diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..895f45f Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..aa6e7ae --- /dev/null +++ b/.gitignore @@ -0,0 +1,58 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +dist/ +develop-eggs/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +#ds store in mac +polly/.DS_Store +.DS_Store +tests/.DS_Store + +# Scrapy stuff: +.scrapy + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# mypy +.mypy_cache/ + +# IDE settings +.vscode/ +.idea diff --git a/OmixAtlas/omixAtlas with polly-python.ipynb b/OmixAtlas/omixAtlas with polly-python.ipynb new file mode 100644 index 0000000..90d6b07 --- /dev/null +++ b/OmixAtlas/omixAtlas with polly-python.ipynb @@ -0,0 +1,1331 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "5031d247", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting polly-python==3.1.0\n", + " Downloading https://elucidatainc.github.io/PublicAssets/builds/polly-python/tests/testpolly/polly_python-3.1.0-py3-none-any.whl (155 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.6/155.6 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hCollecting requests==2.28.1\n", + " Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: tqdm==4.65.0 in 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"Requirement already satisfied: pluggy<2.0,>=0.12 in ./.environments/miniconda/envs/polly/lib/python3.10/site-packages (from pytest>=6.2.5->polly-python==3.1.0) (1.3.0)\n", + "Requirement already satisfied: pycparser in ./.environments/miniconda/envs/polly/lib/python3.10/site-packages (from cffi>=1.12->cryptography<=38.0.0,>=37.0.1->polly-python==3.1.0) (2.21)\n", + "Installing collected packages: tabulate, charset-normalizer, requests, cmapPy, polly-python\n", + " Attempting uninstall: tabulate\n", + " Found existing installation: tabulate 0.8.10\n", + " Uninstalling tabulate-0.8.10:\n", + " Successfully uninstalled tabulate-0.8.10\n", + " Attempting uninstall: charset-normalizer\n", + " Found existing installation: charset-normalizer 3.2.0\n", + " Uninstalling charset-normalizer-3.2.0:\n", + " Successfully uninstalled charset-normalizer-3.2.0\n", + " Attempting uninstall: requests\n", + " Found existing installation: requests 2.31.0\n", + " Uninstalling requests-2.31.0:\n", + " Successfully uninstalled requests-2.31.0\n", + " Attempting uninstall: polly-python\n", + " Found existing installation: polly-python 1.0.0\n", + " Uninstalling polly-python-1.0.0:\n", + " Successfully uninstalled polly-python-1.0.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "polly-validator 0.0.2 requires requests==2.25.1, but you have requests 2.28.1 which is incompatible.\n", + "polly-validator 0.0.2 requires tabulate==0.8.10, but you have tabulate 0.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed charset-normalizer-2.1.1 cmapPy-4.0.1 polly-python-3.1.0 requests-2.28.1 tabulate-0.9.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install polly-python\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bc653a8", + "metadata": {}, + "outputs": [], + "source": [ + "# import\n", + "\n", + "import os\n", + "import json\n", + "from polly.omixatlas import OmixAtlas\n", + "from polly.jobs import jobs\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "384c3ba2", + "metadata": {}, + "outputs": [], + "source": [ + "AUTH_KEY = \"auth key\"\n", + "Polly.auth(AUTH_KEY, env=\"polly\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fe534db", + "metadata": {}, + "outputs": [], + "source": [ + "# connecting to omixatlas\n", + "\n", + "omixatlas = OmixAtlas()\n", + "job = jobs()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cf2acb0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OmixAtlas 1728297667220 Created \n", + " Repository Id Repository Name Category Display Name \\\n", + "0 1728297667220 test_bulkrna_oa_sri private test bulkrna OA sri \n", + "\n", + " Description \n", + "0 this is for bulk rna seq diy \n" + ] + } + ], + "source": [ + "# create omixatlas\n", + "\n", + "result = omixatlas.create(display_name = \"test bulkrna OA sri\", description = \"this is for bulk rna seq diy \") \n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a72361d", + "metadata": {}, + "outputs": [], + "source": [ + "# updating a existing omixatlas\n", + "\n", + "res = omixatlas.update(repo_key = \"{repokey}\", display_name = \"test bulkrna OA sri\", description = \"this is the updated description of this omixatlas\")\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "491de7cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cell_line_count 1\n", + "cell_type_count 11\n", + "data_source_count 1\n", + "data_type_count 1\n", + "dataset_count 6\n", + "datatypes [{'raw counts transcriptomics': 6}]\n", + "disease_count 8\n", + "drug_count 1\n", + "indexes {}\n", + "linked_workspace_id None\n", + "normal_sample_count 153\n", + "organism_count 3\n", + "repo_id 1722434200350\n", + "repo_name bulk_rna_sri_test\n", + "sample_count 574.0\n", + "sources [{'geo': 6}]\n", + "tissue_count 4\n", + "v2_indexes {'files': 'bulk_rna_sri_test_files', 'gct_col_...\n", + "Name: data, dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#getting basic details\n", + "\n", + "omix_atlas_summary = pd.DataFrame.from_dict(omixatlas.omixatlas_summary('1722434200350'))['data']\n", + "omix_atlas_summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e350a46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': [{'repo_name': 'bulk_rna_sri_test',\n", + " 'repo_id': '1722434200350',\n", + " 'indexes': {},\n", + " 'v2_indexes': {'files': 'bulk_rna_sri_test_files',\n", + " 'gct_col_metadata': 'bulk_rna_sri_test_gct_col_metadata'},\n", + " 'linked_workspace_id': None,\n", + " 'sources': [{'geo': 6}],\n", + " 'datatypes': [{'raw counts transcriptomics': 6}],\n", + " 'dataset_count': 6,\n", + " 'disease_count': 8,\n", + " 'tissue_count': 4,\n", + " 'organism_count': 3,\n", + " 'cell_line_count': 1,\n", + " 'cell_type_count': 11,\n", + " 'drug_count': 1,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 574.0,\n", + " 'normal_sample_count': 153},\n", + " {'repo_id': '1724226646825',\n", + " 'repo_name': 'demo_srishti_omixatlas',\n", + " 'indexes': {},\n", + " 'v2_indexes': {},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 0,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1669033066992',\n", + " 'repo_name': 'destination_atlas',\n", + " 'indexes': {'files': 'destination_atlas_files',\n", + " 'gct_col_metadata': 'destination_atlas_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'destination_atlas_files',\n", + " 'gct_col_metadata': 'destination_atlas_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 47}],\n", + " 'datatypes': [{'raw counts transcriptomics': 7}],\n", + " 'dataset_count': 47,\n", + " 'disease_count': 47,\n", + " 'tissue_count': 41,\n", + " 'organism_count': 4,\n", + " 'cell_line_count': 25,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 53,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 3383.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1679482518578',\n", + " 'repo_name': 'details_page',\n", + " 'indexes': {'files': 'details_page_files',\n", + " 'gct_col_metadata': 'details_page_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'details_page_files',\n", + " 'gct_col_metadata': 'details_page_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'some source': 27},\n", + " {'test': 1},\n", + " {'testcrtnjrny': 1},\n", + " {'this is a test': 1}],\n", + " 'datatypes': [{'raw counts transcriptomics': 27}, {'microarray': 3}],\n", + " 'dataset_count': 30,\n", + " 'disease_count': 42,\n", + " 'tissue_count': 28,\n", + " 'organism_count': 4,\n", + " 'cell_line_count': 15,\n", + " 'cell_type_count': 24,\n", + " 'drug_count': 12,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 4,\n", + " 'sample_count': 4263.0,\n", + " 'normal_sample_count': 2136},\n", + " {'repo_id': '1685621126191',\n", + " 'repo_name': 'gdx_demo_atlas',\n", + " 'indexes': {'files': 'gdx_demo_atlas_files',\n", + " 'gct_col_metadata': 'gdx_demo_atlas_gct_col_metadata',\n", + " 'comparisons': 'gdx_demo_atlas_comparisons',\n", + " 'comparison_data': 'gdx_demo_atlas_comparison_data',\n", + " 'pathway_data': 'gdx_demo_atlas_pathway_data'},\n", + " 'v2_indexes': {'files': 'gdx_demo_atlas_files',\n", + " 'gct_col_metadata': 'gdx_demo_atlas_gct_col_metadata',\n", + " 'comparisons': 'gdx_demo_atlas_comparisons',\n", + " 'comparison_data': 'gdx_demo_atlas_comparison_data',\n", + " 'pathway_data': 'gdx_demo_atlas_pathway_data'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 37}],\n", + " 'datatypes': [{'raw counts transcriptomics': 29}, {'': 4}],\n", + " 'dataset_count': 37,\n", + " 'disease_count': 48,\n", + " 'tissue_count': 28,\n", + " 'organism_count': 6,\n", + " 'cell_line_count': 21,\n", + " 'cell_type_count': 18,\n", + " 'drug_count': 12,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 947.0,\n", + " 'normal_sample_count': 267},\n", + " {'repo_id': '1663151941472',\n", + " 'repo_name': 'c_oa',\n", + " 'indexes': {'files': 'c_oa_files'},\n", + " 'v2_indexes': {'files': 'c_oa_files'},\n", + " 'linked_workspace_id': 14918,\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'': 10}],\n", + " 'datatypes': [{'drug response': 10}],\n", + " 'dataset_count': 10,\n", + " 'disease_count': 3,\n", + " 'tissue_count': 1,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 5,\n", + " 'cell_type_count': 1,\n", + " 'drug_count': 1,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 10.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1722598229271',\n", + " 'repo_name': 'bulk_rna_a_omixatlas',\n", + " 'indexes': {},\n", + " 'v2_indexes': {},\n", + " 'org_id': '1',\n", + " 'data_type': 'bulk_rna_seq',\n", + " 'data_management_version': 'v2',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 0,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1659450268526',\n", + " 'repo_name': 'multiple_oa_2',\n", + " 'indexes': {'files': 'multiple_oa_2_files',\n", + " 'gct_col_metadata': 'multiple_oa_2_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'multiple_oa_2_files',\n", + " 'gct_col_metadata': 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11,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 9,\n", + " 'sample_count': 1232147.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1669085436194',\n", + " 'repo_name': 'source_atlas',\n", + " 'indexes': {'files': 'source_atlas_files',\n", + " 'gct_col_metadata': 'source_atlas_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'source_atlas_files',\n", + " 'gct_col_metadata': 'source_atlas_gct_col_metadata'},\n", + " 'data_type': 'bulk_rna_seq',\n", + " 'data_management_version': 'v1',\n", + " 'sources': [{'geo': 44}, {'': 1}, {'test': 1}],\n", + " 'datatypes': [{'raw counts transcriptomics': 44}, {'transcriptomics': 1}],\n", + " 'dataset_count': 46,\n", + " 'disease_count': 39,\n", + " 'tissue_count': 39,\n", + " 'organism_count': 8,\n", + " 'cell_line_count': 23,\n", + " 'cell_type_count': 26,\n", + " 'drug_count': 13,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 3,\n", + " 'sample_count': 3464.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1674572370484',\n", + " 'repo_name': 'test_pipeline',\n", + " 'indexes': {'files': 'test_pipeline_files',\n", + " 'gct_col_metadata': 'test_pipeline_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'test_pipeline_files',\n", + " 'gct_col_metadata': 'test_pipeline_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 45}],\n", + " 'datatypes': [{'raw counts transcriptomics': 32}, {'transcriptomics': 13}],\n", + " 'dataset_count': 45,\n", + " 'disease_count': 34,\n", + " 'tissue_count': 18,\n", + " 'organism_count': 2,\n", + " 'cell_line_count': 9,\n", + " 'cell_type_count': 24,\n", + " 'drug_count': 9,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 2922.0,\n", + " 'normal_sample_count': 2278},\n", + " {'repo_id': '1673014297214',\n", + " 'repo_name': 'test_connector_pipeline',\n", + " 'indexes': {'files': 'test_connector_pipeline_files',\n", + " 'gct_col_metadata': 'test_connector_pipeline_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'test_connector_pipeline_files',\n", + " 'gct_col_metadata': 'test_connector_pipeline_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 11}],\n", + " 'datatypes': [{'transcriptomics': 11}],\n", + " 'dataset_count': 11,\n", + " 'disease_count': 11,\n", + " 'tissue_count': 5,\n", + " 'organism_count': 2,\n", + " 'cell_line_count': 65,\n", + " 'cell_type_count': 7,\n", + " 'drug_count': 3,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 141.0,\n", + " 'normal_sample_count': 29},\n", + " {'repo_id': '1674475984908',\n", + " 'repo_name': 'single_cell_test',\n", + " 'indexes': {'files': 'single_cell_test_files',\n", + " 'h5ad_col_metadata': 'single_cell_test_h5ad_col_metadata'},\n", + " 'v2_indexes': {'files': 'single_cell_test_files',\n", + " 'h5ad_col_metadata': 'single_cell_test_h5ad_col_metadata'},\n", + " 'data_management_version': 'v1',\n", + " 'sources': [{'array express': 2},\n", + " {'covid-19 cell atlas': 1},\n", + " {'human cell atlas': 1}],\n", + " 'datatypes': [{'single cell': 4}],\n", + " 'dataset_count': 4,\n", + " 'disease_count': 4,\n", + " 'tissue_count': 2,\n", + " 'organism_count': 2,\n", + " 'cell_line_count': 1,\n", + " 'cell_type_count': 11,\n", + " 'drug_count': 2,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 3,\n", + " 'sample_count': 31.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1643016586529',\n", + " 'repo_name': 'geo',\n", + " 'indexes': {'files': 'geo_files',\n", + " 'gct_col_metadata': 'geo_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'geo_files',\n", + " 'gct_col_metadata': 'geo_gct_col_metadata'},\n", + " 'linked_workspace_id': '15196',\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 5}, {'source': 1}, {'vfv': 1}],\n", + " 'datatypes': [{'transcriptomics': 6}, {'': 1}],\n", + " 'dataset_count': 7,\n", + " 'disease_count': 12,\n", + " 'tissue_count': 8,\n", + " 'organism_count': 3,\n", + " 'cell_line_count': 5,\n", + " 'cell_type_count': 6,\n", + " 'drug_count': 2,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 3,\n", + " 'sample_count': 1741.0,\n", + " 'normal_sample_count': 154446},\n", + " {'repo_id': '1693554957612',\n", + " 'repo_name': 'testing_nf_pipeline',\n", + " 'indexes': {},\n", + " 'v2_indexes': {},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 0,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1709709547712',\n", + " 'repo_name': 'vcf_demo1',\n", + " 'v2_indexes': {},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 0,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1691567442682',\n", + " 'repo_name': 'test_aug_09',\n", + " 'indexes': {},\n", + " 'v2_indexes': {},\n", + " 'data_management_version': 'v1',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 0,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1679312499313',\n", + " 'repo_name': 'test_pipeline_rnaseq',\n", + " 'indexes': {'files': 'test_pipeline_rnaseq_files',\n", + " 'gct_col_metadata': 'test_pipeline_rnaseq_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'test_pipeline_rnaseq_files',\n", + " 'gct_col_metadata': 'test_pipeline_rnaseq_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 110}],\n", + " 'datatypes': [{'raw counts transcriptomics': 108}, {'transcriptomics': 2}],\n", + " 'dataset_count': 110,\n", + " 'disease_count': 73,\n", + " 'tissue_count': 41,\n", + " 'organism_count': 3,\n", + " 'cell_line_count': 39,\n", + " 'cell_type_count': 49,\n", + " 'drug_count': 22,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 1926.0,\n", + " 'normal_sample_count': 1696},\n", + " {'repo_id': '1698731902069',\n", + " 'repo_name': 'test_oa_sr',\n", + " 'v2_indexes': {'files': 'test_oa_sr_files',\n", + " 'gct_col_metadata': 'test_oa_sr_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 0,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1649664527538',\n", + " 'repo_name': 'tyra_cell_viability_assay',\n", + " 'indexes': {'files': 'tyra_cell_viability_assay_files'},\n", + " 'v2_indexes': {'files': 'tyra_cell_viability_assay_files'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 3760,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1697712481470',\n", + " 'repo_name': 'transactional_ingestion_ps',\n", + " 'v2_indexes': {'files': 'transactional_ingestion_ps_files',\n", + " 'gct_col_metadata': 'transactional_ingestion_ps_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 1}],\n", + " 'datatypes': [{'raw counts transcriptomics': 1}],\n", + " 'dataset_count': 1,\n", + " 'disease_count': 2,\n", + " 'tissue_count': 2,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 1,\n", + " 'cell_type_count': 3,\n", + " 'drug_count': 1,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 3.0,\n", + " 'normal_sample_count': 36},\n", + " {'repo_id': '1698657821316',\n", + " 'repo_name': 'test_oa_gates',\n", + " 'indexes': {'files': 'test_oa_gates_files',\n", + " 'gct_col_metadata': 'test_oa_gates_gct_col_metadata'},\n", + " 'v2_indexes': {'files': 'test_oa_gates_files',\n", + " 'gct_col_metadata': 'test_oa_gates_gct_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 5}],\n", + " 'datatypes': [{'raw counts transcriptomics': 5}],\n", + " 'dataset_count': 5,\n", + " 'disease_count': 7,\n", + " 'tissue_count': 1,\n", + " 'organism_count': 2,\n", + " 'cell_line_count': 1,\n", + " 'cell_type_count': 1,\n", + " 'drug_count': 1,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 1860.0,\n", + " 'normal_sample_count': 603},\n", + " {'repo_id': '1680106629523',\n", + " 'repo_name': 'single_sch_oa',\n", + " 'indexes': {'files': 'single_sch_oa_files',\n", + " 'h5ad_col_metadata': 'single_sch_oa_h5ad_col_metadata'},\n", + " 'v2_indexes': {'files': 'single_sch_oa_files',\n", + " 'h5ad_col_metadata': 'single_sch_oa_h5ad_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 2}, {'': 1}, {'ghi': 1}, {'publication': 1}],\n", + " 'datatypes': [{'': 2}, {'single cell': 1}, {'single_cell': 1}],\n", + " 'dataset_count': 5,\n", + " 'disease_count': 9,\n", + " 'tissue_count': 6,\n", + " 'organism_count': 2,\n", + " 'cell_line_count': 1,\n", + " 'cell_type_count': 40,\n", + " 'drug_count': 13,\n", + " 'data_type_count': 3,\n", + " 'data_source_count': 4,\n", + " 'sample_count': 35.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1722849364887',\n", + " 'repo_name': 'testing_repo_combined_oa_1_v1',\n", + " 'v2_indexes': {'files': 'testing_repo_combined_oa_1_v1_files'},\n", + " 'data_management_version': 'v1',\n", + " 'sources': [{'geo': 1}],\n", + " 'datatypes': [{'raw counts transcriptomics': 1}],\n", + " 'dataset_count': 1,\n", + " 'disease_count': 1,\n", + " 'tissue_count': 1,\n", + " 'organism_count': 1,\n", + " 'cell_line_count': 1,\n", + " 'cell_type_count': 2,\n", + " 'drug_count': 1,\n", + " 'data_type_count': 1,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 12.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1698397847153',\n", + " 'repo_name': 'transactional_test_ps',\n", + " 'v2_indexes': {'files': 'transactional_test_ps_files'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [],\n", + " 'datatypes': [],\n", + " 'dataset_count': 0,\n", + " 'disease_count': 0,\n", + " 'tissue_count': 0,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 0,\n", + " 'drug_count': 0,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 0,\n", + " 'sample_count': 0.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1674580012706',\n", + " 'repo_name': 'test_single_cell',\n", + " 'indexes': {'files': 'test_single_cell_files',\n", + " 'h5ad_col_metadata': 'test_single_cell_h5ad_col_metadata'},\n", + " 'v2_indexes': {'files': 'test_single_cell_files',\n", + " 'h5ad_col_metadata': 'test_single_cell_h5ad_col_metadata'},\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 3}, {'scp': 3}],\n", + " 'datatypes': [{'single cell': 4}, {'': 2}],\n", + " 'dataset_count': 6,\n", + " 'disease_count': 8,\n", + " 'tissue_count': 6,\n", + " 'organism_count': 0,\n", + " 'cell_line_count': 0,\n", + " 'cell_type_count': 3,\n", + " 'drug_count': 1,\n", + " 'data_type_count': 2,\n", + " 'data_source_count': 2,\n", + " 'sample_count': 898615.0,\n", + " 'normal_sample_count': 0},\n", + " {'repo_id': '1671791834799',\n", + " 'repo_name': 'single_cell_source',\n", + " 'indexes': {'files': 'single_cell_source_files',\n", + " 'h5ad_col_metadata': 'single_cell_source_h5ad_col_metadata'},\n", + " 'v2_indexes': {'files': 'single_cell_source_files',\n", + " 'h5ad_col_metadata': 'single_cell_source_h5ad_col_metadata'},\n", + " 'data_type': 'single_cell',\n", + " 'data_management_version': 'v2',\n", + " 'sources': [{'geo': 1}],\n", + " 'datatypes': [],\n", + " 'dataset_count': 1,\n", + " 'disease_count': 2,\n", + " 'tissue_count': 2,\n", + " 'organism_count': 1,\n", + " 'cell_line_count': 1,\n", + " 'cell_type_count': 1,\n", + " 'drug_count': 1,\n", + " 'data_type_count': 0,\n", + " 'data_source_count': 1,\n", + " 'sample_count': 39.0,\n", + " 'normal_sample_count': 0}]}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get all omixatlases\n", + "\n", + "omixatlas.get_all_omixatlas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62517e15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution succeeded (time taken: 2.53 seconds, data scanned: 0.000 MB)\n", + "Fetched 30 rows\n" + ] + }, + { + "data": { + "text/html": [ + "
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column_namecolumn_type
0src_uristring
1sample_idstring
2curated_gene_modifiedarray<string>
3curated_cell_linestring
4curated_cohort_namestring
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" + ], + "text/plain": [ + " column_name column_type\n", + "0 src_uri string\n", + "1 sample_id string\n", + "2 curated_gene_modified array\n", + "3 curated_cell_line string\n", + "4 curated_cohort_name string" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# query metadata in omixatlases\n", + "\n", + "query = \"DESCRIBE bulk_rna_sri_test.samples\"\n", + "results=omixatlas.query_metadata(query)\n", + "results.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1a5184e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query execution succeeded (time taken: 3.20 seconds, data scanned: 0.000 MB)\n", + "Fetched 2 rows\n" + ] + }, + { + "data": { + "text/html": [ + "
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table_name
0bulk_rna_sri_test.datasets
1bulk_rna_sri_test.samples
\n", + "
" + ], + "text/plain": [ + " table_name\n", + "0 bulk_rna_sri_test.datasets\n", + "1 bulk_rna_sri_test.samples" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = \"SHOW TABLES IN bulk_rna_sri_test\"\n", + "results=omixatlas.query_metadata(query)\n", + "results.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PollyKG/.DS_Store b/PollyKG/.DS_Store new file mode 100644 index 0000000..a686bd3 Binary files /dev/null and b/PollyKG/.DS_Store differ diff --git a/PollyKG/querying polly kg.ipynb b/PollyKG/querying polly kg.ipynb new file mode 100644 index 0000000..ba5f672 --- /dev/null +++ b/PollyKG/querying polly kg.ipynb @@ -0,0 +1,1236 @@ +{ + "cells": [ + { + "attachments": { + "elucidata_logo.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "66332fab", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "![elucidata_logo.png](attachment:elucidata_logo.png)" + ] + }, + { + "cell_type": "markdown", + "id": "f5eb5dfb", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "### Installations and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4748422d", + "metadata": { + "kernel": "Python3", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting polly-python==3.1.0-kg\n", + " Downloading https://elucidatainc.github.io/PublicAssets/builds/polly-python/tests/testpolly/polly_python-3.1.0_kg-py3-none-any.whl (154 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.5/154.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hCollecting requests==2.28.1\n", + " Downloading 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" Successfully uninstalled tabulate-0.8.10\n", + " Attempting uninstall: charset-normalizer\n", + " Found existing installation: charset-normalizer 3.2.0\n", + " Uninstalling charset-normalizer-3.2.0:\n", + " Successfully uninstalled charset-normalizer-3.2.0\n", + " Attempting uninstall: requests\n", + " Found existing installation: requests 2.31.0\n", + " Uninstalling requests-2.31.0:\n", + " Successfully uninstalled requests-2.31.0\n", + " Attempting uninstall: polly-python\n", + " Found existing installation: polly-python 1.0.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Uninstalling polly-python-1.0.0:\n", + " Successfully uninstalled polly-python-1.0.0\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "polly-validator 0.0.2 requires requests==2.25.1, but you have requests 2.28.1 which is incompatible.\n", + "polly-validator 0.0.2 requires tabulate==0.8.10, but you have tabulate 0.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed charset-normalizer-2.1.1 cmapPy-4.0.1 polly-python-3.1.0 requests-2.28.1 tabulate-0.9.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install polly-python==3.2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c6ff0d02", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib is building the font cache; this may take a moment.\n" + ] + } + ], + "source": [ + "#Imports\n", + "\n", + "from polly.auth import Polly\n", + "from polly.polly_kg import PollyKG\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import sys\n", + "import io\n", + "\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0703b224", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You're currently using an outdated version of polly-python '1.0.0'. Please update using the command 'pip install polly-python==3.1.0' to upgrade to the newest version '3.1.0'\n" + ] + } + ], + "source": [ + "#Authentication\n", + "\n", + "AUTH_KEY = \"auth_key\"\n", + "Polly.auth(AUTH_KEY)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "756d28bc", + "metadata": { + "kernel": "Python3" + }, + "outputs": [], + "source": [ + "#Connect to PollyKG\n", + "\n", + "kg = PollyKG()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f96875ae", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'status': 'healthy', 'gremlin': {'version': 'tinkerpop-3.7.1'}, 'opencypher': {'version': 'Neptune-9.0.20190305-1.0'}}\n" + ] + }, + { + "data": { + "text/plain": [ + "{'status': 'healthy',\n", + " 'gremlin': {'version': 'tinkerpop-3.7.1'},\n", + " 'opencypher': {'version': 'Neptune-9.0.20190305-1.0'}}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get engine status\n", + "kg.get_engine_status()" + ] + }, + { + "cell_type": "markdown", + "id": "025a92f9", + "metadata": { + "kernel": "Python3" + }, + "source": [ + "### PollyKG Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a630bbe8", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'numNodes': 426515, 'numEdges': 9750778, 'numNodeLabels': 7, 'numEdgeLabels': 27, 'nodeLabels': ['Drug', 'Phenotype', 'Gene', 'Disease', 'Pathway', 'Go', 'Protein'], 'edgeLabels': ['is_a_phenotype', 'disease_has_feature_disease', 'predisposes_towards_disease', 'has_characteristic_disease', 'has_part_go', 'targets', 'linked_to_pathway', 'happens_during_go', 'excluded_subClassOf_disease', 'part_of_go', 'mim2gene_medgen_disease', 'ends_during_go', 'is_a_pathway', 'linked_to', 'positively_regulates_go', 'part_of_progression_of_disease_disease', 'regulates_go', 'disease_arises_from_feature_disease', 'occurs_in_go', 'is_a_go', 'negatively_regulates_go', 'disease_has_major_feature_disease', 'intersection_of_disease', 'curated_content_resource_disease', 'disease_shares_features_of_disease', 'is_a_disease', 'interacts_with'], 'numNodeProperties': 48, 'numEdgeProperties': 28, 'nodeProperties': [{'description': 219818}, {'LocusTag': 193500}, {'Symbol': 193500}, {'Synonyms': 193500}, {'tax_id': 193500}, {'chromosome': 193496}, {'map_location': 193496}, {'Nomenclature_status': 193487}, {'Symbol_from_nomenclature_authority': 193487}, {'type_of_gene': 193487}, {'Feature_type': 193482}, {'Modification_date': 193482}, {'dbXrefs': 189808}, {'gene_id': 109248}, {'gene_name': 109248}, {'gene_stable_id': 109248}, {'transcript_stable_id': 109248}, {'name': 101149}, {'def': 64448}, {'synonym': 63709}, {'component_type': 47995}, {'EnsemblGeneID': 38395}, {'creation_date': 24034}, {'species': 22608}, {'property_value': 22426}, {'xref': 20076}, {'created_by': 19035}, {'relationship': 16329}, {'comment': 14216}, {'intersection_of': 10212}, {'is_obsolete': 8180}, {'alt_id': 4471}, {'category': 3714}, {'count': 3714}, {'critval': 3714}, {'event': 3714}, {'llr': 3714}, {'meddraCode': 3702}, {'subset': 3146}, {'replaced_by': 1699}, {'consider': 80}, {'disjoint_from': 23}, {'holds_over_chain': 12}, {'inverse_of': 1}, {'is_class_level': 1}, {'is_metadata_tag': 1}, {'is_transitive': 1}, {'transitive_over': 1}], 'edgeProperties': [{'count': 7230614}, {'intA': 7230614}, {'intABiologicalRole': 7230614}, {'intB': 7230614}, {'intBBiologicalRole': 7230614}, {'sourceDatabase': 7230614}, {'scoring': 7200058}, {'score': 2028557}, {'datatypeId': 1931724}, {'evidenceCount': 1931724}, {'literature': 1931724}, {'dbXRefs': 1759376}, {'aspect': 292852}, {'evidence': 292852}, {'geneProduct': 292852}, {'source_extract': 292852}, {'ascore': 96833}, {'dscore': 96833}, {'escore': 96833}, {'fscore': 96833}, {'ncbiTaxonId': 96833}, {'nscore': 96833}, {'preferredName_A': 96833}, {'preferredName_B': 96833}, {'pscore': 96833}, {'tscore': 96833}, {'pathway': 46818}, {'topLevelTerm': 46818}], 'totalNodePropertyValues': 3479555, 'totalEdgePropertyValues': 62400221}\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " numNodes numEdges numNodeLabels numEdgeLabels \\\n", + "0 426515 9750778 7 27 \n", + "\n", + " nodeLabels \\\n", + "0 [Drug, Phenotype, Gene, Disease, Pathway, Go, ... \n", + "\n", + " edgeLabels numNodeProperties \\\n", + "0 [is_a_phenotype, disease_has_feature_disease, ... 48 \n", + "\n", + " numEdgeProperties nodeProperties \\\n", + "0 28 [{'description': 219818}, {'LocusTag': 193500}... \n", + "\n", + " edgeProperties totalNodePropertyValues \\\n", + "0 [{'count': 7230614}, {'intA': 7230614}, {'intA... 3479555 \n", + "\n", + " totalEdgePropertyValues \n", + "0 62400221 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Get graph summary\n", + "\n", + "res1 = kg.get_graph_summary()#get graph summary\n", + "res1 = pd.json_normalize(res1)\n", + "res1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "41885b3f", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " edgeLabels\n", + "0 is_a_phenotype\n", + "0 disease_has_feature_disease\n", + "0 predisposes_towards_disease\n", + "0 has_characteristic_disease\n", + "0 has_part_go\n", + "0 targets\n", + "0 linked_to_pathway\n", + "0 happens_during_go\n", + "0 excluded_subClassOf_disease\n", + "0 part_of_go\n", + "0 mim2gene_medgen_disease\n", + "0 ends_during_go\n", + "0 is_a_pathway\n", + "0 linked_to\n", + "0 positively_regulates_go\n", + "0 part_of_progression_of_disease_disease\n", + "0 regulates_go\n", + "0 disease_arises_from_feature_disease\n", + "0 occurs_in_go\n", + "0 is_a_go\n", + "0 negatively_regulates_go\n", + "0 disease_has_major_feature_disease\n", + "0 intersection_of_disease\n", + "0 curated_content_resource_disease\n", + "0 disease_shares_features_of_disease\n", + "0 is_a_disease\n", + "0 interacts_with" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Edge Types\n", + "\n", + "res1[['edgeLabels']].explode('edgeLabels')" + ] + }, + { + "cell_type": "markdown", + "id": "05ae144a", + "metadata": { + "kernel": "Python3" + }, + "source": [ + "### Querying PollyKG" + ] + }, + { + "cell_type": "markdown", + "id": "9ee1d14d", + "metadata": { + "kernel": "Python3" + }, + "source": [ + "##### Two Query Languages Available\n", + "- Gremlin Query Language\n", + "- openCypher Query Language" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f05b89c", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " NodeType NodeCount\n", + "0 [Gene] 193500\n", + "1 [Protein] 109248\n", + "2 [Go] 47995\n", + "3 [Disease] 29967\n", + "4 [Pathway] 22608\n", + "5 [Phenotype] 19483\n", + "6 [Drug] 3714" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Find all Node Types - openCypher\n", + "\n", + "res2 = kg.run_opencypher_query(\"\"\"MATCH (n)\n", + " RETURN labels(n) AS NodeType, count(n) AS NodeCount\n", + " ORDER BY NodeCount DESC;\"\"\")\n", + "pd.json_normalize(res2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "27148de8", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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6Drug3714
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" + ], + "text/plain": [ + " NodeType NodeCount\n", + "0 Gene 193500\n", + "1 Protein 109248\n", + "2 Go 47995\n", + "3 Disease 29967\n", + "4 Pathway 22608\n", + "5 Phenotype 19483\n", + "6 Drug 3714" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Find all Node Types - Gremlin\n", + "\n", + "res2 = kg.run_gremlin_query(\"g.V().groupCount().by(label).order(local).by(values, desc)\")\n", + "\n", + "node = []\n", + "count = []\n", + "for i in range(0,len(res2['data']['@value'][0]['@value'])):\n", + " if (i+1)%2 == 0:\n", + " count.append(res2['data']['@value'][0]['@value'][i]['@value'])\n", + " else:\n", + " node.append(res2['data']['@value'][0]['@value'][i])\n", + "res2 = pd.DataFrame({'NodeType':node,'NodeCount':count})\n", + "res2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0675a1f9", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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EdgeTypeEdgeCount
0interacts_with7327447
1targets1931724
2linked_to292852
3encodes105924
4has_phenotype102425
5linked_to_pathway46818
6is_a_go40264
7is_a_disease39098
8has_indication36066
9is_a_pathway22746
10is_a_phenotype19032
11intersection_of_disease8309
12part_of_go6737
13excluded_subClassOf_disease3366
14regulates_go3132
15positively_regulates_go2816
16negatively_regulates_go2810
17curated_content_resource_disease1297
18has_characteristic_disease644
19has_part_go636
20predisposes_towards_disease374
21disease_has_feature_disease259
22occurs_in_go184
23disease_arises_from_feature_disease90
24disease_has_major_feature_disease54
25disease_shares_features_of_disease46
26mim2gene_medgen_disease27
27happens_during_go13
28part_of_progression_of_disease_disease2
29ends_during_go1
\n", + "
" + ], + "text/plain": [ + " EdgeType EdgeCount\n", + "0 interacts_with 7327447\n", + "1 targets 1931724\n", + "2 linked_to 292852\n", + "3 encodes 105924\n", + "4 has_phenotype 102425\n", + "5 linked_to_pathway 46818\n", + "6 is_a_go 40264\n", + "7 is_a_disease 39098\n", + "8 has_indication 36066\n", + "9 is_a_pathway 22746\n", + "10 is_a_phenotype 19032\n", + "11 intersection_of_disease 8309\n", + "12 part_of_go 6737\n", + "13 excluded_subClassOf_disease 3366\n", + "14 regulates_go 3132\n", + "15 positively_regulates_go 2816\n", + "16 negatively_regulates_go 2810\n", + "17 curated_content_resource_disease 1297\n", + "18 has_characteristic_disease 644\n", + "19 has_part_go 636\n", + "20 predisposes_towards_disease 374\n", + "21 disease_has_feature_disease 259\n", + "22 occurs_in_go 184\n", + "23 disease_arises_from_feature_disease 90\n", + "24 disease_has_major_feature_disease 54\n", + "25 disease_shares_features_of_disease 46\n", + "26 mim2gene_medgen_disease 27\n", + "27 happens_during_go 13\n", + "28 part_of_progression_of_disease_disease 2\n", + "29 ends_during_go 1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Find all Edge Types - Gremlin\n", + "\n", + "res3 = kg.run_gremlin_query(\"\"\"g.E().groupCount().by(label).order(local).by(values, desc)\"\"\")\n", + "\n", + "edge = []\n", + "count = []\n", + "for i in range(0,len(res3['data']['@value'][0]['@value'])):\n", + " if (i+1)%2 == 0:\n", + " count.append(res3['data']['@value'][0]['@value'][i]['@value'])\n", + " else:\n", + " edge.append(res3['data']['@value'][0]['@value'][i])\n", + "\n", + "res3 = pd.DataFrame({'EdgeType':edge,'EdgeCount':count})\n", + "res3 " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ea30d120", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': {'@type': 'g:List',\n", + " '@value': [{'@type': 'g:Map',\n", + " '@value': ['To_Node_Type', 'Gene', 'From_Node_Type', 'Gene']}]},\n", + " 'meta': {'@type': 'g:Map', '@value': []}}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Find the Node Types that an Edge Connects - Gremlin\n", + "\n", + "res4 = kg.run_gremlin_query(\"\"\"g.E().hasLabel('interacts_with')\n", + " .limit(10000)\n", + " .project('From_Node_Type', 'To_Node_Type')\n", + " .by(outV().label()) \n", + " .by(inV().label()) \n", + " .dedup()\"\"\")\n", + "res4" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5560076a", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': {'@type': 'g:List',\n", + " '@value': ['EnsemblGeneID',\n", + " 'Symbol',\n", + " 'LocusTag',\n", + " 'Synonyms',\n", + " 'dbXrefs',\n", + " 'chromosome',\n", + " 'map_location',\n", + " 'description',\n", + " 'type_of_gene',\n", + " 'Symbol_from_nomenclature_authority',\n", + " 'Nomenclature_status',\n", + " 'Modification_date',\n", + " 'Feature_type',\n", + " 'tax_id']},\n", + " 'meta': {'@type': 'g:Map', '@value': []}}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Properties of a Node - Gremlin\n", + "\n", + "res5 = kg.run_gremlin_query(\"g.V().hasLabel('Gene').properties().key().dedup()\")\n", + "res5" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2990e6aa", + "metadata": { + "kernel": "Python3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'properties': ['Symbol',\n", + " 'tax_id',\n", + " 'dbXrefs',\n", + " 'LocusTag',\n", + " 'Synonyms',\n", + " 'chromosome',\n", + " 'description',\n", + " 'Feature_type',\n", + " 'map_location',\n", + " 'type_of_gene',\n", + " 'EnsemblGeneID',\n", + " 'Modification_date',\n", + " 'Nomenclature_status',\n", + " 'Symbol_from_nomenclature_authority']}]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Properties of a Node - openCypher\n", + "\n", + "res5 = kg.run_opencypher_query(\"\"\"MATCH (n:Gene)\n", + " RETURN keys(n) AS properties\n", + " LIMIT 1;\"\"\")\n", + "res5" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SoS", + "language": "sos", + "name": "sos" + }, + "language_info": { + "codemirror_mode": "sos", + "file_extension": ".sos", + "mimetype": "text/x-sos", + "name": "sos", + "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", + "pygments_lexer": "sos" + }, + "sos": { + "kernels": [ + [ + "Python3", + "python3", + "python3", + "", + "" + ] + ], + "panel": { + "displayed": false, + "height": 0 + }, + "version": "0.24.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workspaces/working with workspaces via polly python.ipynb b/workspaces/working with workspaces via polly python.ipynb new file mode 100644 index 0000000..403315f --- /dev/null +++ b/workspaces/working with 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"Requirement already satisfied: pluggy<2.0,>=0.12 in ./.environments/miniconda/envs/polly/lib/python3.10/site-packages (from pytest>=6.2.5->polly-python==3.1.0) (1.3.0)\n", + "Requirement already satisfied: pycparser in ./.environments/miniconda/envs/polly/lib/python3.10/site-packages (from cffi>=1.12->cryptography<=38.0.0,>=37.0.1->polly-python==3.1.0) (2.21)\n", + "Installing collected packages: tabulate, charset-normalizer, requests, cmapPy, polly-python\n", + " Attempting uninstall: tabulate\n", + " Found existing installation: tabulate 0.8.10\n", + " Uninstalling tabulate-0.8.10:\n", + " Successfully uninstalled tabulate-0.8.10\n", + " Attempting uninstall: charset-normalizer\n", + " Found existing installation: charset-normalizer 3.2.0\n", + " Uninstalling charset-normalizer-3.2.0:\n", + " Successfully uninstalled charset-normalizer-3.2.0\n", + " Attempting uninstall: requests\n", + " Found existing installation: requests 2.31.0\n", + " Uninstalling requests-2.31.0:\n", + " Successfully uninstalled requests-2.31.0\n", + " Attempting uninstall: polly-python\n", + " Found existing installation: polly-python 1.0.0\n", + " Uninstalling polly-python-1.0.0:\n", + " Successfully uninstalled polly-python-1.0.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "polly-validator 0.0.2 requires requests==2.25.1, but you have requests 2.28.1 which is incompatible.\n", + "polly-validator 0.0.2 requires tabulate==0.8.10, but you have tabulate 0.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed charset-normalizer-2.1.1 cmapPy-4.0.1 polly-python-3.1.0 requests-2.28.1 tabulate-0.9.0\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install polly-python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bc653a8", + "metadata": {}, + "outputs": [], + "source": [ + "# import\n", + "\n", + "import os\n", + "import json\n", + "from polly.auth import Polly\n", + "from polly.workspaces import Workspaces\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "384c3ba2", + "metadata": {}, + "outputs": [], + "source": [ + "# authentication \n", + "\n", + "AUTH_KEY = \"auth key\"\n", + "Polly.auth(AUTH_KEY, env=\"polly\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fe534db", + "metadata": {}, + "outputs": [], + "source": [ + "# connect to workspace\n", + "\n", + "workspaces = Workspaces()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e24c8617", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Workspace_idWorkspace_namestatusdescriptionlast_modifiedtag_namesfavouritewatch
016562test_function_anshuactiveNone2024-09-30 11:13:02[]FalseFalse
116563test_srishtiactive2024-09-27 10:06:49[]FalseFalse
216556new WS 21active2024-09-25 08:27:05[]FalseFalse
316531_PRD-870_Restoredactive2024-09-25 07:04:58[{'tag_name': 'PRD-870'}]FalseTrue
415487Test 2_Restoredactive2024-09-19 12:35:14[]FalseFalse
...........................
18814454OA_484archived2022-06-03 12:51:52[]FalseFalse
1895803TEST_NOTIFICATIONSactive2022-05-16 07:30:58[]FalseFalse
1904060Search_OGUI24_Restored_Restoredarchived2022-05-09 12:01:40[]FalseFalse
1914026Copy URL workspaceactive2022-03-30 07:07:09[]FalseTrue
1924028Notebooksactive2022-03-24 09:52:17[]TrueFalse
\n", + "

193 rows × 8 columns

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" + ], + "text/plain": [ + " Workspace_id Workspace_name status description \\\n", + "0 16562 test_function_anshu active None \n", + "1 16563 test_srishti active \n", + "2 16556 new WS 21 active \n", + "3 16531 _PRD-870_Restored active \n", + "4 15487 Test 2_Restored active \n", + ".. ... ... ... ... \n", + "188 14454 OA_484 archived \n", + "189 5803 TEST_NOTIFICATIONS active \n", + "190 4060 Search_OGUI24_Restored_Restored archived \n", + "191 4026 Copy URL workspace active \n", + "192 4028 Notebooks active \n", + "\n", + " last_modified tag_names favourite watch \n", + "0 2024-09-30 11:13:02 [] False False \n", + "1 2024-09-27 10:06:49 [] False False \n", + "2 2024-09-25 08:27:05 [] False False \n", + "3 2024-09-25 07:04:58 [{'tag_name': 'PRD-870'}] False True \n", + "4 2024-09-19 12:35:14 [] False False \n", + ".. ... ... ... ... \n", + "188 2022-06-03 12:51:52 [] False False \n", + "189 2022-05-16 07:30:58 [] False False \n", + "190 2022-05-09 12:01:40 [] False False \n", + "191 2022-03-30 07:07:09 [] False True \n", + "192 2022-03-24 09:52:17 [] True False \n", + "\n", + "[193 rows x 8 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get all workspaces\n", + "\n", + "workspaces.fetch_my_workspaces()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d891f0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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file_namesizelast_modified
0folder1--
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" + ], + "text/plain": [ + " file_name size last_modified\n", + "0 folder1 - -" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get content of a workspace with workspace id\n", + "\n", + "query = workspaces.list_contents(workspace_id)\n", + "query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db6d40c2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Workspace Created !\n" + ] + }, + { + "data": { + "text/plain": [ + "{'name': 'test_2024_04Nov',\n", + " 'description': None,\n", + " 'project_property': {'type': 'workspaces', 'labels': ''},\n", + " 'created_time': '2024-11-04 09:48:30',\n", + " 'last_modified': '2024-11-04 09:48:30',\n", + " 'status': 1,\n", + " 'creator': 1647238511,\n", + " 'organisation': 1,\n", + " 'id': 16579,\n", + " 'tag_names': []}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#create new workspace with given name\n", + "\n", + "workspaces.create_workspace(\"test_2024_04Nov\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b60926a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Workspace_copy started, You will be notified upon completion.\n", + "Copy Operation Successful!\n" + ] + } + ], + "source": [ + "# copy content from one workspace to another\n", + "\n", + "workspaces.create_copy({sorce workspace id}, \"source path\", {destination workspace id}, \"destination path\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f11bcdf4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Download successful to path=/import\n" + ] + } + ], + "source": [ + "# download from a workspace\n", + "\n", + "workspaces.download_from_workspaces({workspace id}, \"workspace source path\", \"destination local path\", copy_workspace_path=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c492f025", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Upload successful on workspace-id=16579.\n" + ] + } + ], + "source": [ + "# upload from local to workspace\n", + "\n", + "workspaces.upload_to_workspaces({workspace id}, \"workspace destination path\", \"local source path\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b77b457", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Sync successful on workspace-id=16563.\n" + ] + } + ], + "source": [ + "# sync workspace data from workspace to local\n", + "\n", + "workspaces.sync_data({workspace id}, \"polly://{workspace sorce path}\",\"local destination path\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}