start_time = time.time() @@ -260,9 +264,10 @@ def main(): varDf = pd.read_csv( args.varFile, sep="\t", skiprows=numHeaderSkip, ) - - # Ignore Low Impact Trascripts. - varDf = varDf.groupby("#Uploaded_variation", group_keys=False).apply(lambda g: g[g.IMPACT.isin(["HIGH", "MODERATE"])] if g.IMPACT.isin(["HIGH", "MODERATE"]).any() else g).reset_index(drop=True) + + # Ignore Low Impact Transcripts (LIT). + if str(args.enableLIT).lower() == 'true': + varDf = varDf.groupby("#Uploaded_variation", group_keys=False).apply(lambda g: g[g.IMPACT.isin(["HIGH", "MODERATE"])] if g.IMPACT.isin(["HIGH", "MODERATE"]).any() else g).reset_index(drop=True) # varDf=varDf[0:10] # #do this if need to have a small test diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index aca91ec..7ae9118 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -477,7 +477,8 @@ process ANNOTATE_BY_MODULES { -patientFileType one \\ -genomeRef ${params.ref_ver} \\ -diseaseInh AD \\ - -modules curate,conserve + -modules curate,conserve \\ + -enableLIT ${params.impact_filter} mv scores.csv ${vep.baseName}_scores.csv """ diff --git a/nextflow.config b/nextflow.config index d88352f..ebb201e 100644 --- a/nextflow.config +++ b/nextflow.config @@ -12,6 +12,7 @@ params { ref_dir = null bed_filter = null exome_filter = false + impact_filter = false help = false version = false diff --git a/nextflow_schema.json b/nextflow_schema.json index 724c43c..af790b9 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -71,6 +71,10 @@ "exome_filter": { "type": "boolean", "description": "Enable exonic filter. Addition will filter out variants outside of exonic region" + }, + "impact_filter": { + "type": "boolean", + "description": "Enable low impact. Addition will enable low impact transcripts." } }, "required": ["outdir", "ref_dir", "run_id", "ref_ver"] From 640e905f667e7394b312bb666396eab3ddbb980b Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 27 Jun 2025 16:33:52 -0500 Subject: [PATCH 71/80] Make it boolean and Rename to transcriptDf --- bin/feature.py | 36 ++++++++++++++++++--------------- modules/local/singleton/main.nf | 16 +++++++-------- 2 files changed, 28 insertions(+), 24 deletions(-) diff --git a/bin/feature.py b/bin/feature.py index 01c699f..fb81967 100755 --- a/bin/feature.py +++ b/bin/feature.py @@ -89,8 +89,12 @@ def main(): "-genomeRef", "--genomeRef", help="Proivde genome ref: hg19, hg38" ) parser.add_argument( - "-enableLIT", "--enableLIT", help="Boolean flag to ignore/enable low impact transcripts" + "-enableLIT", "--enableLIT", + action="store_true", + default=False, + help="Enable low‑impact transcripts" ) + args = parser.parse_args() # check the user args checkUserArgs(args) @@ -261,33 +265,33 @@ def main(): break print("input annoatated varFile:", args.varFile) t1 = time.time() - varDf = pd.read_csv( + transcriptDf = pd.read_csv( args.varFile, sep="\t", skiprows=numHeaderSkip, ) # Ignore Low Impact Transcripts (LIT). - if str(args.enableLIT).lower() == 'true': - varDf = varDf.groupby("#Uploaded_variation", group_keys=False).apply(lambda g: g[g.IMPACT.isin(["HIGH", "MODERATE"])] if g.IMPACT.isin(["HIGH", "MODERATE"]).any() else g).reset_index(drop=True) + if args.enableLIT: + transcriptDf = transcriptDf.groupby("#Uploaded_variation", group_keys=False).apply(lambda g: g[g.IMPACT.isin(["HIGH", "MODERATE"])] if g.IMPACT.isin(["HIGH", "MODERATE"]).any() else g).reset_index(drop=True) - # varDf=varDf[0:10] + # transcriptDf=transcriptDf[0:10] # #do this if need to have a small test - print("shape:", varDf.shape) + print("shape:", transcriptDf.shape) t2 = time.time() inputReadTime = t2 - t1 - inputNumRows = len(varDf.index) + inputNumRows = len(transcriptDf.index) # TODO: change with `.rename` - if "GERP++_RS" in varDf.columns: + if "GERP++_RS" in transcriptDf.columns: print("found GERP++RS") - varDf["GERPpp_RS"] = varDf["GERP++_RS"] - if "GERP++_NR" in varDf.columns: + transcriptDf["GERPpp_RS"] = transcriptDf["GERP++_RS"] + if "GERP++_NR" in transcriptDf.columns: print("found GERP++NR") - varDf["GERPpp_NR"] = varDf["GERP++_NR"] + transcriptDf["GERPpp_NR"] = transcriptDf["GERP++_NR"] # update column names which have - in it - if "fathmm-MKL_coding_score" in varDf.columns: - varDf["fathmm_MKL_coding_score"] = varDf["fathmm-MKL_coding_score"] - if "M-CAP_score" in varDf.columns: - varDf["M_CAP_score"] = varDf["M-CAP_score"] + if "fathmm-MKL_coding_score" in transcriptDf.columns: + transcriptDf["fathmm_MKL_coding_score"] = transcriptDf["fathmm-MKL_coding_score"] + if "M-CAP_score" in transcriptDf.columns: + transcriptDf["M_CAP_score"] = transcriptDf["M-CAP_score"] if "conserve" in moduleList: decipherSortedDf = decipherDf.set_index(['Chr', 'Start', 'Stop']).sort_index() @@ -299,7 +303,7 @@ def main(): hgmdHPOScoreAccSortedDf = hgmdHPOScoreDf.groupby('acc_num').first().sort_index() annotateInfoDf = getAnnotateInfoRows_3( - varDf, + transcriptDf, args.genomeRef, clinvarGeneDf, clinvarAlleleDf, diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 7ae9118..af42413 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -470,17 +470,17 @@ process ANNOTATE_BY_MODULES { script: """ feature.py \\ - -patientHPOsimiOMIM $omim_sim \\ - -patientHPOsimiHGMD $hgmd_sim \\ - -varFile ${vep} \\ - -inFileType vepAnnotTab \\ - -patientFileType one \\ - -genomeRef ${params.ref_ver} \\ + ${params.impact_filter ? "-enableLIT" : ""} \\ -diseaseInh AD \\ -modules curate,conserve \\ - -enableLIT ${params.impact_filter} + -inFileType vepAnnotTab \\ + -patientFileType one \\ + -patientHPOsimiOMIM ${omim_sim} \\ + -patientHPOsimiHGMD ${hgmd_sim} \\ + -varFile ${vep} \\ + -genomeRef ${params.ref_ver} - mv scores.csv ${vep.baseName}_scores.csv + mv scores.csv ${vep.baseName}_scores.csv """ } From 589ff286856be4fa554da1484d5845ddc31eb418 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Fri, 27 Jun 2025 16:43:12 -0500 Subject: [PATCH 72/80] Apply lenient validation to accept integer run_id --- conf/base.config | 2 ++ nextflow_schema.json | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/conf/base.config b/conf/base.config index 6ab9d0c..0459025 100644 --- a/conf/base.config +++ b/conf/base.config @@ -2,6 +2,8 @@ docker.enabled = true docker.fixOwnership = true docker.runOptions = '-u $(id -u):$(id -g)' +validation.lenientMode = true + process { cpus = 2 memory = { 25.GB * task.attempt } diff --git a/nextflow_schema.json b/nextflow_schema.json index 724c43c..e15704d 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -55,7 +55,7 @@ }, "run_id": { "type": "string", - "default": "run1", + "default": "1", "description": "Unique identifier for this run." }, "ref_ver": { From 3e8cee5647a74a3bf1d279222cd5d36091904540 Mon Sep 17 00:00:00 2001 From: ZaahidShaik Date: Mon, 7 Jul 2025 10:48:11 -0500 Subject: [PATCH 73/80] Remove *_integrated.csv --- bin/extraModel_main.py | 7 ------- modules/local/singleton/main.nf | 1 - 2 files changed, 8 deletions(-) diff --git a/bin/extraModel_main.py b/bin/extraModel_main.py index 76dde9f..776bb52 100755 --- a/bin/extraModel_main.py +++ b/bin/extraModel_main.py @@ -157,13 +157,6 @@ def AIM(data_folder, sample_id): else: # AIM found no recessive variant pair pass - - print(f"Integrating all information for sample {sample_id}...") - ######### Construction Paused here ######### - integrated_df = integrate_output(out_folder, data_folder, sample_id) - if not os.path.exists(f"{out_folder}/integrated"): - os.mkdir(f"{out_folder}/integrated") - integrated_df.to_csv(f"{out_folder}/integrated/{sample_id}_integrated.csv") return diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index 9b5fe7d..bd91bc9 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -606,7 +606,6 @@ process PREDICTION { path "conf_4Model/${params.run_id}_recessive_predictions.csv", optional: true path "conf_4Model/${params.run_id}_nd_predictions.csv", optional: true path "conf_4Model/${params.run_id}_nd_recessive_predictions.csv", optional: true - path "conf_4Model/integrated/*.csv" path "final_matrix_expanded/*.csv.gz" path "shap_outputs" From 2a9774b39c23de769ec871863e7930b2dc98082f Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Wed, 16 Jul 2025 12:16:29 -0500 Subject: [PATCH 74/80] Substitute extensive params with PREPARE_DATA subworkflow --- .gitignore | 21 +-- main.nf | 69 ++++------ modules/local/prepare_data/main.nf | 173 ++++++++++++++++++++++++ modules/local/singleton/main.nf | 26 +--- modules/local/utils/main.nf | 68 ---------- nextflow.config | 1 + nextflow_schema.json | 7 + subworkflows/local/handle_input/main.nf | 32 +++++ subworkflows/local/prepare_data/main.nf | 61 +++++++++ subworkflows/local/singleton/main.nf | 120 +++++++++------- 10 files changed, 380 insertions(+), 198 deletions(-) create mode 100644 modules/local/prepare_data/main.nf create mode 100644 subworkflows/local/handle_input/main.nf create mode 100644 subworkflows/local/prepare_data/main.nf diff --git a/.gitignore b/.gitignore index fb93515..0311b94 100644 --- a/.gitignore +++ b/.gitignore @@ -9,18 +9,19 @@ test_ref/ test_output/ **/.ipynb_checkpoints/ *.ipynb +.nfs* # Nextflow Related -.nextflow* -.nextflow/ -work*/ -out*/ -store*/ -dag-*.html -trace-*.txt -report-*.html -timeline-*.html -params.yaml +/.nextflow* +/.nextflow/ +/work*/ +/out*/ +/store*/ +/dag-*.html +/trace-*.txt +/report-*.html +/timeline-*.html +/params.yaml # Ignore R history and session files .Rhistory diff --git a/main.nf b/main.nf index 9c1ef9d..217df55 100644 --- a/main.nf +++ b/main.nf @@ -3,82 +3,61 @@ nextflow.enable.dsl = 2 include { validateParameters } from 'plugin/nf-schema' include { - showVersion; addDependentParams + showVersion } from "./modules/local/utils" -include { - GENERATE_MANIFEST_JSON; BUILD_REFERENCE_INDEX; BUILD_REFERENCE_INDEX as BUILD_REFERENCE_INDEX_TRIO; PHENOPACKET_TO_VARIANTS_AND_HPOS; GENERATE_INPUT_VCF; GENERATE_INPUT_VARIANTS -} from "./modules/local/singleton" - include { VCF_PRE_PROCESS_TRIO; GENERATE_TRIO_FEATURES; PREDICTION_TRIO } from "./modules/local/trio" +include { + PREPARE_DATA +} from "./subworkflows/local/prepare_data" + +include { + HANDLE_INPUT +} from "./subworkflows/local/handle_input" + include { VCF_PRE_PROCESS; GENERATE_SINGLETON_FEATURES; PREDICTION } from "./subworkflows/local/singleton" showVersion() validateParameters() -addDependentParams(params) workflow { - skip_preprocess_vcf_flag = false + data = PREPARE_DATA() + chrmap_file = data.map { it.chrmap_file } + ref_model_inputs_dir = data.map { it.ref_model_inputs_dir } + fasta_tuple = data.map { it.fasta_tuple } - GENERATE_MANIFEST_JSON(params.ref_dir) - if (params.input_phenopacket) { - skip_preprocess_vcf_flag = true + (vcf, hpo) = HANDLE_INPUT() - PHENOPACKET_TO_VARIANTS_AND_HPOS(params.input_phenopacket) - GENERATE_INPUT_VCF(PHENOPACKET_TO_VARIANTS_AND_HPOS.out.variants) - vcf = GENERATE_INPUT_VCF.out.vcf - hpo = PHENOPACKET_TO_VARIANTS_AND_HPOS.out.hpo - } else if (params.input_variant) { - skip_preprocess_vcf_flag = true - - GENERATE_INPUT_VARIANTS(params.input_variant) - GENERATE_INPUT_VCF(GENERATE_INPUT_VARIANTS.out.variants) - vcf = GENERATE_INPUT_VCF.out.vcf - hpo = params.input_hpo - } else if (params.input_ped && params.input_vcf) { - BUILD_REFERENCE_INDEX_TRIO() + if (params.input_ped) { VCF_PRE_PROCESS_TRIO( - file(params.input_vcf), + vcf, file(params.input_ped), - BUILD_REFERENCE_INDEX_TRIO.out.fasta, - BUILD_REFERENCE_INDEX_TRIO.out.fasta_index, - BUILD_REFERENCE_INDEX_TRIO.out.fasta_dict, - params.chrmap, + fasta_tuple.map { it[0] }, + fasta_tuple.map { it[1] }, + fasta_tuple.map { it[2] }, + chrmap_file, ) vcf = VCF_PRE_PROCESS_TRIO.out.vcf - hpo = params.input_hpo - } else if (params.input_vcf) { - vcf = file(params.input_vcf) - hpo = params.input_hpo - } else { - error "No input VCF or phenopacket or variant provided." - } - - if (!hpo) { - hpo = file(moduleDir.resolve("./assets/NO_FILE")) } - if (!skip_preprocess_vcf_flag) { - BUILD_REFERENCE_INDEX() + if (!params.input_variant && !params.input_phenopacket) { VCF_PRE_PROCESS( vcf, - BUILD_REFERENCE_INDEX.out.fasta, - BUILD_REFERENCE_INDEX.out.fasta_index, - BUILD_REFERENCE_INDEX.out.fasta_dict, + data, ) vcf = VCF_PRE_PROCESS.out.vcf } - GENERATE_SINGLETON_FEATURES(vcf, hpo) + GENERATE_SINGLETON_FEATURES(vcf, hpo, data) PREDICTION( GENERATE_SINGLETON_FEATURES.out.merged_matrix, GENERATE_SINGLETON_FEATURES.out.merged_compressed_scores, - file(params.ref_model_inputs_dir) + ref_model_inputs_dir, ) if (params.input_ped) { diff --git a/modules/local/prepare_data/main.nf b/modules/local/prepare_data/main.nf new file mode 100644 index 0000000..9b12ee9 --- /dev/null +++ b/modules/local/prepare_data/main.nf @@ -0,0 +1,173 @@ +process RESTRUCTURE_LOCAL_DATA_EXCEPT_VEP { + input: + path local_data_path + + output: + path "*" + + """ + find $local_data_path -follow -mindepth 1 -maxdepth 1 -not -name "vep" -exec ln -s {} . \\; + """ +} + +process RESTRUCTURE_LOCAL_DATA_ONLY_VEP { + input: + path local_data_path + + output: + path "vep" + + """ + ln -s $local_data_path/vep . + """ +} + +process STORE_S3_BUCKET_DATA_EXCEPT_VEP { + tag "Takes 5 minutes to run" + storeDir "./${params.storedir}/s3_bucket_data_except_vep/${params.s3_bucket_data_name}" + + output: + path "*" + + """ + aws s3 sync s3://${params.s3_bucket_data_name} . --exclude "vep/*" + """ +} + +process STORE_S3_BUCKET_DATA_ONLY_VEP { + storeDir "./${params.storedir}/s3_bucket_data_only_vep/v0" + + input: + val s3_bucket_data_name + + output: + path "vep" + + // NOTE(JL): The path may be changed to s3://aim-data-dependencies-public-vep-v0 in future + """ + aws s3 sync s3://$s3_bucket_data_name/vep ./vep + """ +} + +process SPLIT_DATA { + input: + path "data_except_vep/*" + path "data_only_vep/*" + path bed_filter_file // NOTE(JL): Not tested yet + val exome_filter // NOTE(JL): Not tested yet + + output: + path "data_except_vep/bcf_annotate/chrmap.txt", emit: chrmap_file + path "ref_filter.bed", emit: ref_filter_bed + + path "data_except_vep/annotate", emit: ref_annot_dir + path "data_except_vep/var_tier", emit: ref_var_tier_dir + path "data_except_vep/merge_expand", emit: ref_merge_expand_dir + path "data_except_vep/mod5_diffusion", emit: ref_mod5_diffusion_dir + path "data_except_vep/predict_new", emit: ref_predict_new_dir + path "data_except_vep/model_inputs", emit: ref_model_inputs_dir + + // for phrank + tuple( + path("ref_loc.txt"), // ref_loc + path("data_except_vep/phrank/${params.ref_ver}/ensembl_to_symbol.txt"), // ref_to_sym + + path("data_except_vep/phrank/${params.ref_ver}/child_to_parent.txt"), // dagfile + path("data_except_vep/phrank/${params.ref_ver}/disease_to_pheno.txt"), // disease_annotation + path("data_except_vep/phrank/${params.ref_ver}/gene_to_phenotype.txt"), // gene_annotation + path("data_except_vep/phrank/${params.ref_ver}/disease_to_gene.txt"), // disease_gene + emit: phrank_tuple + ) + + // OMIM and HPO + tuple( + path("data_except_vep/omim_annotate/${params.ref_ver}/HGMD_phen.tsv"), // omim_hgmd_phen + path("data_except_vep/omim_annotate/hp.obo"), // omim_obo + path("data_except_vep/omim_annotate/${params.ref_ver}/genemap2_v2022.rds"), // omim_genemap2 + path("data_except_vep/omim_annotate/${params.ref_ver}/HPO_OMIM.tsv"), // omim_pheno + emit: omim_tuple + ) + + // GNOMAD VCF + tuple( + path("data_except_vep/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.genomes.vcf.gz"), // ref_gnomad_genome + path("data_except_vep/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.genomes.vcf.gz.tbi"), // ref_gnomad_genome_idx + path("data_except_vep/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.exomes.vcf.gz"), // ref_gnomad_exome + path("data_except_vep/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.exomes.vcf.gz.tbi"), // ref_gnomad_exome_idx + emit: gnomad_tuple + ) + + // VEP + tuple( + path("data_only_vep/vep/${params.ref_ver}/"), // vep_dir_cache + path("data_only_vep/vep/${params.ref_ver}/Plugins/"), // vep_dir_plugins + path("gnomad.genomes*.vcf.gz", arity: 1), // vep_custom_gnomad + path("data_only_vep/vep/${params.ref_ver}/clinvar_20220730.vcf.gz"), // vep_custom_clinvar + path("data_only_vep/vep/${params.ref_ver}/HGMD_Pro_2022.2_${params.ref_ver}.vcf.gz"), // vep_custom_hgmd + path("new_tabbed_revel*.tsv.gz", arity: 1), // vep_plugin_revel + path("data_only_vep/vep/${params.ref_ver}/spliceai_scores.masked.snv.${params.ref_ver}.vcf.gz"), // vep_plugin_spliceai_snv + path("data_only_vep/vep/${params.ref_ver}/spliceai_scores.masked.indel.${params.ref_ver}.vcf.gz"), // vep_plugin_spliceai_indel + path("*_whole_genome_*.tsv.gz", arity: 1), // vep_plugin_cadd + path("dbNSFP*.gz", arity: 1), // vep_plugin_dbnsfp + path("data_only_vep/vep/${params.ref_ver}/*.tbi"), // vep_idx + emit: vep_tuple + ) + + script: + """ + # tree . + + ref_assembly=\$( [ "${params.ref_ver}" = "hg19" ] && echo "grch37" || echo "grch38" ) + ln -s data_except_vep/phrank/${params.ref_ver}/\${ref_assembly}_symbol_to_location.txt ./ref_loc.txt + + if [[ -s "${bed_filter_file}" ]]; then # Check if the bed filter file is not empty. + ln -s "${bed_filter_file}" ref_filter.bed + elif [[ "${exome_filter}" == "true" ]]; then # Check if the exome filter is true. + ln -s "./data_except_vep/filter_exonic/${params.ref_ver}.bed" ref_filter.bed + else + touch ref_filter.bed + fi + + # VEP + if [[ "${params.ref_ver}" == "hg19" ]]; then + vep_gnomad_name="gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" + vep_cadd_name="hg19_whole_genome_SNVs.tsv.gz" + vep_dbnsfp_name="dbNSFP4.3a_grch37.gz" + vep_revel_name="new_tabbed_revel.tsv.gz" + else + vep_gnomad_name="gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" + vep_cadd_name="hg38_whole_genome_SNV.tsv.gz" + vep_dbnsfp_name="dbNSFP4.1a_grch38.gz" + vep_revel_name="new_tabbed_revel_grch38.tsv.gz" + fi + + ln -s "./data_only_vep/vep/${params.ref_ver}/\${vep_gnomad_name}" . + ln -s "./data_only_vep/vep/${params.ref_ver}/\${vep_cadd_name}" . + ln -s "./data_only_vep/vep/${params.ref_ver}/\${vep_dbnsfp_name}" . + ln -s "./data_only_vep/vep/${params.ref_ver}/\${vep_revel_name}" . + + """ +} + +process BUILD_REFERENCE_INDEX { + container "broadinstitute/gatk" + storeDir "${params.storedir}/general/reference_index/" + + output: + tuple( + path("final_${params.ref_ver}.fa"), + path("final_${params.ref_ver}.fa.fai"), + path("final_${params.ref_ver}.dict"), + emit: fasta_tuple + ) + + script: + """ + wget --quiet http://hgdownload.soe.ucsc.edu/goldenPath/${params.ref_ver}/bigZips/${params.ref_ver}.fa.gz + gunzip ${params.ref_ver}.fa.gz + sed 's/>chr/>/g' ${params.ref_ver}.fa > num_prefix_${params.ref_ver}.fa + samtools faidx num_prefix_${params.ref_ver}.fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y M > final_${params.ref_ver}.fa + samtools faidx final_${params.ref_ver}.fa + gatk CreateSequenceDictionary -R final_${params.ref_ver}.fa + """ +} diff --git a/modules/local/singleton/main.nf b/modules/local/singleton/main.nf index bd91bc9..2727458 100644 --- a/modules/local/singleton/main.nf +++ b/modules/local/singleton/main.nf @@ -131,7 +131,7 @@ process FILTER_BED { script: """ - if [ -f ${ref_filter_bed} ]; then + if [[ -s ${ref_filter_bed} ]]; then # Check if the bed filter file is not empty. awk '{gsub(/^chr/, ""); print}' ${ref_filter_bed} > bed bcftools filter --regions-file bed ${vcf} -Oz -o "${params.run_id}.recode.vcf.gz" tabix -p vcf "${params.run_id}.recode.vcf.gz" @@ -142,26 +142,6 @@ process FILTER_BED { """ } -process BUILD_REFERENCE_INDEX { - container "broadinstitute/gatk" - storeDir "${params.storedir}/general/reference_index/" - - output: - path "final_${params.ref_ver}.fa", emit: fasta - path "final_${params.ref_ver}.fa.fai", emit: fasta_index - path "final_${params.ref_ver}.dict", emit: fasta_dict - - script: - """ - wget --quiet http://hgdownload.soe.ucsc.edu/goldenPath/${params.ref_ver}/bigZips/${params.ref_ver}.fa.gz - gunzip ${params.ref_ver}.fa.gz - sed 's/>chr/>/g' ${params.ref_ver}.fa > num_prefix_${params.ref_ver}.fa - samtools faidx num_prefix_${params.ref_ver}.fa 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y M > final_${params.ref_ver}.fa - samtools faidx final_${params.ref_ver}.fa - gatk CreateSequenceDictionary -R final_${params.ref_ver}.fa - """ -} - process CONVERT_GVCF { container "broadinstitute/gatk" @@ -303,14 +283,14 @@ process VCF_TO_VARIANTS { process VARIANTS_TO_ENSEMBL { input: path var - path ref + path ref_loc output: path "${params.run_id}-ensmbl.txt" script: """ - location_to_gene.py $var $ref | \\ + location_to_gene.py $var $ref_loc | \\ sed 's/:/\\t/g' | sed 's/X\\t/23\\t/g' | sed 's/Y\\t/24\\t/g' | \\ sed 's/MT\\t/25\\t/g' > ${params.run_id}-ensmbl.txt """ diff --git a/modules/local/utils/main.nf b/modules/local/utils/main.nf index 4af9bf7..1074741 100644 --- a/modules/local/utils/main.nf +++ b/modules/local/utils/main.nf @@ -6,71 +6,3 @@ def showVersion() { println workflow.manifest.version exit 0 } - -def addDependentParams(params) { - params.ref_assembly = params.ref_ver == "hg19" ? "grch37" : "grch38" - - // for data dependency - params.chrmap = "${params.ref_dir}/bcf_annotate/chrmap.txt" - - params.ref_loc = "${params.ref_dir}/phrank/${params.ref_ver}/${params.ref_assembly}_symbol_to_location.txt" - params.ref_to_sym = "${params.ref_dir}/phrank/${params.ref_ver}/ensembl_to_symbol.txt" - - // FILTER BED - // EXONIC FILTER BED - params.ref_exonic_filter_bed = "${params.ref_dir}/filter_exonic/${params.ref_ver}.bed" - if (params.bed_filter) { - params.ref_filter_bed = params.bed_filter - } else if (params.exome_filter) { - params.ref_filter_bed = params.ref_exonic_filter_bed - } else { - params.ref_filter_bed = moduleDir.resolve("./assets/NO_FILE") - } - - // for phrank - params.phrank_dagfile = "${params.ref_dir}/phrank/${params.ref_ver}/child_to_parent.txt" - params.phrank_disease_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_pheno.txt" - params.phrank_gene_annotation = "${params.ref_dir}/phrank/${params.ref_ver}/gene_to_phenotype.txt" - params.phrank_disease_gene = "${params.ref_dir}/phrank/${params.ref_ver}/disease_to_gene.txt" - - // OMIM and HPO - params.omim_hgmd_phen = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HGMD_phen.tsv" - params.omim_obo = "${params.ref_dir}/omim_annotate/hp.obo" - params.omim_genemap2 = "${params.ref_dir}/omim_annotate/${params.ref_ver}/genemap2_v2022.rds" - params.omim_pheno = "${params.ref_dir}/omim_annotate/${params.ref_ver}/HPO_OMIM.tsv" - - // GNOMAD VCF - params.ref_gnomad_genome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.genomes.vcf.gz" - params.ref_gnomad_genome_idx = "${params.ref_gnomad_genome}.tbi" - params.ref_gnomad_exome = "${params.ref_dir}/filter_vep/${params.ref_ver}/gnomad.${params.ref_ver}.blacklist.exomes.vcf.gz" - params.ref_gnomad_exome_idx = "${params.ref_gnomad_exome}.tbi" - - // VEP - params.vep_dbnsfp_name = params.ref_ver == "hg19" ? "dbNSFP4.3a_grch37.gz" : "dbNSFP4.1a_grch38.gz" - params.vep_gnomad_name = params.ref_ver == "hg19" ? "gnomad.genomes.r2.1.sites.grch37_noVEP.vcf.gz" : "gnomad.genomes.GRCh38.v3.1.2.sites.vcf.gz" - params.vep_cadd_name = params.ref_ver == "hg19" ? "hg19_whole_genome_SNVs.tsv.gz" : "hg38_whole_genome_SNV.tsv.gz" - - params.vep_dir_cache = "${params.ref_dir}/vep/${params.ref_ver}/" - params.vep_dir_plugins = "${params.ref_dir}/vep/${params.ref_ver}/Plugins/" - params.vep_custom_gnomad = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_gnomad_name}" - params.vep_custom_clinvar = "${params.ref_dir}/vep/${params.ref_ver}/clinvar_20220730.vcf.gz" - params.vep_custom_hgmd = "${params.ref_dir}/vep/${params.ref_ver}/HGMD_Pro_2022.2_${params.ref_ver}.vcf.gz" - params.vep_plugin_revel = "${params.ref_dir}/vep/${params.ref_ver}/new_tabbed_revel_${params.ref_assembly}.tsv.gz" // changed for hg19 - params.vep_plugin_spliceai_snv = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.snv.${params.ref_ver}.vcf.gz" - params.vep_plugin_spliceai_indel = "${params.ref_dir}/vep/${params.ref_ver}/spliceai_scores.masked.indel.${params.ref_ver}.vcf.gz" - params.vep_plugin_cadd = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_cadd_name}" // changed for hg19 - params.vep_plugin_dbnsfp = "${params.ref_dir}/vep/${params.ref_ver}/${params.vep_dbnsfp_name}" - params.vep_idx = "${params.ref_dir}/vep/${params.ref_ver}/*.tbi" - - params.ref_annot_dir = "${params.ref_dir}/annotate" - params.ref_var_tier_dir = "${params.ref_dir}/var_tier" - params.ref_merge_expand_dir = "${params.ref_dir}/merge_expand" - params.ref_mod5_diffusion_dir = "${params.ref_dir}/mod5_diffusion" - params.ref_predict_new_dir = "${params.ref_dir}/predict_new" - params.ref_model_inputs_dir = "${params.ref_dir}/model_inputs" - - // Documentation - params.usage_file = "${projectDir}/docs/source/nf_usage.txt" - params.script_chunking = "${projectDir}/scripts/split_chunks.py" - params.script_annot = "${projectDir}/scripts/annotation/*.py" -} diff --git a/nextflow.config b/nextflow.config index ebb201e..5ed334a 100644 --- a/nextflow.config +++ b/nextflow.config @@ -10,6 +10,7 @@ params { run_id = "run1" ref_ver = "hg19" ref_dir = null + s3_bucket_data_name = "aim-data-dependencies-2.0-public" bed_filter = null exome_filter = false impact_filter = false diff --git a/nextflow_schema.json b/nextflow_schema.json index b5638a3..168a4bc 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -53,6 +53,13 @@ "description": "Path to aim pipeline dependencies directory.", "format": "directory-path" }, + "s3_bucket_data_name": { + "type": "string", + "default": "aim-data-dependencies-2.0-public", + "description": "[Not Implemented] S3 bucket name to aim pipeline dependencies directory.", + "format": "path", + "hidden": true + }, "run_id": { "type": "string", "default": "1", diff --git a/subworkflows/local/handle_input/main.nf b/subworkflows/local/handle_input/main.nf new file mode 100644 index 0000000..836f4ca --- /dev/null +++ b/subworkflows/local/handle_input/main.nf @@ -0,0 +1,32 @@ + + +include { + PHENOPACKET_TO_VARIANTS_AND_HPOS; GENERATE_INPUT_VCF; GENERATE_INPUT_VARIANTS +} from "../../../modules/local/singleton" + +workflow HANDLE_INPUT { + if (params.input_phenopacket) { + PHENOPACKET_TO_VARIANTS_AND_HPOS(params.input_phenopacket) + GENERATE_INPUT_VCF(PHENOPACKET_TO_VARIANTS_AND_HPOS.out.variants) + vcf = GENERATE_INPUT_VCF.out.vcf + hpo = PHENOPACKET_TO_VARIANTS_AND_HPOS.out.hpo + } else if (params.input_variant) { + GENERATE_INPUT_VARIANTS(params.input_variant) + GENERATE_INPUT_VCF(GENERATE_INPUT_VARIANTS.out.variants) + vcf = GENERATE_INPUT_VCF.out.vcf + hpo = params.input_hpo + } else if (params.input_vcf) { + vcf = file(params.input_vcf) + hpo = params.input_hpo + } else { + error "No input VCF or phenopacket or variant provided." + } + + if (!hpo) { + hpo = file(moduleDir.resolve("./assets/NO_FILE")) + } + + emit: + vcf = vcf + hpo = hpo +} \ No newline at end of file diff --git a/subworkflows/local/prepare_data/main.nf b/subworkflows/local/prepare_data/main.nf new file mode 100644 index 0000000..62b7c46 --- /dev/null +++ b/subworkflows/local/prepare_data/main.nf @@ -0,0 +1,61 @@ +include { + RESTRUCTURE_LOCAL_DATA_EXCEPT_VEP; + RESTRUCTURE_LOCAL_DATA_ONLY_VEP; + STORE_S3_BUCKET_DATA_EXCEPT_VEP; + SPLIT_DATA; + BUILD_REFERENCE_INDEX; +} from "../../../modules/local/prepare_data" + +include { + GENERATE_MANIFEST_JSON +} from "../../../modules/local/singleton" + +workflow PREPARE_DATA { + if (params.ref_dir) { + GENERATE_MANIFEST_JSON(file(params.ref_dir)) + data_except_vep = RESTRUCTURE_LOCAL_DATA_EXCEPT_VEP(file(params.ref_dir)) + data_only_vep = RESTRUCTURE_LOCAL_DATA_ONLY_VEP(file(params.ref_dir)) + } else { + error "Not Implemented" + data_except_vep = STORE_S3_BUCKET_DATA_EXCEPT_VEP() + } + + ( + chrmap_file, + ref_filter_bed, + ref_annot_dir, + ref_var_tier_dir, + ref_merge_expand_dir, + ref_mod5_diffusion_dir, + ref_predict_new_dir, + ref_model_inputs_dir, + phrank_tuple, + omim_tuple, + gnomad_tuple, + vep_tuple + ) = SPLIT_DATA( + data_except_vep, + data_only_vep, + params.bed_filter ? file(params.bed_filter) : moduleDir.resolve("assets/NO_FILE"), + params.exome_filter, + ) + + fasta_tuple = BUILD_REFERENCE_INDEX() + + emit: + data = chrmap_file.map{["chrmap_file", it]}.concat( + ref_filter_bed.map{["ref_filter_bed", it]}, + ref_annot_dir.map{["ref_annot_dir", it]}, + ref_var_tier_dir.map{["ref_var_tier_dir", it]}, + ref_merge_expand_dir.map{["ref_merge_expand_dir", it]}, + ref_mod5_diffusion_dir.map{["ref_mod5_diffusion_dir", it]}, + ref_predict_new_dir.map{["ref_predict_new_dir", it]}, + ref_model_inputs_dir.map{["ref_model_inputs_dir", it]}, + + fasta_tuple.map{["fasta_tuple", it]}, + gnomad_tuple.map{["gnomad_tuple", it]}, + omim_tuple.map{["omim_tuple", it]}, + phrank_tuple.map{["phrank_tuple", it]}, + vep_tuple.map{["vep_tuple", it]} + ).toList().map{ it.collectEntries { k, v -> [(k): v]} } +} \ No newline at end of file diff --git a/subworkflows/local/singleton/main.nf b/subworkflows/local/singleton/main.nf index 3f25326..a9d8493 100644 --- a/subworkflows/local/singleton/main.nf +++ b/subworkflows/local/singleton/main.nf @@ -1,7 +1,3 @@ -include { - addDependentParams -} from "../../../modules/local/utils" - include { VALIDATE_VCF; NORMALIZE_VCF; GENERATE_INPUT_VCF; VCF_TO_VARIANTS; VARIANTS_TO_ENSEMBL; ENSEMBL_TO_GENESYM; GENESYM_TO_PHRANK; @@ -10,32 +6,32 @@ include { ANNOTATE_TIER; JOIN_PHRANK; MERGE_SCORES_BY_CHROMOSOME; PREDICTION } from "../../../modules/local/singleton" - -addDependentParams(params) - workflow VCF_PRE_PROCESS { take: input_vcf - fasta - fasta_index - fasta_dict + data main: + chrmap_file = data.map { it.chrmap_file } + ref_filter_bed = data.map { it.ref_filter_bed } + fasta_tuple = data.map { it.fasta_tuple } + gnomad_tuple = data.map { it.gnomad_tuple } + VALIDATE_VCF(input_vcf) NORMALIZE_VCF(VALIDATE_VCF.out.vcf) CONVERT_GVCF( NORMALIZE_VCF.out.vcf, NORMALIZE_VCF.out.tbi, - fasta, - fasta_index, - fasta_dict, - params.chrmap + fasta_tuple.map { it[0] }, + fasta_tuple.map { it[1] }, + fasta_tuple.map { it[2] }, + chrmap_file ) FILTER_UNPASSED( CONVERT_GVCF.out.vcf, CONVERT_GVCF.out.tbi, - params.chrmap + chrmap_file ) FILTER_MITO_AND_UNKOWN_CHR( FILTER_UNPASSED.out.vcf, @@ -44,15 +40,15 @@ workflow VCF_PRE_PROCESS { FILTER_BED( FILTER_MITO_AND_UNKOWN_CHR.out.vcf, FILTER_MITO_AND_UNKOWN_CHR.out.tbi, - moduleDir.resolve(params.ref_filter_bed), + ref_filter_bed, ) FILTER_PROBAND( FILTER_BED.out.vcf, FILTER_BED.out.tbi, - params.ref_gnomad_genome, - params.ref_gnomad_genome_idx, - params.ref_gnomad_exome, - params.ref_gnomad_exome_idx + gnomad_tuple.map { it[0] }, + gnomad_tuple.map { it[1] }, + gnomad_tuple.map { it[2] }, + gnomad_tuple.map { it[3] }, ) emit: @@ -63,17 +59,27 @@ workflow PHRANK_SCORING { take: vcf hpo + data main: + phrank_tuple = data.map { it.phrank_tuple } VCF_TO_VARIANTS(vcf) - VARIANTS_TO_ENSEMBL(VCF_TO_VARIANTS.out, params.ref_loc) - ENSEMBL_TO_GENESYM(VARIANTS_TO_ENSEMBL.out, params.ref_to_sym) - GENESYM_TO_PHRANK(ENSEMBL_TO_GENESYM.out, - hpo, - params.phrank_dagfile, - params.phrank_disease_annotation, - params.phrank_gene_annotation, - params.phrank_disease_gene) + VARIANTS_TO_ENSEMBL( + VCF_TO_VARIANTS.out, + phrank_tuple.map { it[0] }, + ) + ENSEMBL_TO_GENESYM( + VARIANTS_TO_ENSEMBL.out, + phrank_tuple.map { it[1] }, + ) + GENESYM_TO_PHRANK( + ENSEMBL_TO_GENESYM.out, + hpo, + phrank_tuple.map { it[2] }, + phrank_tuple.map { it[3] }, + phrank_tuple.map { it[4] }, + phrank_tuple.map { it[5] }, + ) emit: phrank = GENESYM_TO_PHRANK.out @@ -83,56 +89,66 @@ workflow GENERATE_SINGLETON_FEATURES { take: vcf hpo + data main: + ref_annot_dir = data.map { it.ref_annot_dir } + ref_var_tier_dir = data.map { it.ref_var_tier_dir } + ref_merge_expand_dir = data.map { it.ref_merge_expand_dir } + ref_mod5_diffusion_dir = data.map { it.ref_mod5_diffusion_dir } + omim_tuple = data.map { it.omim_tuple } + vep_tuple = data.map { it.vep_tuple } + SPLIT_VCF_BY_CHROMOSOME(vcf) ANNOTATE_BY_VEP( SPLIT_VCF_BY_CHROMOSOME.out.chr_vcfs.flatten(), - params.vep_dir_cache, - params.vep_dir_plugins, - params.vep_custom_gnomad, - params.vep_custom_clinvar, - params.vep_custom_hgmd, - params.vep_plugin_revel, - params.vep_plugin_spliceai_snv, - params.vep_plugin_spliceai_indel, - params.vep_plugin_cadd, - params.vep_plugin_dbnsfp, - file(params.vep_idx) + vep_tuple.map { it[0] }, + vep_tuple.map { it[1] }, + vep_tuple.map { it[2] }, + vep_tuple.map { it[3] }, + vep_tuple.map { it[4] }, + vep_tuple.map { it[5] }, + vep_tuple.map { it[6] }, + vep_tuple.map { it[7] }, + vep_tuple.map { it[8] }, + vep_tuple.map { it[9] }, + vep_tuple.map { it[10] }, ) - HPO_SIM(hpo, - params.omim_hgmd_phen, - params.omim_obo, - params.omim_genemap2, - params.omim_pheno) + HPO_SIM( + hpo, + omim_tuple.map { it[0] }, + omim_tuple.map { it[1] }, + omim_tuple.map { it[2] }, + omim_tuple.map { it[3] }, + ) ANNOTATE_BY_MODULES ( ANNOTATE_BY_VEP.out.vep_output, HPO_SIM.out.hgmd_sim, HPO_SIM.out.omim_sim, - file(params.ref_annot_dir) + ref_annot_dir, ) - PHRANK_SCORING(vcf, hpo) + PHRANK_SCORING(vcf, hpo, data) ANNOTATE_TIER ( ANNOTATE_BY_MODULES.out.scores, PHRANK_SCORING.out, - file(params.ref_annot_dir), - file(params.ref_var_tier_dir), + ref_annot_dir, + ref_var_tier_dir, ) JOIN_PHRANK ( ANNOTATE_BY_MODULES.out.scores, PHRANK_SCORING.out, - file(params.ref_merge_expand_dir) + ref_merge_expand_dir, ) MERGE_SCORES_BY_CHROMOSOME( PHRANK_SCORING.out, ANNOTATE_TIER.out.tier.collect(), JOIN_PHRANK.out.compressed_scores.collect(), - file(params.ref_annot_dir), - file(params.ref_mod5_diffusion_dir), - file(params.ref_merge_expand_dir) + ref_annot_dir, + ref_mod5_diffusion_dir, + ref_merge_expand_dir, ) emit: From dddb716244f4812bba16424ecf190164d5f1687e Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Wed, 16 Jul 2025 14:38:29 -0500 Subject: [PATCH 75/80] Ignore code in utils for prettier --- .prettierignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.prettierignore b/.prettierignore index 5ca594e..4e2ba77 100644 --- a/.prettierignore +++ b/.prettierignore @@ -9,3 +9,4 @@ testing/ testing* *.pyc bin/ +utils/ From e7783896e556de7faea57bedd60a3d6845311015 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Mon, 21 Jul 2025 09:31:27 -0500 Subject: [PATCH 76/80] Use shorten form for using output file of VCF_PRE_PROCESS --- main.nf | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/main.nf b/main.nf index 217df55..3137df8 100644 --- a/main.nf +++ b/main.nf @@ -34,7 +34,7 @@ workflow { (vcf, hpo) = HANDLE_INPUT() if (params.input_ped) { - VCF_PRE_PROCESS_TRIO( + vcf = VCF_PRE_PROCESS_TRIO( vcf, file(params.input_ped), fasta_tuple.map { it[0] }, @@ -42,15 +42,13 @@ workflow { fasta_tuple.map { it[2] }, chrmap_file, ) - vcf = VCF_PRE_PROCESS_TRIO.out.vcf } if (!params.input_variant && !params.input_phenopacket) { - VCF_PRE_PROCESS( + vcf = VCF_PRE_PROCESS( vcf, data, ) - vcf = VCF_PRE_PROCESS.out.vcf } GENERATE_SINGLETON_FEATURES(vcf, hpo, data) From 31c8990fdbd4762b71c27a03d0574bd92da45c81 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Mon, 21 Jul 2025 09:51:39 -0500 Subject: [PATCH 77/80] Add more details for not implemented code --- subworkflows/local/prepare_data/main.nf | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/subworkflows/local/prepare_data/main.nf b/subworkflows/local/prepare_data/main.nf index 62b7c46..2b08fe8 100644 --- a/subworkflows/local/prepare_data/main.nf +++ b/subworkflows/local/prepare_data/main.nf @@ -2,6 +2,7 @@ include { RESTRUCTURE_LOCAL_DATA_EXCEPT_VEP; RESTRUCTURE_LOCAL_DATA_ONLY_VEP; STORE_S3_BUCKET_DATA_EXCEPT_VEP; + STORE_S3_BUCKET_DATA_ONLY_VEP; SPLIT_DATA; BUILD_REFERENCE_INDEX; } from "../../../modules/local/prepare_data" @@ -17,7 +18,8 @@ workflow PREPARE_DATA { data_only_vep = RESTRUCTURE_LOCAL_DATA_ONLY_VEP(file(params.ref_dir)) } else { error "Not Implemented" - data_except_vep = STORE_S3_BUCKET_DATA_EXCEPT_VEP() + // data_except_vep = STORE_S3_BUCKET_DATA_EXCEPT_VEP() + // data_only_vep = STORE_S3_BUCKET_DATA_ONLY_VEP() } ( From cc3db94e2c0639c5fcd2c79e9855e3d38d0118e8 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Thu, 31 Jul 2025 11:44:59 -0500 Subject: [PATCH 78/80] Bump the version to 1.1 --- README.md | 30 +++++++++++------------------- docs/source/conf.py | 6 +++--- docs/source/installation.rst | 19 ++++++++++--------- nextflow.config | 7 ++++--- nextflow_schema.json | 2 +- params.yaml.example | 9 --------- 6 files changed, 29 insertions(+), 44 deletions(-) delete mode 100644 params.yaml.example diff --git a/README.md b/README.md index 1d4def1..2ab6c05 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ AIM utilizes various databases for variant annotation, all of which have been co ``` 3. Use the following command to sync the S3 bucket to your local directory: ```bash - $ aws s3 sync s3://aim-data-dependencies-2.0-public . --no-sign-request + $ aws s3 sync s3://aim-data-dependencies-2.3-public . --no-sign-request ``` ### Get the software @@ -47,7 +47,7 @@ AIM utilizes various databases for variant annotation, all of which have been co AIM is released as a Nextflow pipeline for easy distribution. To get it: ```bash -$ git clone -b nextflow_conversion https://github.com/LiuzLab/AI_MARRVEL +$ git clone https://github.com/LiuzLab/AI_MARRVEL $ cd AI_MARRVEL $ nextflow run main.nf --version ``` @@ -55,25 +55,17 @@ $ nextflow run main.nf --version ### Run with your sample ```bash -$ nextflow run main.nf --ref_dir - --input_vcf - --input_hpo - --outdir - --storedir - --bed_filter # Optional - --run_id [Sample Id] # Optional, default: 1 - --ref_ver [Reference genome: hg19/hg38] # Optional, default: hg19 - --exome_filter # Optional +nextflow run /path/to/AI_MARRVEL/main.nf \\ + -profile \\ + --ref_dir /path/to/dependencies/ \\ + --input_vcf /path/to/input.vcf \\ + --input_hpo /path/to/input.hpos.txt \\ + --outdir /path/to/output \\ + --storedir /path/to/store \\ + --run_id \\ + --ref_ver ``` -Alternatively, the pipeline can be executed with a parameter file (yaml) - -```bash -$ nextflow run main.nf -params-file params.yaml -``` - -NOTE: You need to create `params.yaml` by copying [params.yaml.example](params.yaml.example) file and follow the instruction. - For more information on usage and parameters which are open for modification, please use `--help` option as shown below. ``` diff --git a/docs/source/conf.py b/docs/source/conf.py index 2c71d24..7b29fdb 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,13 +15,13 @@ # -- Project information ----------------------------------------------------- project = 'AI-MARRVEL' -copyright = "2024, Zhandong Liu's Lab at Baylor College of Medicine" +copyright = "2025, Zhandong Liu's Lab at Baylor College of Medicine" author = 'Chaozhong Liu, Dongxue Mao' # The short X.Y version -version = 'v1.0' +version = 'v1.1' # The full version, including alpha/beta/rc tags -release = '1.0.0' +release = '1.1.0' # -- General configuration --------------------------------------------------- diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 8c8f0a1..6d9b1f9 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -30,14 +30,15 @@ Running AIM Use following command-line to run AIM. .. code-block:: bash - - nextflow run Liuzlab/AI_MARRVEL -r nextflow_conversion \ - --ref_dir \ - --ref_ver [Reference genome: hg19/hg38] \ - --input_vcf \ - --input_hpo \ - --outdir \ - --run_id [Sample Id] + nextflow run /path/to/AI_MARRVEL/main.nf \\ + -profile \\ + --ref_dir /path/to/dependencies/ \\ + --input_vcf /path/to/input.vcf \\ + --input_hpo /path/to/input.hpos.txt \\ + --outdir /path/to/output \\ + --storedir /path/to/store \\ + --run_id \\ + --ref_ver @@ -60,7 +61,7 @@ AIM utilizes various databases for variant annotation, all of which have been co .. code-block:: bash - aws s3 sync s3://aim-data-dependencies-2.0-public . --no-sign-request + aws s3 sync s3://aim-data-dependencies-2.3-public . --no-sign-request .. warning:: diff --git a/nextflow.config b/nextflow.config index 5ed334a..254ad6b 100644 --- a/nextflow.config +++ b/nextflow.config @@ -10,7 +10,7 @@ params { run_id = "run1" ref_ver = "hg19" ref_dir = null - s3_bucket_data_name = "aim-data-dependencies-2.0-public" + s3_bucket_data_name = "aim-data-dependencies-2.3-public" bed_filter = null exome_filter = false impact_filter = false @@ -59,13 +59,14 @@ validation { enabled = true command = """ nextflow run /path/to/AI_MARRVEL/main.nf \\ + -profile \\ --ref_dir /path/to/dependencies/ \\ --input_vcf /path/to/input.vcf \\ --input_hpo /path/to/input.hpos.txt \\ --outdir /path/to/output \\ + --storedir /path/to/store \\ --run_id \\ - --ref_ver \\ - -profile + --ref_ver """ fullParameter = "help_full" showHiddenParameter = "show_hidden" diff --git a/nextflow_schema.json b/nextflow_schema.json index 168a4bc..a98eb45 100644 --- a/nextflow_schema.json +++ b/nextflow_schema.json @@ -55,7 +55,7 @@ }, "s3_bucket_data_name": { "type": "string", - "default": "aim-data-dependencies-2.0-public", + "default": "aim-data-dependencies-2.3-public", "description": "[Not Implemented] S3 bucket name to aim pipeline dependencies directory.", "format": "path", "hidden": true diff --git a/params.yaml.example b/params.yaml.example deleted file mode 100644 index bf3f876..0000000 --- a/params.yaml.example +++ /dev/null @@ -1,9 +0,0 @@ -# Please Copy This File to `params.yaml` -# And Complete Placeholder Marked With , -# Examples Are In Comment -# NOTE: It's intended that params.yaml file is not tracked by git. - -ref_dir: # Example: "/home/jaeyeon/Data/aim_data_dependencies/" -input_vcf: # Example: "/home/jaeyeon/Data/aim_test/demo.vcf.gz" -input_hpo: # Example: "/home/jaeyeon/Data/aim_test/demo.hpos.txt" -outdir: # Example: "./out" From 148c3846cf12bef5ac604d98dd8a261463d2b9ab Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Thu, 31 Jul 2025 12:03:13 -0500 Subject: [PATCH 79/80] Fix a bug failing to handle empty optional inputs --- subworkflows/local/handle_input/main.nf | 2 +- subworkflows/local/prepare_data/main.nf | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/subworkflows/local/handle_input/main.nf b/subworkflows/local/handle_input/main.nf index 836f4ca..299926d 100644 --- a/subworkflows/local/handle_input/main.nf +++ b/subworkflows/local/handle_input/main.nf @@ -23,7 +23,7 @@ workflow HANDLE_INPUT { } if (!hpo) { - hpo = file(moduleDir.resolve("./assets/NO_FILE")) + hpo = file(moduleDir.resolve("../../../assets/NO_FILE")) } emit: diff --git a/subworkflows/local/prepare_data/main.nf b/subworkflows/local/prepare_data/main.nf index 2b08fe8..eddf110 100644 --- a/subworkflows/local/prepare_data/main.nf +++ b/subworkflows/local/prepare_data/main.nf @@ -38,7 +38,7 @@ workflow PREPARE_DATA { ) = SPLIT_DATA( data_except_vep, data_only_vep, - params.bed_filter ? file(params.bed_filter) : moduleDir.resolve("assets/NO_FILE"), + params.bed_filter ? file(params.bed_filter) : moduleDir.resolve("../../../assets/NO_FILE"), params.exome_filter, ) From cb5a279ad5bf95a92918896c71cca1b094739f62 Mon Sep 17 00:00:00 2001 From: jaeyeon Date: Thu, 31 Jul 2025 12:22:45 -0500 Subject: [PATCH 80/80] Remove integrated file from the documents --- docs/source/output.rst | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/source/output.rst b/docs/source/output.rst index cdd0226..ff37f29 100644 --- a/docs/source/output.rst +++ b/docs/source/output.rst @@ -23,8 +23,6 @@ Following the execution of AIM, you will receive five output files, which are sa - Prediction results from AIM Novel Disease Gene mode. * - \*_nd_recessive_predictions.csv - Prediction results from AIM Novel Disease Gene + Recessive mode. - * - \*_integrated.csv - - Combining all 4 prediction results. Output Explanation ======================

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