diff --git a/docs/Tutorials/T1_ImageAnalysis.ipynb b/docs/Tutorials/T1_ImageAnalysis.ipynb index b837899..0ba1f3d 100644 --- a/docs/Tutorials/T1_ImageAnalysis.ipynb +++ b/docs/Tutorials/T1_ImageAnalysis.ipynb @@ -5,7 +5,7 @@ "id": "29cad22f", "metadata": {}, "source": [ - "# Image Analysis" + "# Image single-cell analysis" ] }, { @@ -13,7 +13,17 @@ "id": "28f2691f", "metadata": {}, "source": [ - "In this tutorial I will show you how to perform quality control on your image processing and segmentation results." + "In this tutorial I will show you how to perform quality control on your image processing and segmentation results. \n", + "This tutorial assumes that you have already processed and quantified your images, and thus you have a quantification matrix. \n", + "For information to how to get here from raw images go to the image processing section." + ] + }, + { + "cell_type": "markdown", + "id": "c9ad3ffb", + "metadata": {}, + "source": [ + "## Import necesary packages and data" ] }, { @@ -26,7 +36,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/.pixi/envs/spatialdata/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } @@ -46,63 +56,63 @@ }, { "cell_type": "markdown", - "id": "369fbdfd", + "id": "c21a257a", + "metadata": {}, + "source": [ + "### Let's download the demo data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7930e707", "metadata": {}, + "outputs": [], "source": [ - "## Part 1: Visualize segmentation in QuPath" + "url = \"https://zenodo.org/records/15830141/files/data.tar.gz?download=1\"\n", + "output_path = \"demodata.gz\" \n", + "\n", + "import requests\n", + "response = requests.get(url, stream=True)\n", + "response.raise_for_status()\n", + "\n", + "with open(output_path, \"wb\") as f:\n", + " for chunk in response.iter_content(chunk_size=8192):\n", + " if chunk:\n", + " f.write(chunk)\n", + "\n", + "print(f\"Download complete: {output_path}\")" ] }, { "cell_type": "markdown", - "id": "1326fcee", + "id": "cf9be848", "metadata": {}, "source": [ - "QuPath is a great piece of software created to allow users to see their images in a smooth manner." + "Now you have to decompress it" ] }, { "cell_type": "markdown", - "id": "0ba4c6ea", + "id": "369fbdfd", "metadata": {}, "source": [ - "Here is our demo data, this is how it should look like" + "## Part 1: Visualize segmentation" ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "a41afe36", + "cell_type": "markdown", + "id": "1326fcee", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;36m.\u001b[0m\n", - "├── Tutorial_1.ipynb\n", - "├── \u001b[1;36mdata\u001b[0m\n", - "│   ├── \u001b[1;36mimage\u001b[0m\n", - "│   │   └── \u001b[31mmIF.ome.tif\u001b[0m\n", - "│   ├── \u001b[1;36mmanual_artefact_annotations\u001b[0m\n", - "│   │   └── \u001b[31martefacts.geojson\u001b[0m\n", - "│   ├── \u001b[1;36mquantification\u001b[0m\n", - "│   │   └── \u001b[31mquant.csv\u001b[0m\n", - "│   └── \u001b[1;36msegmentation\u001b[0m\n", - "│   └── \u001b[31msegmentation_mask.tif\u001b[0m\n", - "└── \u001b[1;36moutputs\u001b[0m\n", - " └── segmentation_for_qupath.geojson\n", - "\n", - "7 directories, 6 files\n" - ] - } - ], "source": [ - "! tree" + "First step is to ensure that the segmentation is good enough. Cell segmentation is a critical step and it must be controlled. \n", + "QuPath is a great piece of software created to allow users to see their images in a smooth manner. \n", + "However QuPath is usually not happy with us dropping a .tif mask into it.. Therefore, openDVP has utilities for translating a standard segmentation mask into QuPath compatible shapes, these shapes also allow you to continue part of your analysis in QuPath if you want." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "2383e316", "metadata": {}, "outputs": [], @@ -114,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "32344e09", "metadata": {}, "outputs": [ @@ -134,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "413d5d1d", "metadata": {}, "outputs": [ @@ -142,17 +152,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/x7/grkjlk8s223dy6234rnz1885mxz2_6/T/ipykernel_14111/2237838827.py:2: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n", + "/var/folders/x7/grkjlk8s223dy6234rnz1885mxz2_6/T/ipykernel_33694/2237838827.py:2: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n", " io.imshow(seg, vmax=1)\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -172,68 +182,75 @@ "io.imshow(seg, vmax=1)" ] }, - { - "cell_type": "markdown", - "id": "f77cf31f", - "metadata": {}, - "source": [ - "## Let's visualize interactively in Napari or Qupath" - ] - }, - { - "cell_type": "markdown", - "id": "bafeede8", - "metadata": {}, - "source": [ - "### For Napari" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "28c2f46e", - "metadata": {}, - "outputs": [], - "source": [ - "#load image\n", - "image = io.imread(\"data/image/mIF.ome.tif\")" - ] - }, { "cell_type": "code", - "execution_count": 10, - "id": "c307056c", + "execution_count": 5, + "id": "5fd2f1cf", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/x7/grkjlk8s223dy6234rnz1885mxz2_6/T/ipykernel_33694/1375747239.py:2: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n", + " io.imshow(seg[:500,:500])\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/.pixi/envs/spatialdata/lib/python3.12/site-packages/skimage/io/_plugins/matplotlib_plugin.py:158: UserWarning: Low image data range; displaying image with stretched contrast.\n", + " lo, hi, cmap = _get_display_range(image)\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# this should produce a napari window with image and segmentation mask\n", - "viewer = napari.Viewer()\n", - "viewer.add_image(image, name=\"mIF_image\")\n", - "viewer.add_labels(seg, name='Segmentation')" + "# we can zoom in to check\n", + "io.imshow(seg[:500,:500])" + ] + }, + { + "cell_type": "markdown", + "id": "57d6189d", + "metadata": {}, + "source": [ + "You could plot and check various regions of the tissue, and overlay with the image, but this starts to get bulky and slow. We recommend an interactive session where one can zoom in and out, and check different channels." + ] + }, + { + "cell_type": "markdown", + "id": "5e69c3e6", + "metadata": {}, + "source": [ + "### Visualize interactively in QuPath" ] }, { "cell_type": "markdown", - "id": "7084a8db", + "id": "a9ca7de7", "metadata": {}, "source": [ - "### For QuPath" + "Now let's transform this into polygons that QuPath can digest" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "76dc97ba", "metadata": {}, "outputs": [ @@ -241,7 +258,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/dask/dataframe/__init__.py:31: FutureWarning: The legacy Dask DataFrame implementation is deprecated and will be removed in a future version. Set the configuration option `dataframe.query-planning` to `True` or None to enable the new Dask Dataframe implementation and silence this warning.\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/.pixi/envs/spatialdata/lib/python3.12/site-packages/dask/dataframe/__init__.py:31: FutureWarning: The legacy Dask DataFrame implementation is deprecated and will be removed in a future version. Set the configuration option `dataframe.query-planning` to `True` or None to enable the new Dask Dataframe implementation and silence this warning.\n", " warnings.warn(\n" ] }, @@ -256,7 +273,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/.pixi/envs/spatialdata/lib/python3.12/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", " from pkg_resources import DistributionNotFound, get_distribution\n" ] }, @@ -264,21 +281,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m13:18:26.98\u001b[0m | \u001b[1mINFO\u001b[0m | Simplifying the geometry with tolerance 1\n" + "\u001b[32m16:13:05.55\u001b[0m | \u001b[1mINFO\u001b[0m | Simplifying the geometry with tolerance 1\n" ] - } - ], - "source": [ - "# transform mask into shapes\n", - "gdf = dvp.io.segmask_to_qupath(path_to_segmentation, simplify_value=1, save_as_detection=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e16acc04", - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ @@ -349,18 +354,20 @@ "5 POLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ... detection" ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# transform mask into a geodataframe containing the polygons\n", + "gdf = dvp.io.segmask_to_qupath(path_to_segmentation, simplify_value=1, save_as_detection=True)\n", "gdf.head()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "id": "b84aeade", "metadata": {}, "outputs": [ @@ -368,21 +375,81 @@ "name": "stderr", "output_type": "stream", "text": [ - "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/pyogrio/geopandas.py:710: UserWarning: 'crs' was not provided. The output dataset will not have projection information defined and may not be usable in other systems.\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/.pixi/envs/spatialdata/lib/python3.12/site-packages/pyogrio/geopandas.py:710: UserWarning: 'crs' was not provided. The output dataset will not have projection information defined and may not be usable in other systems.\n", " write(\n" ] } ], "source": [ + "# now lets write that geodataframe into a file\n", "gdf.to_file(\"outputs/segmentation_for_qupath.geojson\")" ] }, { "cell_type": "markdown", - "id": "f1e11457", + "id": "cc7b8206", + "metadata": {}, + "source": [ + "Now open QuPath, drag the image into it, and then draft that .geojson file, you should be able to see the shapes :)" + ] + }, + { + "cell_type": "markdown", + "id": "8d5b02d9", + "metadata": {}, + "source": [ + "\"viz_segmentation_in_qupath\"" + ] + }, + { + "cell_type": "markdown", + "id": "f77cf31f", + "metadata": {}, + "source": [ + "### Visualize interactively in Napari" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "28c2f46e", + "metadata": {}, + "outputs": [], + "source": [ + "#load image\n", + "image = io.imread(\"data/image/mIF.ome.tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c307056c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this should produce a napari window with image and segmentation mask\n", + "viewer = napari.Viewer()\n", + "viewer.add_image(image, name=\"mIF_image\")\n", + "viewer.add_labels(seg, name='Segmentation')" + ] + }, + { + "cell_type": "markdown", + "id": "f25a332f", "metadata": {}, "source": [ - "This file you just drag and drop into qupath after you have loaded the image." + "Napari is a great python-based software that keeps getting better. It has a little learning curve, I suggest you check [Loading multichannel images](https://napari.org/stable/howtos/layers/image.html#loading-multichannel-images) for more details of image controls and check this [Segmentation masks info](https://napari.org/stable/howtos/layers/labels.html#creating-deleting-merging-and-splitting-connected-components)." ] }, { @@ -390,12 +457,22 @@ "id": "d2b42ba2", "metadata": {}, "source": [ - "## Quant to adata" + "## Part 2: Analyze the single cell data" + ] + }, + { + "cell_type": "markdown", + "id": "86375022", + "metadata": {}, + "source": [ + "openDVP follows the guidelines of the scverse ecosystem for the most replicable and interoperable data formats and functions python can offer life scientists and bioinformaticians. \n", + "A big part is using [AnnData](https://anndata.readthedocs.io/en/stable/), a really nice data object that stores data and its metadata all together. \n", + "For life scientists it also means you could use functions already created by very popular and well-maintained packages, like [Scanpy](https://scanpy.readthedocs.io/en/1.10.x/index.html)." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "ed1f9c70", "metadata": {}, "outputs": [ @@ -403,20 +480,58 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m15:14:06.74\u001b[0m | \u001b[1mINFO\u001b[0m | Detected 0 in 'CellID' — shifting all values by +1 for 1-based indexing.\n", - "\u001b[32m15:14:06.75\u001b[0m | \u001b[1mINFO\u001b[0m | 16808 cells and 15 variables\n" + "\u001b[32m16:13:11.75\u001b[0m | \u001b[1mINFO\u001b[0m | Detected 0 in 'CellID' — shifting all values by +1 for 1-based indexing.\n", + "\u001b[32m16:13:11.75\u001b[0m | \u001b[1mINFO\u001b[0m | 16808 cells and 15 variables\n" ] } ], "source": [ + "#transform the .csv matrix to an AnnData object\n", "adata = dvp.io.quant_to_adata(\"data/quantification/quant.csv\")" ] }, + { + "cell_type": "markdown", + "id": "d7a56e09", + "metadata": {}, + "source": [ + "adata is composed of three main compartments: \n", + "1. X , which stores all the numerical data\n", + "2. adata.obs, which stores all the metadata of the observations (here X,Y coordinates, morphological features)\n", + "3. adata.var, which stores all the metadata for the variables, aka the markers" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "11c88879", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5.70393701, 7.23228346, 5.71811024, 29.96377953, 21.42992126],\n", + " [ 5.53472222, 6.14583333, 4.83159722, 23.3125 , 13.00520833],\n", + " [ 5.56097561, 6.38922764, 4.97764228, 24.02845528, 15.96341463],\n", + " [ 5.52616279, 6.00581395, 4.74127907, 21.62209302, 10.7122093 ],\n", + " [ 5.5971564 , 6.38125329, 5.00105319, 24.71879937, 22.37651395]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#chech first 25 values of adata.X\n", + "adata.X[:5,:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9f3643d3", + "metadata": {}, "outputs": [ { "data": { @@ -517,148 +632,40 @@ " 196.610173\n", " 0.896601\n", " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 16803\n", - " 16804\n", - " 4993.579685\n", - " 106.373030\n", - " 571.0\n", - " 52.291050\n", - " 14.719719\n", - " 0.959562\n", - " -1.563557\n", - " 116.870058\n", - " 0.976068\n", - " \n", - " \n", - " 16804\n", - " 16805\n", - " 4993.413242\n", - " 810.385845\n", - " 438.0\n", - " 40.105247\n", - " 14.334812\n", - " 0.933940\n", - " 1.531591\n", - " 90.970563\n", - " 0.962637\n", - " \n", - " \n", - " 16805\n", - " 16806\n", - " 4994.153535\n", - " 645.010101\n", - " 495.0\n", - " 50.864135\n", - " 13.112118\n", - " 0.966202\n", - " 1.538209\n", - " 111.248737\n", - " 0.951923\n", - " \n", - " \n", - " 16806\n", - " 16807\n", - " 4993.835570\n", - " 1244.718121\n", - " 298.0\n", - " 28.947821\n", - " 13.495126\n", - " 0.884686\n", - " -1.561806\n", - " 69.213203\n", - " 0.973856\n", - " \n", - " \n", - " 16807\n", - " 16808\n", - " 4994.250774\n", - " 4186.876161\n", - " 323.0\n", - " 33.325316\n", - " 13.027177\n", - " 0.920429\n", - " -1.564638\n", - " 77.213203\n", - " 0.984756\n", - " \n", " \n", "\n", - "

16808 rows × 10 columns

\n", "" ], "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 \n", - "1 2 6.598958 126.006944 576.0 45.835698 \n", - "2 3 17.416667 156.656504 984.0 40.751104 \n", - "3 4 4.982558 179.337209 344.0 34.620290 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 \n", - "... ... ... ... ... ... \n", - "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity \n", - "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", - "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", - "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", - "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", - "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", - "... ... ... ... ... ... \n", - "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", + " CellID Y_centroid X_centroid Area MajorAxisLength MinorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 36.841132 \n", + "1 2 6.598958 126.006944 576.0 45.835698 18.372329 \n", + "2 3 17.416667 156.656504 984.0 40.751104 31.700565 \n", + "3 4 4.982558 179.337209 344.0 34.620290 13.577757 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 49.053930 \n", "\n", - "[16808 rows x 10 columns]" + " Eccentricity Orientation Extent Solidity \n", + "0 0.644782 0.359469 146.669048 0.949178 \n", + "1 0.916152 1.513685 113.112698 0.886154 \n", + "2 0.628380 -1.528462 121.396970 0.955340 \n", + "3 0.919884 1.474818 82.627417 0.971751 \n", + "4 0.433912 1.287374 196.610173 0.896601 " ] }, - "execution_count": 5, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "adata.obs" - ] - }, - { - "cell_type": "markdown", - "id": "fcd7d1c3", - "metadata": {}, - "source": [ - "## Filter cells" - ] - }, - { - "cell_type": "markdown", - "id": "bdf02b44", - "metadata": {}, - "source": [ - "### Filter cells too big" + "# cell metadata\n", + "adata.obs.head()" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "dc172d8f", + "execution_count": 13, + "id": "a2560369", "metadata": {}, "outputs": [ { @@ -682,209 +689,55 @@ " \n", " \n", " \n", - " CellID\n", - " Y_centroid\n", - " X_centroid\n", - " Area\n", - " MajorAxisLength\n", - " MinorAxisLength\n", - " Eccentricity\n", - " Orientation\n", - " Extent\n", - " Solidity\n", " \n", " \n", " \n", " \n", - " 0\n", - " 1\n", - " 17.612598\n", - " 53.337008\n", - " 1270.0\n", - " 48.198269\n", - " 36.841132\n", - " 0.644782\n", - " 0.359469\n", - " 146.669048\n", - " 0.949178\n", - " \n", - " \n", - " 1\n", - " 2\n", - " 6.598958\n", - " 126.006944\n", - " 576.0\n", - " 45.835698\n", - " 18.372329\n", - " 0.916152\n", - " 1.513685\n", - " 113.112698\n", - " 0.886154\n", - " \n", - " \n", - " 2\n", - " 3\n", - " 17.416667\n", - " 156.656504\n", - " 984.0\n", - " 40.751104\n", - " 31.700565\n", - " 0.628380\n", - " -1.528462\n", - " 121.396970\n", - " 0.955340\n", - " \n", - " \n", - " 3\n", - " 4\n", - " 4.982558\n", - " 179.337209\n", - " 344.0\n", - " 34.620290\n", - " 13.577757\n", - " 0.919884\n", - " 1.474818\n", - " 82.627417\n", - " 0.971751\n", - " \n", - " \n", - " 4\n", - " 5\n", - " 19.159558\n", - " 228.598210\n", - " 1899.0\n", - " 54.446578\n", - " 49.053930\n", - " 0.433912\n", - " 1.287374\n", - " 196.610173\n", - " 0.896601\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 16803\n", - " 16804\n", - " 4993.579685\n", - " 106.373030\n", - " 571.0\n", - " 52.291050\n", - " 14.719719\n", - " 0.959562\n", - " -1.563557\n", - " 116.870058\n", - " 0.976068\n", + " mean_750_bg\n", " \n", " \n", - " 16804\n", - " 16805\n", - " 4993.413242\n", - " 810.385845\n", - " 438.0\n", - " 40.105247\n", - " 14.334812\n", - " 0.933940\n", - " 1.531591\n", - " 90.970563\n", - " 0.962637\n", + " mean_647_bg\n", " \n", " \n", - " 16805\n", - " 16806\n", - " 4994.153535\n", - " 645.010101\n", - " 495.0\n", - " 50.864135\n", - " 13.112118\n", - " 0.966202\n", - " 1.538209\n", - " 111.248737\n", - " 0.951923\n", + " mean_555_bg\n", " \n", " \n", - " 16806\n", - " 16807\n", - " 4993.835570\n", - " 1244.718121\n", - " 298.0\n", - " 28.947821\n", - " 13.495126\n", - " 0.884686\n", - " -1.561806\n", - " 69.213203\n", - " 0.973856\n", + " mean_488_bg\n", " \n", " \n", - " 16807\n", - " 16808\n", - " 4994.250774\n", - " 4186.876161\n", - " 323.0\n", - " 33.325316\n", - " 13.027177\n", - " 0.920429\n", - " -1.564638\n", - " 77.213203\n", - " 0.984756\n", + " mean_DAPI_bg\n", " \n", " \n", "\n", - "

16808 rows × 10 columns

\n", "" ], "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 \n", - "1 2 6.598958 126.006944 576.0 45.835698 \n", - "2 3 17.416667 156.656504 984.0 40.751104 \n", - "3 4 4.982558 179.337209 344.0 34.620290 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 \n", - "... ... ... ... ... ... \n", - "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity \n", - "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", - "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", - "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", - "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", - "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", - "... ... ... ... ... ... \n", - "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", - "\n", - "[16808 rows x 10 columns]" + "Empty DataFrame\n", + "Columns: []\n", + "Index: [mean_750_bg, mean_647_bg, mean_555_bg, mean_488_bg, mean_DAPI_bg]" ] }, - "execution_count": 6, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "adata.obs" + "# variable metadata, now empty\n", + "adata.var.head()" + ] + }, + { + "cell_type": "markdown", + "id": "fcd7d1c3", + "metadata": {}, + "source": [ + "### Filter cells by Area" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "id": "f645161a", "metadata": {}, "outputs": [ @@ -892,22 +745,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_abs_value for feature 'Area'...\n", - "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Feature 'Area' identified from adata.obs.\n", - "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'Area' >= 295.0000 (from quantile bound: 0.01).\n", - "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'Area' <= 2376.4400 (from quantile bound: 0.99).\n", - "\u001b[32m15:14:06.77\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | 16475 of 16808 cells (98.02%) passed the filter.\n", - "\u001b[32m15:14:06.78\u001b[0m | \u001b[1mINFO\u001b[0m | New boolean column 'Area_filter' added to adata.obs.\n" + "\u001b[32m16:13:11.84\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_abs_value for feature 'Area'...\n", + "\u001b[32m16:13:11.84\u001b[0m | \u001b[1mINFO\u001b[0m | Feature 'Area' identified from adata.obs.\n", + "\u001b[32m16:13:11.84\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'Area' >= 295.0000 (from quantile bound: 0.01).\n", + "\u001b[32m16:13:11.84\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'Area' <= 2376.4400 (from quantile bound: 0.99).\n", + "\u001b[32m16:13:11.84\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | 16475 of 16808 cells (98.02%) passed the filter.\n", + "\u001b[32m16:13:11.84\u001b[0m | \u001b[1mINFO\u001b[0m | New boolean column 'Area_filter' added to adata.obs.\n" ] } ], "source": [ - "adata = dvp.tl.filter_by_abs_value(adata=adata,feature_name=\"Area\", lower_bound=0.01, upper_bound=0.99, mode=\"quantile\")" + "# filter cells that are too big or too small\n", + "adata = dvp.tl.filter_by_abs_value(\n", + " adata=adata, feature_name=\"Area\", \n", + " lower_bound=0.01, upper_bound=0.99, mode=\"quantile\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "810c79f5", "metadata": {}, "outputs": [ @@ -933,15 +789,6 @@ " \n", " \n", " CellID\n", - " Y_centroid\n", - " X_centroid\n", - " Area\n", - " MajorAxisLength\n", - " MinorAxisLength\n", - " Eccentricity\n", - " Orientation\n", - " Extent\n", - " Solidity\n", " Area_filter\n", " \n", " \n", @@ -949,345 +796,158 @@ " \n", " 0\n", " 1\n", - " 17.612598\n", - " 53.337008\n", - " 1270.0\n", - " 48.198269\n", - " 36.841132\n", - " 0.644782\n", - " 0.359469\n", - " 146.669048\n", - " 0.949178\n", " True\n", " \n", " \n", " 1\n", " 2\n", - " 6.598958\n", - " 126.006944\n", - " 576.0\n", - " 45.835698\n", - " 18.372329\n", - " 0.916152\n", - " 1.513685\n", - " 113.112698\n", - " 0.886154\n", " True\n", " \n", " \n", " 2\n", " 3\n", - " 17.416667\n", - " 156.656504\n", - " 984.0\n", - " 40.751104\n", - " 31.700565\n", - " 0.628380\n", - " -1.528462\n", - " 121.396970\n", - " 0.955340\n", " True\n", " \n", " \n", " 3\n", " 4\n", - " 4.982558\n", - " 179.337209\n", - " 344.0\n", - " 34.620290\n", - " 13.577757\n", - " 0.919884\n", - " 1.474818\n", - " 82.627417\n", - " 0.971751\n", " True\n", " \n", " \n", " 4\n", " 5\n", - " 19.159558\n", - " 228.598210\n", - " 1899.0\n", - " 54.446578\n", - " 49.053930\n", - " 0.433912\n", - " 1.287374\n", - " 196.610173\n", - " 0.896601\n", - " True\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 16803\n", - " 16804\n", - " 4993.579685\n", - " 106.373030\n", - " 571.0\n", - " 52.291050\n", - " 14.719719\n", - " 0.959562\n", - " -1.563557\n", - " 116.870058\n", - " 0.976068\n", - " True\n", - " \n", - " \n", - " 16804\n", - " 16805\n", - " 4993.413242\n", - " 810.385845\n", - " 438.0\n", - " 40.105247\n", - " 14.334812\n", - " 0.933940\n", - " 1.531591\n", - " 90.970563\n", - " 0.962637\n", - " True\n", - " \n", - " \n", - " 16805\n", - " 16806\n", - " 4994.153535\n", - " 645.010101\n", - " 495.0\n", - " 50.864135\n", - " 13.112118\n", - " 0.966202\n", - " 1.538209\n", - " 111.248737\n", - " 0.951923\n", - " True\n", - " \n", - " \n", - " 16806\n", - " 16807\n", - " 4993.835570\n", - " 1244.718121\n", - " 298.0\n", - " 28.947821\n", - " 13.495126\n", - " 0.884686\n", - " -1.561806\n", - " 69.213203\n", - " 0.973856\n", - " True\n", - " \n", - " \n", - " 16807\n", - " 16808\n", - " 4994.250774\n", - " 4186.876161\n", - " 323.0\n", - " 33.325316\n", - " 13.027177\n", - " 0.920429\n", - " -1.564638\n", - " 77.213203\n", - " 0.984756\n", " True\n", " \n", " \n", "\n", - "

16808 rows × 11 columns

\n", "" ], "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 \n", - "1 2 6.598958 126.006944 576.0 45.835698 \n", - "2 3 17.416667 156.656504 984.0 40.751104 \n", - "3 4 4.982558 179.337209 344.0 34.620290 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 \n", - "... ... ... ... ... ... \n", - "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", - "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", - "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", - "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", - "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", - "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", - "... ... ... ... ... ... \n", - "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", - "\n", - " Area_filter \n", - "0 True \n", - "1 True \n", - "2 True \n", - "3 True \n", - "4 True \n", - "... ... \n", - "16803 True \n", - "16804 True \n", - "16805 True \n", - "16806 True \n", - "16807 True \n", - "\n", - "[16808 rows x 11 columns]" + " CellID Area_filter\n", + "0 1 True\n", + "1 2 True\n", + "2 3 True\n", + "3 4 True\n", + "4 5 True" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "adata.obs" + "# this column has now been added to adata.obs\n", + "adata.obs[[\"CellID\",\"Area_filter\"]].head()" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "d2d55fd3", + "execution_count": 16, + "id": "4f5b4be6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Area_filter\n", + "True 16475\n", + "False 333\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we see that 333 cells have been labelled as not passing the filter\n", + "adata.obs.Area_filter.value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "3aa02d51", + "metadata": {}, + "source": [ + "Filtering here does not actually filter the dataset. We just added a column to the adata.obs that describes the status of cells relative to that filter. This is important because then we should check what cells are going to be filtered out, and we can add all the filters we prefer and then filter the dataset based on the desired filters. " + ] + }, + { + "cell_type": "markdown", + "id": "3f0aa7e5", "metadata": {}, - "outputs": [], "source": [ - "# explain why adding a column instead of just outright filtering is important" + "### Filter by initial nuclear stain signal" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "ee4c756d", + "execution_count": 17, + "id": "20844eba", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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IOwAAwNEIOwAAwNEIOwAAwNFsXS4CcBuPxyO7du0y15s0aSKxsXzfAIBwI+zgrP5gK/5oV52+b3e/vMRcn35vtln/DQAQXoQdVPsPdnJahhwp3ssf7TOk75vX45Hdu3f77iMwAkD4EHZQ7T/YKemZdp9G1Cot2ScPv10gaQ3yCYwAEGaEHcAmSQRGAKgRhB2gBgR0W3kr3kd3FgCEHmEHqMFuK0/pQUnJbBl0X0LCRrqzACAMCDtADXZbeeITKtyXmBB8HwDg7NBeDgAAHI2wAwAAHI2wAwAAHI2aHZwVRhIBACIdYQdnxRpJFB//d3ny5k6SlZVl7tfQQ/gBAEQCwg5CM8rocIk8/HauGVodm1RHEhISGEYNAIgIhB2EfGh1bEoqw6gBABGDPgYAAOBohB0AAOBodGMh7KO0zGW59aCczM2vHQAiEWEHYR2lldYgX/bnfXvKelBOVt3XzjB+AAgPPk0R1oLllPRMSaqbLm5Tndd+MiTlyt0vL5Fdu3aF9fwAwE1o2QEiCAuCAkDo0bIDAAAcjbADAAAcjbADAAAcjbADAAAcjbADAAAcjdFYQITyeDwBQ9CZewcAqoewA0QoDTo6505yWoYcKd7LKvIAUE2EHSCCJf97ckIAQPXRJg4AABzN1rAzadIkufTSS6Vu3brSsGFDuemmm2TLli0Bx5SWlsqIESOkfv36UqdOHRkwYIDs2bMn4JidO3dKv379JCUlxTzPQw89JMePH6/hVwMAACKRrWFn9erVJsh89tlnsnTpUjl27Jj06dNHDh065Dtm1KhRsmDBApk7d645XhdKvOWWW3z7T5w4YYLO0aNHZc2aNfL666/LzJkzZfz48Ta9KgAAEElsrdlZvHhxwG0NKdoyk5ubK1dffbUUFxfLtGnTZPbs2dKzZ09zzIwZM6Rt27YmIF1++eXy4YcfyjfffCPLli2TzMxM6dy5szzxxBMyZswYmTBhgiSwzhAAAK4WUTU7Gm5UevrJlaI19GhrT+/evX3HtGnTRpo3by45OTnmtl526NDBBB1Ldna2lJSUyKZNm2r8NQAAAAeEnXPPPVd+/PHHU+4vKioy+6o7p8gDDzwgV155pbRv397cV1BQYFpm6tWrF3CsBhvdZx3jH3Ss/da+YMrKykwY8t8AAIAzVasb65///KeplQkWIvwnQTsTWrvz9ddfyyeffCI1URg9ceLEsP8coDq8Ho+pTTt5w+6zAQCXhZ3333/fd33JkiWSlpbmu63hZ/ny5dKyZcszPomRI0fKwoUL5aOPPpKmTZv67s/KyjKFx9pi5N+6o6OxdJ91zBdffBHwfNZoLeuY8saOHSujR4/23daWHSZrQ6QoLdknD79dIJ7Sg5KS2VJS7D4hAHBT2NGh4SomJkYGDx4csC8+Pt4EnT/96U9Vfj6v1yv33XefzJs3T1atWiWtWrUK2N+lSxfzvBqidMi50qHpOtS8W7du5rZePvnkk1JYWGiKm5WO7EpNTZV27doF/bmJiYlmAyJVUlqGeOIprgeAGg87WlejNJSsXbtWMjIyzuqHa9eVjrR67733zFw7Vo2NthglJyebyyFDhphWGC1a1gCj4UgDjo7EUjpUXUPNoEGDZPLkyeY5xo0bZ56bQAMAAKpVs7Njx46Q/PCpU6eayx49egTcr8PL77rrLnP9+eefN4sfasuO1gTpSKuXX37Zd2ytWrVMF9jw4cNNCKpdu7ZpdXr88cdDco44dWFKU09CLQkAwOnz7GjXkm7afWS1+FimT59e5W6s00lKSpIpU6aYrSItWrSQDz74oEo/E2e/MKXWlFBLAgBwdNjRkUzacnLJJZdIo0aNTA0P3LMwJa06AADHh51XXnnFzHasdTIAAACOm1RQh4NfccUVoT8bAACASAg7v/nNb8woKgAAAEd2Y5WWlsqrr75qFt/s2LGjmQvH33PPPReq8wMAAKj5sLNhwwazurjSJR78UazsPAw5BwC4LuysXLky9GeCqBlyDgCAK+bZgbsw5BwA4Kqwc80111TaXbVixYqzOScAAAB7w45Vr2M5duyYrF+/3tTvlF8gFAAAIOrCjq5XFcyECRPk4MGDZ3tOiBAUJgMAXDvPTkXuuOOOKq+LhegpTH7wjdVSduyo3acDAID9Bco5OTlm4U44B4XJAABXhp1bbrnllNXL8/PzZd26dfLoo4+G6twAAADsCTtpaWkBt2NjY6V169ZmJfQ+ffqc/VkBqLSOSjVp0sT83wMAhCHszJgxozoPAxCiOio1/d5sadasmd2nBADOrtnJzc2VzZs3m+sXXXSRXHzxxaE6LwCV1VEBAMIbdgoLC+W2226TVatWSb169cx9RUVFZrLBOXPmSIMGDarztAAAACFXrQ7/++67Tw4cOCCbNm2Sffv2mU0nFCwpKZHf/va3oT9LAJXW8eTl5ZlNrwMAQtCys3jxYlm2bJm0bdvWd1+7du1kypQpFCgDNYw6HgAIQ9jRb4/x8fGn3K/38c0SqHnU8QBAiLuxevbsKffff//JZQT8vl2OGjVKevXqVZ2nBAAAiJyw89///d+mPqdly5Zy3nnnma1Vq1bmvpdeein0ZwkAAFCT3VhaE/Dll1+aup1//OMf5j6t3+ndu3d1zwMAAMD+lp0VK1aYQmRtwYmJiZFrr73WjMzS7dJLLzVz7Xz88cfhOVMAAIBwh50XXnhBhg4dKqmpqUGXkPjP//xPee6556pzHgAAAPaHnb///e9y3XXXVbhfh53rrMoAAABRGXb27NkTdMi5JS4uTn744YdQnBcAAEDNhx1dZVlnSq7Ihg0bpFGjRqE4LwCV8Ho8ZuoHnTXZTAHhtfuMAMAho7Guv/56efTRR01XVlJSUsC+I0eOyGOPPSY33HBDqM8RQDmlJfvk4bcLJK1BvuzP+1ZSMltKYkKC3acFANEfdsaNGyfvvvuuXHjhhTJy5Ehp3bq1uV+Hn+tSESdOnJA//OEP4TpXAH6S0jIkJT1TjhTttftUAMA5YSczM1PWrFkjw4cPl7Fjx4rXe7LtXIehZ2dnm8CjxwAAAESKM55UsEWLFvLBBx/I/v37Zdu2bSbwXHDBBXLOOeeE5wwBnFEdj1VfFxtbrQnSAcBxqjWDstJwoxMJAoisOp6EhI2sfg4AoQg7ACKzjodCZQAIRDs3AABwNFp2gCirx2FOHQA4M4QdIIrqcTylB82cOgCAqiPsAFFUj+OJpx4HAM4UNTsAAMDRbA07H330kfTv318aN25sJiacP39+wP677rrL3O+/lV91fd++fTJw4EBJTU2VevXqyZAhQ+TgwYM1/EoAAECksjXsHDp0SDp16mRmXq6Ihpv8/Hzf9tZbbwXs16CzadMmWbp0qSxcuNAEqHvuuacGzh4AAEQDW2t2+vbta7bKJCYmSlZWVtB9mzdvlsWLF8vatWvlkksuMfe99NJLZsHSZ5991rQYAQAAd4v4mp1Vq1ZJw4YNzaKjuibXjz/+6NuXk5Njuq6soKN69+5tpsn//PPPK3zOsrIyKSkpCdgAAIAzRXTY0S6sN954Q5YvXy5//OMfZfXq1aYlSFdXVwUFBSYI+YuLi5P09HSzryKTJk2StLQ038a0+gAAOFdEDz2/7bbbfNc7dOggHTt2lPPOO8+09vTq1avaz6srto8ePdp3W1t2CDxwChYEBYBAUfUpeO6550pGRoZZbV1pLU9hYWHAMcePHzcjtCqq87HqgHT0lv8GOGsCwly5++UlsmvXLrtPBwBsF1Vh51//+pep2WnUqJG53a1bNykqKpLc3FzfMStWrBCPxyNdu3a18UwB+ycgTE7LsPs0ACAi2NqNpfPhWK00aseOHbJ+/XpTc6PbxIkTZcCAAaaVZvv27fLwww/L+eefL9nZ2eb4tm3bmrqeoUOHyiuvvCLHjh2TkSNHmu4vRmIBp9IvAlZrD11cANzC1k+6devWycUXX2w2pXU0en38+PFSq1Yt2bBhg/zHf/yHXHjhhWaywC5dusjHH39suqEss2bNkjZt2pgaHh1y3r17d3n11VdtfFVA5NKgo91bdHEBcBNbW3Z69OghXm/FSzgvWbLktM+hLUCzZ88O8Zm5D9/43YPuLQBuw180GHzjBwA4VUQPPUfN4hs/AMCJaNkBAACORtgBAACORtgBAACORtgBAACORtgBAACOxmgswEXzKJkFQnVqqxi7zwgAag5hB3DRPEq6SGhKZktJTEiw+5QAoMYQdgA3zaNU8YTlAOBY1OwAAABHo2XHpcqvhQXn8Xo8J2t0RH6q1QEAFyLsuLyGQ02/N9vu00EYaH3Ow28XSFqDfNmf962p1QEANyLsuBhrYTlfUlqGpKRnypGivXafCgDYhpodAADgaIQdAADgaIQdAADgaNTsAC7mPyrPGpkXG8t3IADOQthBAIYru3NUnharHynea0bmNWvWzO7TAoCQIuwgAMOV3Sf53yO2AMCpaK9GhcOVk+qm230qAACcNcIOAABwNLqxAJxSr0WhMgAn4dMMgF+9Vq4pWPYfoQUA0Y6WHQAB9VqJCQl2nwYAhBQtOwAAwNFo2XE5/zoN5tQBADgRYcflrHl1PKUHmVPHRQi5ANyEsANTp+GJp07DzSE3xe4TAoAwIuwALkXIBeAWFCgDAABHI+wAAABHI+wAAABHo2YHQACWjQDgNHyKAQjAshEAnIaWHQCnYNkIAE5Cyw4AAHA0WnZcxOPx+LolTE0GM+cCAFyAsOMiGnS0DiM5LUP2533L8hAAAFegG8tlNOikpGdKUt10u08FAADnh52PPvpI+vfvL40bN5aYmBiZP39+wH6v1yvjx4+XRo0aSXJysvTu3Vu2bt0acMy+fftk4MCBkpqaKvXq1ZMhQ4bIwYMHa/iVAACASGVr2Dl06JB06tRJpkyZEnT/5MmT5c9//rO88sor8vnnn0vt2rUlOztbSktLfcdo0Nm0aZMsXbpUFi5caALUPffcU4OvAnB+rVdeXp5v09sAEE1srdnp27ev2YLRVp0XXnhBxo0bJzfeeKO574033pDMzEzTAnTbbbfJ5s2bZfHixbJ27Vq55JJLzDEvvfSSXH/99fLss8+aFiMAZze5oG7j5m2U5HoZcqR4r0y/N1uaNWtm9+kBQPTX7OzYsUMKCgpM15UlLS1NunbtKjk5Oea2XmrXlRV0lB6vM75qS1BFysrKpKSkJGCLdnz7RrgmF3zwjdUSm5Jqar205gsAok3EjsbSoKO0Jcef3rb26WXDhg0D9sfFxUl6errvmGAmTZokEydOFKeOtOLbN0I5uaAnnskFAUS3iG3ZCaexY8dKcXGxb9OWECeNtOLbN+xoUaQ1EUCkitiwk5WVZS737NkTcL/etvbpZWFhYcD+48ePmxFa1jHBJCYmmtFb/huA6rcoso4WgEgWsWGnVatWJrAsX77cd5/W1mgtTrdu3cxtvSwqKpLc3FzfMStWrDDfMLW2B0D4aUsirYkAIpmtNTs6H862bdsCipLXr19vam6aN28uDzzwgPzXf/2XXHDBBSb8PProo2aE1U033WSOb9u2rVx33XUydOhQMzz92LFjMnLkSDNSi5FYAADA9rCzbt06ueaaa3y3R48ebS4HDx4sM2fOlIcfftjMxaPz5mgLTvfu3c1Q86SkJN9jZs2aZQJOr169zCisAQMGmLl5AAAAbA87PXr0MPPpVERnVX788cfNVhFtBZo9e3aYzhAAAES7iK3ZAQAACAXCDgAAcLSInVQQQHTS0ZDWMPQmTZqYWjoAsBNhB8AZr5dlBZnK5t5RzOQNIBIQdgCc4XpZBZKQsNEEmYow7w6ASELYcUF3gjWNv1kvrOLBb0CV18tKTGC9LADRg7DjYFZ3gn4bj02qI57Sg5KS2VJS7D4xAABqEGHH4Ux3glckNiWV1asRltod01oYY/cZAUDFCDsAql27Y7UW0q0FIJIRdgBUu3aH1kIA0YAJMAAAgKPRsgMgpDU85pI6HgARhLADIGQ1PGkN8mV/3rfU8QCIKHRjAQhZDU9KeqYk1U0PaO3Jy8vzzfUEAHYg7AAIY2tPrpnryVorCwDsQDcWgLBhtmUAkYCwA6DGV0NXrIgOoKYQdgDU6PIlOqv3keK9rIgOoMYQdgDUmOR/FzEDQE0i7ACo2XW0AKCGEXYA1Og6Wil2nxAA1yHsOKTo0zdrLRCBWEcLgJ0IOw4p+tRvz3xrBgDgVIQdhxR90qoDAEBwTHIBAAAcjbADAAAcjW4sB2KoLwAAPyHsuGCoLwAAbkbYcSiG+gIAcBI1OwAAwNEIOwAAwNEIOwAAwNEIOwAAwNEIOwAAwNEIOwAAwNEIOwAAwNGYZweAbTwej+zatctcb9KkicTG8v0LQOjxyQLANhp07n55idms0AMAoUbLDgBbJadl2H0KABwuolt2JkyYIDExMQFbmzZtfPtLS0tlxIgRUr9+falTp44MGDBA9uzZY+s5A6j+4rXff/+92fLy8gKua3cXADi2Zeeiiy6SZcuW+W7Hxf10yqNGjZK//e1vMnfuXElLS5ORI0fKLbfcIp9++qk4vb7BqnEAnLZ4bWxSHUlr0Fj2531rrickJMj0e7OlWbNmdp8mgCgV8WFHw01WVtYp9xcXF8u0adNk9uzZ0rNnT3PfjBkzpG3btvLZZ5/J5ZdfLk6tb9Bm/yPFe80fAMBpi9fGpqRKSnqmHCnaa64nJrCgLQAHd2OprVu3SuPGjeXcc8+VgQMHys6dO839ubm5cuzYMendu7fvWO3iat68ueTk5IhTadDRPwRJddNNs79u4rX7rAD7Wju1m4uuLgBR27LTtWtXmTlzprRu3Vry8/Nl4sSJctVVV8nXX38tBQUFpnm7Xr16AY/JzMw0+ypTVlZmNktJSYlEc7N/SmZLu08HsLW1U9HVBSAqw07fvn191zt27GjCT4sWLeTtt9+W5OTkaj/vpEmTTHBySrM/4GaM5gIQ9d1Y/rQV58ILL5Rt27aZOp6jR49KUVFRwDE6GitYjY+/sWPHmpofa9MmcACRPVKLrioArgg7Bw8elO3bt0ujRo2kS5cuEh8fL8uXL/ft37Jli6np6datW6XPk5iYKKmpqQEbgEjuss2VX09ZJGvXrq009FDDAyDqurEefPBB6d+/v+m60m92jz32mNSqVUt+9atfmaHmQ4YMkdGjR0t6eroJLPfdd58JOk4ciQU4sbXm5I0qdtkeLjGhJyFhY4X1OdTwAIi6sPOvf/3LBJsff/xRGjRoIN27dzfDyvW6ev75581aOjqZoBYcZ2dny8svv2z3aQMIU4G9hp6EuDhfUPKNRoz56RhqeABEVdiZM2dOpfuTkpJkypQpZgPgjgJ7KyilNcg3Ew9qWGIuHgCOqdlxK6sOgTl1gJ+CkjXfFABEdcsOAusQ9ButfotNsfuEAACIIoSdKGHqEGjVAapW8OxXwwMAhB0Ajit4poYHgD/CDgBHYEZxABUh7ABwdLeWNbmgTlPRpEkTcwnAXQg7AByn/PD02KQ6ZuFgJhoE3ImwA8DRw9OPFO2V2JRU6ngAF6M9FwAAOBphBwAAOBrdWABcV7RMoTLgLvxvB+CiouVcMxu5zkoOwD1o2QHgqqJlCpUB9yHsAHAlnX/Hv4WHri3AuQg7AFy9wK6uO3ekeC9z8AAORtgB4FrJ/56Lh+JlwNn4Hx3hzex5eXknP4RZ8RwIG4qXAWejZScKmtn1g1hXcgZw9vxbcfy/RFC8DDgXYSeCCyf1Azk5NYNWHSAM62Z5Sg+aLxEpdp8QgLAj7EQgWnSA8NJWHE88rTiAWxB2IrhwkhYdwP5h6WdTsByq5wFwdgg7AFBB66o63ZD0yubrOZPnARA+hB0AqKh1NQTz9VT1eQCED2EHAEIwkEDn6wEQmQg7ABCCgQSM6gIiF2EHAKoyJ0+QImMGEgDRgbATIfyLHJkxGYisOXnSGuSf0fpZ/l1c5v9yTI2cLoAKEHYihH+R4/68b5lfB4igOXmqWo9jtQTpNm7eRik9cLKLKyEurtIWIgDhRdiJwEUJjxTttftUAJRTfrHQqszOnHSWLUQAQoOwAwCnWTfLP7AkJGw0YeVMZ2euqIWIiQeB8CPsAEAFLTPBAktAl1QIauuYeBAIP8KOzU4pZAQQ0etmnS4QVQcTDwLhRdixGXN1ANEn0hcSpWsMCETYiQDM1QEglOgaAwIRdgAgwkZ1VdZSo9eVttZU1mpD1xjwE8IOANSgikZ1VTZbc/l5uGKT6khCQsJpW23KByu6s+BWhB0AsKHmJzEhsOYn2Fw8GlDKLzaq83DFpqSe8viqBKvKghF1PnAywk4Nsz5QrKbogoIC6nUAFwo2p0/5uXhCMYAhWLA62zqf6nSrVRWhC+FA2Klh/h9e2hQdyuGrAKJHVYew1+QAhqrW+VS3W+1MnltRXI1QIezYwPrw0qboSB6+CsCeIeyVzeRc/piqtKyEo3bHf3mbqnarVdaKY52b9dynO946llYguCrsTJkyRZ555hnTLdSpUyd56aWX5LLLLrP1nGiOBRCuVh//Y7RlJT4+Tp68uZM0btz4lElKrWPj4//uO8aOz6Xyn4n+LURWnVJlj7EWWNVV5K1j9fFeryfkr4tg5SyOCDt//etfZfTo0fLKK69I165d5YUXXpDs7GzZsmWLNGzY0LbzojkWQDgnLrSOMa3Eh0vk4bdzTYGzdi2VD0nmWL9jDhcV+gKC/xD4YKPC1OlqDStrPfKfKb58WLFaiE5pzYoJ3mWmr8u/FckEpaK9vtdVfqHV6n7pLP/5rUL5eR6sVYsAFT6OCDvPPfecDB06VH7961+b2xp6/va3v8n06dPlkUcesfXc9D+i/39iloUAEC5WgbP+8a/KMRoQyg+B9x8VZgUipSGl9EBgrWFKJSO/rJYbZYUcfXz5sFL+8dZz+69B5j8S7XSvq3zoCvals/xAkfI0dFgj4LTVyP88rBB2tkEmWKsWX4jDJ+rDztGjRyU3N1fGjh3ru09/qXr37i05OTkSCfw/PIJ94wIAO1Q0Uqt8ILICSJJUXmvo/3zBWmT08ZXVJPm3ZpUPPylnuDq91WUXLKQEGyjif5nWoLHvnE+2hgUPYWcbZKxWLUtVWqHKBzU9pnzdlnUOlT2P20o5oj7s7N27V06cOCGZmT/9wii9/Y9//CPoY8rKysxmKS4uNpclJSUhPbcDBw7IgcI8KT2wX2ITa8ux0sNy/GipHCzME0/ZIYk9WHzKZaznuBz8cXfQfVU55mwfzzHRdUyknAfHRO+/99GEBNm6NfXkZ5b1eeV/TGJt8VTwuRXsZ/k/3/GyIxV+7o2YkiuessOS3KCpHCs7HPyc/X52+Z/l//hTziexthwt0WO+9x2T8O/z0s9l7YbTc9Pzio2pZX6G/+Up51zuPdCfXSe9oXmvnhp4tWRlZQV89lvPb56n7Ihs3brV/Nzyx+j7rceUlvxozk2P+/2sj8z+YM9rPU6POaqvNzHFnEdJ/j/N9bj4ePM4dbrnqQnWuarpv/ulNG3aNOQ/w/q77fWepsvEG+V27dqlr9C7Zs2agPsfeugh72WXXRb0MY899ph5DBsbGxsbG5tE/ZaXl1dpVoj6lp2MjAypVauW7NmzJ+B+vV1RmtUuLy1o9m9q27dvn9SvX19iYqrQIRtlNPlqE2peXp6kpp78xuVGvA+8B4r34CTeB94DJ7wH2qKjrWbWSLyKRH3Y0abJLl26yPLly+Wmm27yhRe9PXLkyKCPSUxMNJu/evXqidPpL3I0/jKHGu8D74HiPTiJ94H3INrfg7S0tNMeE/VhR2krzeDBg+WSSy4xc+vo0PNDhw75RmcBAAD3ckTYufXWW+WHH36Q8ePHm4Kozp07y+LFi08pWgYAAO7jiLCjtMuqom4rt9Muu8cee+yUrju34X3gPVC8ByfxPvAeuOk9iNEqZbtPAgAAIFyYmxoAADgaYQcAADgaYQcAADgaYcdBPvroI+nfv7+ZXEknR5w/f37Afi3P0hFrjRo1kuTkZLN+mE5P7iSTJk2SSy+9VOrWrWtWvNe5l7Zs2RJwTGlpqYwYMcJMIlmnTh0ZMGDAKZNSRrOpU6dKx44dffNmdOvWTRYtWuSa1x/M008/bf5PPPDAA656HyZMmGBet//Wpk0bV70HStdnuuOOO8zr1M++Dh06yLp161z12diyZctTfhd0039/N/wuEHYcROcW6tSpk0yZMiXo/smTJ8uf//xnsyr8559/LrVr15bs7GzzS+4Uq1evNv9hP/vsM1m6dKkcO3ZM+vTpY94by6hRo2TBggUyd+5cc7wu6nfLLbeIU+j6M/rHXRfI1Q/0nj17yo033iibNm1yxesvb+3atfI///M/JgD6c8v7cNFFF0l+fr5v++STT1z1Huzfv1+uvPJKiY+PN6H/m2++kT/96U9yzjnnuOqzce3atQG/B/r5qH7xi1+443chlOtUIXLoP+28efN8tz0ejzcrK8v7zDPP+O4rKiryJiYmet966y2vUxUWFpr3YvXq1b7XHB8f7507d67vmM2bN5tjcnJyvE51zjnneP/yl7+47vUfOHDAe8EFF3iXLl3q/fnPf+69//77zf1ueR90HcBOnToF3eeW92DMmDHe7t27V7jfrZ+N999/v/e8884zr98Nvwu07LjEjh07zISL2jzrP8V2165dJScnR5zKWtE+PT3dXGprh7b2+L8P2qzfvHlzR74PJ06ckDlz5piWLe3Octvr11a+fv36Bbxe5ab3QbtjtGv73HPPlYEDB8rOnTtd9R68//77ZnZ9bcHQru2LL75YXnvtNVd/Nh49elTefPNNufvuu01Xlht+Fwg7LqH/mVX5WaX1trXPaXSNNK3R0Cbs9u3bm/v0tep6auXXQnPa+7Bx40bT764ThQ0bNkzmzZsn7dq1c83rVxryvvzyS1PHVZ5b3gf9gz1z5kwzo7zWcukf9quuusosnOiW9+C7774zr/2CCy6QJUuWyPDhw+W3v/2tvP766679bJw/f74UFRXJXXfdZW674XfBMTMoA8G+1X/99dcBNQpu0bp1a1m/fr1p2XrnnXfM2nHaD+8WuoLz/fffb+oSkpKSxK369u3ru641Sxp+WrRoIW+//bYpxHUD/dKjLTtPPfWUua0tO/q5oPU5+v/CjaZNm2Z+N063UriT0LLjEllZWeayfHW93rb2OYkuHbJw4UJZuXKlKdi16GvVJlz9VuPk90G/pZ1//vnSpUsX07Khhesvvviia16/NssXFhbKz372M4mLizObhj0tQtXr+o3VDe9DefrN/cILL5Rt27a55ndBR1hpq6a/tm3b+rrz3PbZ+P3338uyZcvkN7/5je8+N/wuEHZcolWrVuaXdvny5b77SkpKzMgDreVwCq3N1qCj3TYrVqwwr9uf/vHXURn+74MOTdcPPie9D8G+3ZaVlbnm9ffq1ct05WnrlrXpt3utWbGuu+F9KO/gwYOyfft2EwDc8rug3djlp5/49ttvTQuXmz4bLTNmzDC1S1rLZnHF74LdFdII7ciTr776ymz6T/vcc8+Z699//73Z//TTT3vr1avnfe+997wbNmzw3njjjd5WrVp5jxw54nWK4cOHe9PS0ryrVq3y5ufn+7bDhw/7jhk2bJi3efPm3hUrVnjXrVvn7datm9mc4pFHHjGjz3bs2GH+nfV2TEyM98MPP3TF66+I/2gst7wPv/vd78z/Bf1d+PTTT729e/f2ZmRkmFGKbnkPvvjiC29cXJz3ySef9G7dutU7a9Ysb0pKivfNN9/0HeOGz0Z14sQJ8++tI9TKc/rvAmHHQVauXGlCTvlt8ODBZr8OMXz00Ue9mZmZZlhlr169vFu2bPE6SbDXr9uMGTN8x+gH2L333muGY+uH3s0332wCkVPcfffd3hYtWngTEhK8DRo0MP/OVtBxw+uvathxw/tw6623ehs1amR+F5o0aWJub9u2zVXvgVqwYIG3ffv25nOvTZs23ldffTVgvxs+G9WSJUvM52Gw1+b03wVWPQcAAI5GzQ4AAHA0wg4AAHA0wg4AAHA0wg4AAHA0wg4AAHA0wg4AAHA0wg4AAHA0wg4AAHA0wg4A1JCYmBiZP3++3acBuA5hB4BjrFq1ygQK3WJjYyUtLU0uvvhiefjhhyU/Pz/oY9566y2pVauWjBgxotLn001XSx8wYIB89913vmNatmwpL7zwQlhfF4CzQ9gB4Di6YvPu3btl7dq1MmbMGFm2bJm0b9/erIRe3rRp00wY0tBTWlpa6fPNnTtXNm3aJP3795cTJ07UwCsBEAqEHQBB9ejRQ+677z554IEH5JxzzjGtGq+99pocOnRIfv3rX0vdunXl/PPPl0WLFvke8/XXX0vfvn2lTp065vhBgwbJ3r17ffsXL14s3bt3l3r16kn9+vXlhhtukO3bt/v2//Of/zQtKO+++65cc801kpKSIp06dZKcnJwzOveGDRtKVlaWXHjhhXLbbbfJp59+Kg0aNJDhw4cHHLdjxw5Zs2aNPPLII+ZY/bkVPV+jRo3k6quvlvHjx8s333wj27Ztk+rQFiZ9j5KTk+Xcc8+Vd955J2C/nk/nzp0lKSlJLrnkEtPtpe/J+vXrq/XzABB2AFTi9ddfl4yMDPniiy9M8NGw8Itf/EKuuOIK+fLLL6VPnz4m0Bw+fFiKioqkZ8+eptto3bp1Jtjs2bNHfvnLX/qeT4PS6NGjzf7ly5ebrqabb75ZPB5PwM/9wx/+IA8++KD5A68h5Fe/+pUcP3682q9Dg8WwYcNM6CksLPTdP2PGDOnXr5/p7rrjjjtMK09VnksdPXq0Wufy6KOPmq6wv//97zJw4EATxjZv3mz2lZSUmFajDh06mPf3iSeeMC1TAM6S3cuuA4hMP//5z73du3f33T5+/Li3du3a3kGDBvnuy8/P9+rHSE5OjveJJ57w9unTJ+A58vLyzP4tW7YE/Rk//PCD2b9x40Zze8eOHeb2X/7yF98xmzZtMvdt3rz5tOe8cuVKc+z+/ftP2bdo0SKz7/PPPze3T5w44W3WrJl3/vz5vnNJSEjwfvfddxU+3+7du71XXHGFt0mTJt6ysjJzX4sWLbzPP/+8tyr0uYYNGxZwX9euXb3Dhw8316dOneqtX7++98iRI779r732mnncV199VaWfAeBUtOwAqFDHjh1917WIV7uetNXBol1VSltLtKVi5cqVpgvL2tq0aWP2W11VW7duNa002n2TmppqinvVzp07K/y52n1k/YyzcTJrnBwRpZYuXWpamq6//npzW1uwrr32Wpk+ffopj23atKnUrl1bGjdubB7zf//3f5KQkFCt8+jWrdspt62WHa0N0teuXViWyy67rFo/B8BP4vyuA0CA+Pj4gNsaFPzvs4KDdkMdPHjQdMH88Y9/POV5rMCi+1u0aGFqfzQ46OO0cLh8l1BFP+NsWIHCCljaZbVv3z5ft5T1MzZs2CATJ040XWyWjz/+2IQzrd3RWiUA0YWwAyAkfvazn5kWDw0TcXGnfrT8+OOPpuVCg85VV11l7vvkk09q5NyOHDkir776qikw1kJlPZf33ntP5syZIxdddJHvOB1hpQXUH374oVx33XW++1u1amWKqkPhs88+kzvvvDPgttY5qdatW8ubb74pZWVlkpiYaO7TEWUAzg7dWABCQuep0ZYS7abSP9DadbVkyRIzcktDhI7o0m4wDR06kmnFihWmWDkctMuroKDAdJtpoLnyyivNqLCpU6ea/f/7v/9rzkWLp7Vlydp05Jd2a1WlULm6dPi6dpV9++238thjj5ni75EjR5p9t99+u2lduueee0xLlL5/zz77bEALF4AzR9gBEBLaLaWjnTTY6Cgtre3RYevaIqJdQrpp8MjNzTXBYtSoUfLMM8+E5Vy0hUTPp0uXLvL0009L7969zbD4du3amf0aNnQUWLAAoSOl3n///YAh86GkXWT6PmhtzhtvvGHm97HOS7vKFixYYEah6fBzHZWmQ92Vfx0PgDMTo1XKZ/gYAEANmTVrlmkdKy4uDqgvAlB11OwAQATR1h4drdakSRMzwk3n2dHuNoIOUH10YwGIGtbszMG2p556yvYWmIrOzb8I+nS01kgnOGzbtq3p6tNJHLXOCUD10Y0FIGrs2rXLjKwKJj093Wx2OXDggJkxOhgdSq9D7gHYg7ADAAAcjW4sAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAADgaIQdAAAgTvb/tTz4vB5CfvsAAAAASUVORK5CYII=", "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: [mean_750_bg, mean_647_bg, mean_555_bg, mean_488_bg, mean_DAPI_bg, mean_Vimentin, mean_CD3e, mean_panCK, mean_CD8, mean_DAPI_1, mean_COL1A1, mean_CD20, mean_CD68, mean_Ki67, mean_DAPI_2]" + "
" ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + } + ], + "source": [ + "# let's check the nuclear stain distribution\n", + "df = pd.DataFrame(data=adata.X, columns=adata.var_names)\n", + "sns.histplot(data=df, x=\"mean_DAPI_bg\", bins=200)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "844759f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m16:13:12.15\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_abs_value for feature 'mean_DAPI_bg'...\n", + "\u001b[32m16:13:12.16\u001b[0m | \u001b[1mINFO\u001b[0m | Feature 'mean_DAPI_bg' identified from adata.X.\n", + "\u001b[32m16:13:12.16\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'mean_DAPI_bg' >= 5.0000 (from absolute bound: 5).\n", + "\u001b[32m16:13:12.16\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'mean_DAPI_bg' <= 60.0000 (from absolute bound: 60).\n", + "\u001b[32m16:13:12.16\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | 16777 of 16808 cells (99.82%) passed the filter.\n", + "\u001b[32m16:13:12.16\u001b[0m | \u001b[1mINFO\u001b[0m | New boolean column 'mean_DAPI_bg_filter' added to adata.obs.\n" + ] } ], "source": [ - "adata.var" + "#based on that we can filter out dataset by absolute values\n", + "adata = dvp.tl.filter_by_abs_value(\n", + " adata=adata,feature_name=\"mean_DAPI_bg\", lower_bound=5, upper_bound=60, mode=\"absolute\")" ] }, { "cell_type": "markdown", - "id": "3f0aa7e5", + "id": "c6dab6a3", "metadata": {}, "source": [ - "### Filter by initial nuclear stain signal" + "### Filter by ratio of nuclear stain between last and first DAPI images" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "20844eba", + "execution_count": 19, + "id": "e852c5ba", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1297,37 +957,76 @@ } ], "source": [ + "#let's check the ratio between first and last cycle\n", "df = pd.DataFrame(data=adata.X, columns=adata.var_names)\n", - "sns.histplot(data=df, x=\"mean_DAPI_bg\", bins=200)" + "df['ratio'] = df['mean_DAPI_2'] / df['mean_DAPI_bg']\n", + "\n", + "fig,ax = plt.subplots()\n", + "sns.histplot(data=df, x=\"ratio\", bins=200, ax=ax)\n", + "ax.set_xlim(0,1.5)\n", + "ax.set_yscale('log')" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "844759f5", + "execution_count": 20, + "id": "c2224ca4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_abs_value for feature 'mean_DAPI_bg'...\n", - "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Feature 'mean_DAPI_bg' identified from adata.X.\n", - "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'mean_DAPI_bg' >= 8.0822 (from quantile bound: 0.01).\n", - "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'mean_DAPI_bg' <= 47.2041 (from quantile bound: 0.99).\n", - "\u001b[32m15:14:07.13\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | 16470 of 16808 cells (97.99%) passed the filter.\n", - "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | New boolean column 'mean_DAPI_bg_filter' added to adata.obs.\n" + "\u001b[32m16:13:12.40\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_ratio...\n", + "\u001b[32m16:13:12.40\u001b[0m | \u001b[1mINFO\u001b[0m | Number of cells with DAPI ratio < 0.25: 1035\n", + "\u001b[32m16:13:12.40\u001b[0m | \u001b[1mINFO\u001b[0m | Number of cells with DAPI ratio > 1.05: 28\n", + "\u001b[32m16:13:12.40\u001b[0m | \u001b[1mINFO\u001b[0m | Cells with DAPI ratio between 0.25 and 1.05: 15745\n", + "\u001b[32m16:13:12.40\u001b[0m | \u001b[1mINFO\u001b[0m | Cells filtered: 6.32%\n", + "\u001b[32m16:13:12.40\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | filter_by_ratio complete.\n", + "\u001b[32m16:13:12.40\u001b[0m | \u001b[1mINFO\u001b[0m | New boolean column 'DAPI_ratio_pass' added to adata.obs.\n" ] } ], "source": [ - "adata = dvp.tl.filter_by_abs_value(adata=adata,feature_name=\"mean_DAPI_bg\", lower_bound=0.01, upper_bound=0.99, mode=\"quantile\")" + "# based on that histogram we see\n", + "# many cells lost almost all their nuclear stain signal\n", + "adata = dvp.tl.filter_by_ratio(\n", + " adata=adata, end_cycle=\"mean_DAPI_2\", start_cycle=\"mean_DAPI_bg\", \n", + " label=\"DAPI\", min_ratio=0.25, max_ratio=1.05)" + ] + }, + { + "cell_type": "markdown", + "id": "554670a5", + "metadata": {}, + "source": [ + "### Filter by manual annotations" + ] + }, + { + "cell_type": "markdown", + "id": "9dea8fc4", + "metadata": {}, + "source": [ + "Annotations should be made in QuPath, and classified by functionality. \n", + "This means that you should create a QuPath Annotation Class for each different kind of ROI. " ] }, { "cell_type": "code", - "execution_count": 13, - "id": "96a3c953", + "execution_count": 21, + "id": "6ad690cd", + "metadata": {}, + "outputs": [], + "source": [ + "#check annotations\n", + "gdf = gpd.read_file(\"data/manual_artefact_annotations/artefacts.geojson\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "78be7461", "metadata": {}, "outputs": [ { @@ -1351,423 +1050,47 @@ " \n", " \n", " \n", - " CellID\n", - " Y_centroid\n", - " X_centroid\n", - " Area\n", - " MajorAxisLength\n", - " MinorAxisLength\n", - " Eccentricity\n", - " Orientation\n", - " Extent\n", - " Solidity\n", - " Area_filter\n", - " mean_DAPI_bg_filter\n", + " id\n", + " objectType\n", + " classification\n", + " geometry\n", " \n", " \n", " \n", " \n", " 0\n", - " 1\n", - " 17.612598\n", - " 53.337008\n", - " 1270.0\n", - " 48.198269\n", - " 36.841132\n", - " 0.644782\n", - " 0.359469\n", - " 146.669048\n", - " 0.949178\n", - " True\n", - " True\n", + " 9dbac0eb-6171-4da8-9c3f-846ecdb81dfb\n", + " annotation\n", + " { \"name\": \"folded_tissue\", \"color\": [ 176, 102...\n", + " POLYGON ((722 2645, 702 2647, 689.93 2650.81, ...\n", " \n", " \n", " 1\n", - " 2\n", - " 6.598958\n", - " 126.006944\n", - " 576.0\n", - " 45.835698\n", - " 18.372329\n", - " 0.916152\n", - " 1.513685\n", - " 113.112698\n", - " 0.886154\n", - " True\n", - " True\n", + " cc4df5d0-fe6b-4285-849a-698851827e9c\n", + " annotation\n", + " { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19...\n", + " POLYGON ((4685 2530, 4682 2531, 4677 2531, 467...\n", " \n", " \n", " 2\n", - " 3\n", - " 17.416667\n", - " 156.656504\n", - " 984.0\n", - " 40.751104\n", - " 31.700565\n", - " 0.628380\n", - " -1.528462\n", - " 121.396970\n", - " 0.955340\n", - " True\n", - " True\n", + " e6aaf657-f4e7-401f-834b-a2fd5a072300\n", + " annotation\n", + " { \"name\": \"folded_tissue\", \"color\": [ 176, 102...\n", + " POLYGON ((3127 3675, 3119 3676, 3116 3677, 311...\n", " \n", " \n", " 3\n", - " 4\n", - " 4.982558\n", - " 179.337209\n", - " 344.0\n", - " 34.620290\n", - " 13.577757\n", - " 0.919884\n", - " 1.474818\n", - " 82.627417\n", - " 0.971751\n", - " True\n", - " True\n", + " be635097-4631-46e7-b1a8-878363184124\n", + " annotation\n", + " { \"name\": \"CD8_noise\", \"color\": [ 51, 236, 220...\n", + " POLYGON ((117 3008, 110 3009.62, 105 3010, 96....\n", " \n", " \n", " 4\n", - " 5\n", - " 19.159558\n", - " 228.598210\n", - " 1899.0\n", - " 54.446578\n", - " 49.053930\n", - " 0.433912\n", - " 1.287374\n", - " 196.610173\n", - " 0.896601\n", - " True\n", - " True\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 16803\n", - " 16804\n", - " 4993.579685\n", - " 106.373030\n", - " 571.0\n", - " 52.291050\n", - " 14.719719\n", - " 0.959562\n", - " -1.563557\n", - " 116.870058\n", - " 0.976068\n", - " True\n", - " True\n", - " \n", - " \n", - " 16804\n", - " 16805\n", - " 4993.413242\n", - " 810.385845\n", - " 438.0\n", - " 40.105247\n", - " 14.334812\n", - " 0.933940\n", - " 1.531591\n", - " 90.970563\n", - " 0.962637\n", - " True\n", - " True\n", - " \n", - " \n", - " 16805\n", - " 16806\n", - " 4994.153535\n", - " 645.010101\n", - " 495.0\n", - " 50.864135\n", - " 13.112118\n", - " 0.966202\n", - " 1.538209\n", - " 111.248737\n", - " 0.951923\n", - " True\n", - " True\n", - " \n", - " \n", - " 16806\n", - " 16807\n", - " 4993.835570\n", - " 1244.718121\n", - " 298.0\n", - " 28.947821\n", - " 13.495126\n", - " 0.884686\n", - " -1.561806\n", - " 69.213203\n", - " 0.973856\n", - " True\n", - " True\n", - " \n", - " \n", - " 16807\n", - " 16808\n", - " 4994.250774\n", - " 4186.876161\n", - " 323.0\n", - " 33.325316\n", - " 13.027177\n", - " 0.920429\n", - " -1.564638\n", - " 77.213203\n", - " 0.984756\n", - " True\n", - " True\n", - " \n", - " \n", - "\n", - "

16808 rows × 12 columns

\n", - "" - ], - "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 \n", - "1 2 6.598958 126.006944 576.0 45.835698 \n", - "2 3 17.416667 156.656504 984.0 40.751104 \n", - "3 4 4.982558 179.337209 344.0 34.620290 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 \n", - "... ... ... ... ... ... \n", - "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", - "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", - "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", - "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", - "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", - "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", - "... ... ... ... ... ... \n", - "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", - "\n", - " Area_filter mean_DAPI_bg_filter \n", - "0 True True \n", - "1 True True \n", - "2 True True \n", - "3 True True \n", - "4 True True \n", - "... ... ... \n", - "16803 True True \n", - "16804 True True \n", - "16805 True True \n", - "16806 True True \n", - "16807 True True \n", - "\n", - "[16808 rows x 12 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata.obs" - ] - }, - { - "cell_type": "markdown", - "id": "c6dab6a3", - "metadata": {}, - "source": [ - "### Filter by ratio of nuclear stain between last and first DAPI images" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e852c5ba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pd.DataFrame(data=adata.X, columns=adata.var_names)\n", - "df['ratio'] = df['mean_DAPI_2'] / df['mean_DAPI_bg']\n", - "\n", - "fig,ax = plt.subplots()\n", - "sns.histplot(data=df, x=\"ratio\", bins=200, ax=ax)\n", - "ax.set_xlim(0,1.5)\n", - "ax.set_yscale('log')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c2224ca4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_ratio...\n", - "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Number of cells with DAPI ratio < 0.25: 1035\n", - "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Number of cells with DAPI ratio > 1.05: 28\n", - "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Cells with DAPI ratio between 0.25 and 1.05: 15745\n", - "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Cells filtered: 6.32%\n", - "\u001b[32m15:14:07.44\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | filter_by_ratio complete.\n" - ] - } - ], - "source": [ - "adata = dvp.tl.filter_by_ratio(adata=adata, end_cycle=\"mean_DAPI_2\", start_cycle=\"mean_DAPI_bg\", label=\"DAPI\", min_ratio=0.25, max_ratio=1.05)" - ] - }, - { - "cell_type": "markdown", - "id": "554670a5", - "metadata": {}, - "source": [ - "### Filter by manual annotations" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "6ad690cd", - "metadata": {}, - "outputs": [], - "source": [ - "#check annotations\n", - "gdf = gpd.read_file(\"data/manual_artefact_annotations/artefacts.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "78be7461", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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77908eec6-399d-4e17-90ba-26446b5dcacdannotation{ \"name\": \"Antibody_clumps\", \"color\": [ 32, 19...POLYGON ((4693 2599, 4690 2600, 4685 2600, 468...
8f1021867-a0ec-4323-8cf9-d9d70063566aannotation{ \"name\": \"CD8_noise\", \"color\": [ 51, 236, 220...POLYGON ((3 1994, 0 1994.23, 0 2047.23, 0 2052...
946b88b7d-3f7f-4a7d-812d-5ae37a6090cbannotation{ \"name\": \"folded_tissue\", \"color\": [ 176, 102...POLYGON ((1745 18, 1743.71 18.43, 1738 19, 172...baff029c-3349-4fa2-946a-0f5e55c46dc8annotation{ \"name\": \"Antibody_clumps\", \"color\": [ 32, 19...POLYGON ((3987 4058, 3984 4059, 3979 4059, 397...
\n", @@ -1780,11 +1103,6 @@ "2 e6aaf657-f4e7-401f-834b-a2fd5a072300 annotation \n", "3 be635097-4631-46e7-b1a8-878363184124 annotation \n", "4 baff029c-3349-4fa2-946a-0f5e55c46dc8 annotation \n", - "5 ad114d98-3048-4e3c-9a94-982e72a95515 annotation \n", - "6 99a65f87-acf2-434d-9ab5-7692e39af63b annotation \n", - "7 7908eec6-399d-4e17-90ba-26446b5dcacd annotation \n", - "8 f1021867-a0ec-4323-8cf9-d9d70063566a annotation \n", - "9 46b88b7d-3f7f-4a7d-812d-5ae37a6090cb annotation \n", "\n", " classification \\\n", "0 { \"name\": \"folded_tissue\", \"color\": [ 176, 102... \n", @@ -1792,37 +1110,28 @@ "2 { \"name\": \"folded_tissue\", \"color\": [ 176, 102... \n", "3 { \"name\": \"CD8_noise\", \"color\": [ 51, 236, 220... \n", "4 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", - "5 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", - "6 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", - "7 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", - "8 { \"name\": \"CD8_noise\", \"color\": [ 51, 236, 220... \n", - "9 { \"name\": \"folded_tissue\", \"color\": [ 176, 102... \n", "\n", " geometry \n", "0 POLYGON ((722 2645, 702 2647, 689.93 2650.81, ... \n", "1 POLYGON ((4685 2530, 4682 2531, 4677 2531, 467... \n", "2 POLYGON ((3127 3675, 3119 3676, 3116 3677, 311... \n", "3 POLYGON ((117 3008, 110 3009.62, 105 3010, 96.... \n", - "4 POLYGON ((3987 4058, 3984 4059, 3979 4059, 397... \n", - "5 POLYGON ((4791 1546, 4788.47 1546.95, 4788 154... \n", - "6 POLYGON ((4636 1840, 4628 1843, 4620 1847, 461... \n", - "7 POLYGON ((4693 2599, 4690 2600, 4685 2600, 468... \n", - "8 POLYGON ((3 1994, 0 1994.23, 0 2047.23, 0 2052... \n", - "9 POLYGON ((1745 18, 1743.71 18.43, 1738 19, 172... " + "4 POLYGON ((3987 4058, 3984 4059, 3979 4059, 397... " ] }, - "execution_count": 17, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gdf" + "# here we see how the shapes look like\n", + "gdf.head()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "id": "d5fae116", "metadata": {}, "outputs": [ @@ -1838,1142 +1147,39 @@ } ], "source": [ + "# lets plot those shapes and color them by class\n", "fig,ax = plt.subplots()\n", - "gdf.plot(column=\"classification\", legend=True, figsize=(8, 6), ax=ax)\n", - "ax.invert_yaxis()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5502375b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filterDAPI_ratioDAPI_ratio_pass
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue0.065991False
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue0.107462False
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue0.098039False
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue0.136228False
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue0.104794False
.............................................
16803168044993.579685106.373030571.052.29105014.7197190.959562-1.563557116.8700580.976068TrueTrue0.209647False
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637TrueTrue0.477926True
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923TrueTrue0.268536True
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856TrueTrue0.523596True
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756TrueTrue0.401436True
\n", - "

16808 rows × 14 columns

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" - ], - "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 \n", - "1 2 6.598958 126.006944 576.0 45.835698 \n", - "2 3 17.416667 156.656504 984.0 40.751104 \n", - "3 4 4.982558 179.337209 344.0 34.620290 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 \n", - "... ... ... ... ... ... \n", - "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", - "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", - "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", - "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", - "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", - "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", - "... ... ... ... ... ... \n", - "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", - "\n", - " Area_filter mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass \n", - "0 True True 0.065991 False \n", - "1 True True 0.107462 False \n", - "2 True True 0.098039 False \n", - "3 True True 0.136228 False \n", - "4 True True 0.104794 False \n", - "... ... ... ... ... \n", - "16803 True True 0.209647 False \n", - "16804 True True 0.477926 True \n", - "16805 True True 0.268536 True \n", - "16806 True True 0.523596 True \n", - "16807 True True 0.401436 True \n", - "\n", - "[16808 rows x 14 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata.obs" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "07e5d802", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32m15:14:08.83\u001b[0m | \u001b[1mINFO\u001b[0m | Each class of annotation will be a different column in adata.obs\n", - "\u001b[32m15:14:08.83\u001b[0m | \u001b[1mINFO\u001b[0m | TRUE means cell was inside annotation, FALSE means cell not in annotation\n", - "\u001b[32m15:14:08.84\u001b[0m | \u001b[1mINFO\u001b[0m | GeoJSON loaded, detected: 10 annotations\n" - ] - } - ], - "source": [ - "adata = dvp.tl.filter_by_annotation(adata=adata, path_to_geojson=\"data/manual_artefact_annotations/artefacts.geojson\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0a0169b5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filterDAPI_ratioDAPI_ratio_passAntibody_clumpsCD8_noisefolded_tissueANYannotation
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue0.065991FalseFalseFalseFalseFalseUnannotated
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue0.107462FalseFalseFalseFalseFalseUnannotated
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue0.098039FalseFalseFalseFalseFalseUnannotated
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue0.136228FalseFalseFalseFalseFalseUnannotated
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue0.104794FalseFalseFalseFalseFalseUnannotated
............................................................
16803168044993.579685106.373030571.052.29105014.7197190.959562-1.563557116.8700580.976068TrueTrue0.209647FalseFalseTrueFalseTrueCD8_noise
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637TrueTrue0.477926TrueFalseTrueFalseTrueCD8_noise
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923TrueTrue0.268536TrueFalseTrueFalseTrueCD8_noise
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856TrueTrue0.523596TrueFalseTrueFalseTrueCD8_noise
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756TrueTrue0.401436TrueFalseFalseFalseFalseUnannotated
\n", - "

16808 rows × 19 columns

\n", - "
" - ], - "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 \n", - "1 2 6.598958 126.006944 576.0 45.835698 \n", - "2 3 17.416667 156.656504 984.0 40.751104 \n", - "3 4 4.982558 179.337209 344.0 34.620290 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 \n", - "... ... ... ... ... ... \n", - "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", - "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", - "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", - "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", - "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", - "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", - "... ... ... ... ... ... \n", - "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", - "\n", - " Area_filter mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass \\\n", - "0 True True 0.065991 False \n", - "1 True True 0.107462 False \n", - "2 True True 0.098039 False \n", - "3 True True 0.136228 False \n", - "4 True True 0.104794 False \n", - "... ... ... ... ... \n", - "16803 True True 0.209647 False \n", - "16804 True True 0.477926 True \n", - "16805 True True 0.268536 True \n", - "16806 True True 0.523596 True \n", - "16807 True True 0.401436 True \n", - "\n", - " Antibody_clumps CD8_noise folded_tissue ANY annotation \n", - "0 False False False False Unannotated \n", - "1 False False False False Unannotated \n", - "2 False False False False Unannotated \n", - "3 False False False False Unannotated \n", - "4 False False False False Unannotated \n", - "... ... ... ... ... ... \n", - "16803 False True False True CD8_noise \n", - "16804 False True False True CD8_noise \n", - "16805 False True False True CD8_noise \n", - "16806 False True False True CD8_noise \n", - "16807 False False False False Unannotated \n", - "\n", - "[16808 rows x 19 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata.obs" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "292055ac", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n", - " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n" - ] - } - ], - "source": [ - "# new processed adata\n", - "adata_processed = adata[\n", - " (adata.obs[\"Area_filter\"])\n", - " & (adata.obs[\"DAPI_ratio_pass\"])\n", - " & (~adata.obs[\"Antibody_clumps\"])\n", - " & (~adata.obs[\"folded_tissue\"])\n", - "].copy() # type: ignore" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c163d788", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 16808 × 15\n", - " obs: 'CellID', 'Y_centroid', 'X_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Orientation', 'Extent', 'Solidity', 'Area_filter', 'mean_DAPI_bg_filter', 'DAPI_ratio', 'DAPI_ratio_pass', 'Antibody_clumps', 'CD8_noise', 'folded_tissue', 'ANY', 'annotation'" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "2232f39e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 15244 × 15\n", - " obs: 'CellID', 'Y_centroid', 'X_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Orientation', 'Extent', 'Solidity', 'Area_filter', 'mean_DAPI_bg_filter', 'DAPI_ratio', 'DAPI_ratio_pass', 'Antibody_clumps', 'CD8_noise', 'folded_tissue', 'ANY', 'annotation'" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata_processed" - ] - }, - { - "cell_type": "markdown", - "id": "24a5aa14", - "metadata": {}, - "source": [ - "### QuPath QC" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "482a0b18", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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labelobjectTypegeometry
01detectionPOLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...
12detectionPOLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...
23detectionPOLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...
34detectionPOLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1...
45detectionPOLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ...
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" - ], - "text/plain": [ - " label objectType geometry\n", - "0 1 detection POLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...\n", - "1 2 detection POLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...\n", - "2 3 detection POLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...\n", - "3 4 detection POLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1...\n", - "4 5 detection POLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ..." - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf = gpd.read_file(\"outputs/segmentation_for_qupath.geojson\")\n", - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "893392ee", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filterDAPI_ratioDAPI_ratio_passAntibody_clumpsCD8_noisefolded_tissueANYannotation
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue0.065991FalseFalseFalseFalseFalseUnannotated
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue0.107462FalseFalseFalseFalseFalseUnannotated
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue0.098039FalseFalseFalseFalseFalseUnannotated
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue0.136228FalseFalseFalseFalseFalseUnannotated
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue0.104794FalseFalseFalseFalseFalseUnannotated
\n", - "
" - ], - "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength MinorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 36.841132 \n", - "1 2 6.598958 126.006944 576.0 45.835698 18.372329 \n", - "2 3 17.416667 156.656504 984.0 40.751104 31.700565 \n", - "3 4 4.982558 179.337209 344.0 34.620290 13.577757 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 49.053930 \n", - "\n", - " Eccentricity Orientation Extent Solidity Area_filter \\\n", - "0 0.644782 0.359469 146.669048 0.949178 True \n", - "1 0.916152 1.513685 113.112698 0.886154 True \n", - "2 0.628380 -1.528462 121.396970 0.955340 True \n", - "3 0.919884 1.474818 82.627417 0.971751 True \n", - "4 0.433912 1.287374 196.610173 0.896601 True \n", - "\n", - " mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass Antibody_clumps \\\n", - "0 True 0.065991 False False \n", - "1 True 0.107462 False False \n", - "2 True 0.098039 False False \n", - "3 True 0.136228 False False \n", - "4 True 0.104794 False False \n", - "\n", - " CD8_noise folded_tissue ANY annotation \n", - "0 False False False Unannotated \n", - "1 False False False Unannotated \n", - "2 False False False Unannotated \n", - "3 False False False Unannotated \n", - "4 False False False Unannotated " - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata.obs.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "37bf07f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32m15:14:09.23\u001b[0m | \u001b[1mINFO\u001b[0m | Found 15244 matching IDs between adata.obs['CellID'] and geodataframe['label'].\n" - ] - } - ], - "source": [ - "# check processed cells in qupath\n", - "cells = dvp.io.adata_to_qupath(\n", - " adata=adata_processed, \n", - " geodataframe=gdf,\n", - " adataobs_on=\"CellID\",\n", - " gdf_on=\"label\",\n", - " classify_by=None,\n", - " simplify_value=None,\n", - " save_as_detection=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "44441f39", - "metadata": {}, - "outputs": [], - "source": [ - "cells.to_file(\"outputs/filtered_cells.geojson\")" - ] - }, - { - "cell_type": "markdown", - "id": "82494daf", - "metadata": {}, - "source": [ - "### Napari QC not quite possible without spatialdata object, check tutorial #3" - ] - }, - { - "cell_type": "markdown", - "id": "3b4ba360", - "metadata": {}, - "source": [ - "## Phenotype my cells" + "gdf.plot(column=\"classification\", legend=True, figsize=(8, 6), ax=ax)\n", + "ax.invert_yaxis()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "388c2b6a", + "execution_count": 24, + "id": "07e5d802", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Help on function impute_marker_with_annotation in module opendvp.pp.impute_marker_with_annotation:\n", - "\n", - "impute_marker_with_annotation(adata: anndata._core.anndata.AnnData, target_variable: str, target_annotation_column: str, quantile_for_imputation: float = 0.05) -> anndata._core.anndata.AnnData\n", - " Change value of a feature in an AnnData object for rows matching a specific annotation.\n", - " \n", - " Using a specified quantile value from the variable's distribution.\n", - " \n", - " Parameters:\n", - " ----------\n", - " adata : ad.AnnData\n", - " The annotated data matrix.\n", - " target_variable : str\n", - " The variable (gene/feature) to impute.\n", - " target_annotation_column : str\n", - " The column in adata.obs to use for selecting rows to impute.\n", - " quantile_for_imputation : float, optional\n", - " The quantile to use for imputation (default is 0.05).\n", - " \n", - " Returns:\n", - " -------\n", - " ad.AnnData\n", - " A copy of the AnnData object with imputed values.\n", - "\n" + "\u001b[32m16:13:12.50\u001b[0m | \u001b[1mINFO\u001b[0m | Each class of annotation will be a different column in adata.obs\n", + "\u001b[32m16:13:12.50\u001b[0m | \u001b[1mINFO\u001b[0m | TRUE means cell was inside annotation, FALSE means cell not in annotation\n", + "\u001b[32m16:13:12.51\u001b[0m | \u001b[1mINFO\u001b[0m | GeoJSON loaded, detected: 10 annotations\n" ] } ], "source": [ - "help(dvp.pp.impute_marker_with_annotation)" + "adata = dvp.tl.filter_by_annotation(\n", + " adata=adata, \n", + " path_to_geojson=\"data/manual_artefact_annotations/artefacts.geojson\")" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "a187eae5", + "execution_count": 25, + "id": "0a0169b5", "metadata": {}, "outputs": [ { @@ -2997,20 +1203,6 @@ " \n", " \n", " \n", - " CellID\n", - " Y_centroid\n", - " X_centroid\n", - " Area\n", - " MajorAxisLength\n", - " MinorAxisLength\n", - " Eccentricity\n", - " Orientation\n", - " Extent\n", - " Solidity\n", - " Area_filter\n", - " mean_DAPI_bg_filter\n", - " DAPI_ratio\n", - " DAPI_ratio_pass\n", " Antibody_clumps\n", " CD8_noise\n", " folded_tissue\n", @@ -3021,20 +1213,6 @@ " \n", " \n", " 0\n", - " 1\n", - " 17.612598\n", - " 53.337008\n", - " 1270.0\n", - " 48.198269\n", - " 36.841132\n", - " 0.644782\n", - " 0.359469\n", - " 146.669048\n", - " 0.949178\n", - " True\n", - " True\n", - " 0.065991\n", - " False\n", " False\n", " False\n", " False\n", @@ -3043,20 +1221,6 @@ " \n", " \n", " 1\n", - " 2\n", - " 6.598958\n", - " 126.006944\n", - " 576.0\n", - " 45.835698\n", - " 18.372329\n", - " 0.916152\n", - " 1.513685\n", - " 113.112698\n", - " 0.886154\n", - " True\n", - " True\n", - " 0.107462\n", - " False\n", " False\n", " False\n", " False\n", @@ -3065,20 +1229,6 @@ " \n", " \n", " 2\n", - " 3\n", - " 17.416667\n", - " 156.656504\n", - " 984.0\n", - " 40.751104\n", - " 31.700565\n", - " 0.628380\n", - " -1.528462\n", - " 121.396970\n", - " 0.955340\n", - " True\n", - " True\n", - " 0.098039\n", - " False\n", " False\n", " False\n", " False\n", @@ -3087,20 +1237,6 @@ " \n", " \n", " 3\n", - " 4\n", - " 4.982558\n", - " 179.337209\n", - " 344.0\n", - " 34.620290\n", - " 13.577757\n", - " 0.919884\n", - " 1.474818\n", - " 82.627417\n", - " 0.971751\n", - " True\n", - " True\n", - " 0.136228\n", - " False\n", " False\n", " False\n", " False\n", @@ -3109,20 +1245,6 @@ " \n", " \n", " 4\n", - " 5\n", - " 19.159558\n", - " 228.598210\n", - " 1899.0\n", - " 54.446578\n", - " 49.053930\n", - " 0.433912\n", - " 1.287374\n", - " 196.610173\n", - " 0.896601\n", - " True\n", - " True\n", - " 0.104794\n", - " False\n", " False\n", " False\n", " False\n", @@ -3136,37 +1258,9 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 16803\n", - " 16804\n", - " 4993.579685\n", - " 106.373030\n", - " 571.0\n", - " 52.291050\n", - " 14.719719\n", - " 0.959562\n", - " -1.563557\n", - " 116.870058\n", - " 0.976068\n", - " True\n", - " True\n", - " 0.209647\n", - " False\n", " False\n", " True\n", " False\n", @@ -3175,20 +1269,6 @@ " \n", " \n", " 16804\n", - " 16805\n", - " 4993.413242\n", - " 810.385845\n", - " 438.0\n", - " 40.105247\n", - " 14.334812\n", - " 0.933940\n", - " 1.531591\n", - " 90.970563\n", - " 0.962637\n", - " True\n", - " True\n", - " 0.477926\n", - " True\n", " False\n", " True\n", " False\n", @@ -3197,20 +1277,6 @@ " \n", " \n", " 16805\n", - " 16806\n", - " 4994.153535\n", - " 645.010101\n", - " 495.0\n", - " 50.864135\n", - " 13.112118\n", - " 0.966202\n", - " 1.538209\n", - " 111.248737\n", - " 0.951923\n", - " True\n", - " True\n", - " 0.268536\n", - " True\n", " False\n", " True\n", " False\n", @@ -3219,20 +1285,6 @@ " \n", " \n", " 16806\n", - " 16807\n", - " 4993.835570\n", - " 1244.718121\n", - " 298.0\n", - " 28.947821\n", - " 13.495126\n", - " 0.884686\n", - " -1.561806\n", - " 69.213203\n", - " 0.973856\n", - " True\n", - " True\n", - " 0.523596\n", - " True\n", " False\n", " True\n", " False\n", @@ -3240,123 +1292,302 @@ " CD8_noise\n", " \n", " \n", - " 16807\n", - " 16808\n", - " 4994.250774\n", - " 4186.876161\n", - " 323.0\n", - " 33.325316\n", - " 13.027177\n", - " 0.920429\n", - " -1.564638\n", - " 77.213203\n", - " 0.984756\n", - " True\n", - " True\n", - " 0.401436\n", - " True\n", - " False\n", - " False\n", - " False\n", - " False\n", - " Unannotated\n", + " 16807\n", + " False\n", + " False\n", + " False\n", + " False\n", + " Unannotated\n", + " \n", + " \n", + "\n", + "

16808 rows × 5 columns

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" + ], + "text/plain": [ + " Antibody_clumps CD8_noise folded_tissue ANY annotation\n", + "0 False False False False Unannotated\n", + "1 False False False False Unannotated\n", + "2 False False False False Unannotated\n", + "3 False False False False Unannotated\n", + "4 False False False False Unannotated\n", + "... ... ... ... ... ...\n", + "16803 False True False True CD8_noise\n", + "16804 False True False True CD8_noise\n", + "16805 False True False True CD8_noise\n", + "16806 False True False True CD8_noise\n", + "16807 False False False False Unannotated\n", + "\n", + "[16808 rows x 5 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show the new columns\n", + "# for each annotation class there is a True/False column\n", + "# True means cell is inside shape, False means cell is outside shape\n", + "# ANY means the cell is at least in one shape\n", + "# annotation column shows name of annotation class\n", + "adata.obs.iloc[:, -5:]" + ] + }, + { + "cell_type": "markdown", + "id": "9cdc20d4", + "metadata": {}, + "source": [ + "Now that we have a well labelled dataset we can filter out cells we consider not good enough. \n", + "We will remove cells found inside \"Antibody_clumps\" and \"folded_tissue\". \n", + "(These have squigly lines up front because of the flipped True/False status). " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "292055ac", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/.pixi/envs/spatialdata/lib/python3.12/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n" + ] + } + ], + "source": [ + "# new processed adata\n", + "adata_processed = adata[\n", + " (adata.obs[\"Area_filter\"])\n", + " & (adata.obs[\"DAPI_ratio_pass\"])\n", + " & (~adata.obs[\"Antibody_clumps\"])\n", + " & (~adata.obs[\"folded_tissue\"])\n", + "].copy() # type: ignore" + ] + }, + { + "cell_type": "markdown", + "id": "ddeca144", + "metadata": {}, + "source": [ + "As you might have seen from plotting the labels, we can see that a large area of the imaged tissue has been labelled as CD8 noise. \n", + "This kind of artefacts can happen. \n", + "The simplest solution is to reduce the marker specific signal for cells present in those annotations, so-called marker imputation. \n", + "(any better solutions are welcome!)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "cb84cc4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m16:13:12.58\u001b[0m | \u001b[1mINFO\u001b[0m | Imputing with 0.15% percentile value = 7.545454545454546\n" + ] + } + ], + "source": [ + "adata_processed = dvp.pp.impute_marker_with_annotation(\n", + " adata=adata_processed,\n", + " target_variable=\"mean_CD8\", \n", + " target_annotation_column=\"CD8_noise\",\n", + " quantile_for_imputation=0.15\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "24a5aa14", + "metadata": {}, + "source": [ + "### QuPath QC" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "482a0b18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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01detectionPOLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...
12detectionPOLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...
23detectionPOLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...
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45detectionPOLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ...
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16808 rows × 19 columns

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" ], "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "0 1 17.612598 53.337008 1270.0 48.198269 \n", - "1 2 6.598958 126.006944 576.0 45.835698 \n", - "2 3 17.416667 156.656504 984.0 40.751104 \n", - "3 4 4.982558 179.337209 344.0 34.620290 \n", - "4 5 19.159558 228.598210 1899.0 54.446578 \n", - "... ... ... ... ... ... \n", - "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", - "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", - "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", - "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", - "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", - "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", - "... ... ... ... ... ... \n", - "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", - "\n", - " Area_filter mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass \\\n", - "0 True True 0.065991 False \n", - "1 True True 0.107462 False \n", - "2 True True 0.098039 False \n", - "3 True True 0.136228 False \n", - "4 True True 0.104794 False \n", - "... ... ... ... ... \n", - "16803 True True 0.209647 False \n", - "16804 True True 0.477926 True \n", - "16805 True True 0.268536 True \n", - "16806 True True 0.523596 True \n", - "16807 True True 0.401436 True \n", - "\n", - " Antibody_clumps CD8_noise folded_tissue ANY annotation \n", - "0 False False False False Unannotated \n", - "1 False False False False Unannotated \n", - "2 False False False False Unannotated \n", - "3 False False False False Unannotated \n", - "4 False False False False Unannotated \n", - "... ... ... ... ... ... \n", - "16803 False True False True CD8_noise \n", - "16804 False True False True CD8_noise \n", - "16805 False True False True CD8_noise \n", - "16806 False True False True CD8_noise \n", - "16807 False False False False Unannotated \n", - "\n", - "[16808 rows x 19 columns]" + " label objectType geometry\n", + "0 1 detection POLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...\n", + "1 2 detection POLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...\n", + "2 3 detection POLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...\n", + "3 4 detection POLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1...\n", + "4 5 detection POLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ..." ] }, - "execution_count": 30, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "adata.obs" + "gdf = gpd.read_file(\"outputs/segmentation_for_qupath.geojson\")\n", + "gdf.head()" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "44deb355", + "execution_count": 29, + "id": "37bf07f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m15:14:09.64\u001b[0m | \u001b[1mINFO\u001b[0m | Imputing with 0.15% percentile value = 7.545454545454546\n" + "\u001b[32m16:13:12.90\u001b[0m | \u001b[1mINFO\u001b[0m | Found 15244 matching IDs between adata.obs['CellID'] and geodataframe['label'].\n" ] } ], "source": [ - "adata_CD8 = dvp.pp.impute_marker_with_annotation(\n", - " adata=adata_processed,\n", - " target_variable=\"mean_CD8\", \n", - " target_annotation_column=\"CD8_noise\",\n", - " quantile_for_imputation=0.15\n", - " )" + "# check processed cells in qupath\n", + "# this will create a geodataframe from the processed adata, with only the good cells\n", + "cells = dvp.io.adata_to_qupath(\n", + " adata=adata_processed, \n", + " geodataframe=gdf,\n", + " adataobs_on=\"CellID\",\n", + " gdf_on=\"label\",\n", + " classify_by=None,\n", + " simplify_value=None,\n", + " save_as_detection=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, + "id": "44441f39", + "metadata": {}, + "outputs": [], + "source": [ + "# let's save the file to drag it into qupath\n", + "cells.to_file(\"outputs/filtered_cells.geojson\")" + ] + }, + { + "cell_type": "markdown", + "id": "82494daf", + "metadata": {}, + "source": [ + "Napari QC not quite possible without spatialdata object, check tutorial #3 for more details" + ] + }, + { + "cell_type": "markdown", + "id": "3b4ba360", + "metadata": {}, + "source": [ + "## Phenotype the cells" + ] + }, + { + "cell_type": "markdown", + "id": "584f6a49", + "metadata": {}, + "source": [ + "Clustering of cells from imaging data can be challenging. The simplest approach is to perform unsupervised clustering algorithms like k-means, louvain, or leiden. Despite their effectiveness in scRNAseq data, the data from multiplex imaging is noisier and struggles to cluster properly. \n", + "Therefore, we use a supervised approach.\n", + "There are many elaborate approaches to phenotype your cells, machine learning algorithms, train your own XGBoost model, etc. We instead suggest we start with a simple approach with clear tradeoffs. \n", + "We will use SCIMAP phenotyping." + ] + }, + { + "cell_type": "markdown", + "id": "50394215", + "metadata": {}, + "source": [ + "Phenotying cells with scimap requires 3 steps: \n", + "1. Import manually assigned thresholds (1 per marker)\n", + "2. Rescale dataset with thresholds to help algorithm assign phenotypes\n", + "3. Phenotype cells\n", + "\n", + "Unfortunately, as of 08.07.2025, installing scimap and opendvp together is not possible. \n", + "Therefore for steps 2,3 opendvp provides a wrapper of scimap functions." + ] + }, + { + "cell_type": "markdown", + "id": "607c7f28", + "metadata": {}, + "source": [ + "### Import manually assigned thresholds" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "0da35a57", "metadata": {}, "outputs": [ @@ -3364,13 +1595,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering out all rows with value 0.0 (assuming not gated)\n", - "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Found 8 valid gates\n", - "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Markers found: ['mean_Vimentin' 'mean_CD3e' 'mean_panCK' 'mean_CD8' 'mean_COL1A1'\n", + "\u001b[32m16:13:13.29\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering out all rows with value 0.0 (assuming not gated)\n", + "\u001b[32m16:13:13.29\u001b[0m | \u001b[1mINFO\u001b[0m | Found 8 valid gates\n", + "\u001b[32m16:13:13.29\u001b[0m | \u001b[1mINFO\u001b[0m | Markers found: ['mean_Vimentin' 'mean_CD3e' 'mean_panCK' 'mean_CD8' 'mean_COL1A1'\n", " 'mean_CD20' 'mean_CD68' 'mean_Ki67']\n", - "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Samples found: ['TD_15_TNBC_subset']\n", - "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Applying log1p transformation to gate values and formatting for scimap.\n", - "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Output DataFrame columns: ['markers', 'TD_15_TNBC_subset']\n" + "\u001b[32m16:13:13.29\u001b[0m | \u001b[1mINFO\u001b[0m | Samples found: ['TD_15_TNBC_subset']\n", + "\u001b[32m16:13:13.30\u001b[0m | \u001b[1mINFO\u001b[0m | Applying log1p transformation to gate values and formatting for scimap.\n", + "\u001b[32m16:13:13.30\u001b[0m | \u001b[1mINFO\u001b[0m | Output DataFrame columns: ['markers', 'TD_15_TNBC_subset']\n" ] }, { @@ -3455,30 +1686,50 @@ "13 mean_Ki67 1.093354" ] }, - "execution_count": 33, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gates = dvp.io.import_thresholds(gates_csv_path=\"data/phenotyping/gates.csv\")\n", - "gates" + "# this function loads and processes the values ready for the phenotyping\n", + "thresholds = dvp.io.import_thresholds(gates_csv_path=\"data/phenotyping/gates.csv\")\n", + "thresholds" + ] + }, + { + "cell_type": "markdown", + "id": "5205598a", + "metadata": {}, + "source": [ + "### Get adata ready for phenotyping " ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 32, "id": "014fc45a", "metadata": {}, "outputs": [], "source": [ - "adata_phenotyping = adata_CD8[:,adata_CD8.var_names.isin(gates['markers'])].copy()\n", + "# here we subset the adata object\n", + "# so that it only has the the markers mentioned in the imported threholds\n", + "# we also label all observations in that adata, with the sample_id\n", + "adata_phenotyping = adata_processed[:,adata_processed.var_names.isin(thresholds['markers'])].copy()\n", "adata_phenotyping.obs['sample_id'] = \"TD_15_TNBC_subset\"" ] }, + { + "cell_type": "markdown", + "id": "96d637d4", + "metadata": {}, + "source": [ + "### Rescale data based on thresholds" + ] + }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 33, "id": "7fb8aa73", "metadata": {}, "outputs": [ @@ -3512,15 +1763,23 @@ "# create adata for gating by filtering unused columns\n", "adata_rescaled = dvp.pp.rescale(\n", " adata=adata_phenotyping,\n", - " gate=gates,\n", + " gate=thresholds,\n", " method=\"all\",\n", " imageid=\"sample_id\"\n", " )" ] }, + { + "cell_type": "markdown", + "id": "d7cc82ca", + "metadata": {}, + "source": [ + "### Phenotype cells" + ] + }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 34, "id": "fd0f28f3", "metadata": {}, "outputs": [ @@ -3529,159 +1788,167 @@ "text/html": [ "\n", - "\n", + "
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7ImmuneMacrophagepos
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 41, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# load the phenotyping workflow\n", + "# we must load the phenotyping workflow\n", + "# this is the set of biological knowledge we have about the markers\n", + "# and what they mean to classify the cells\n", "phenotype = pd.read_csv('data/phenotyping/celltype_matrix.csv')\n", + "# this just shows you the table \n", "phenotype.style.format(na_rep='')" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 35, "id": "98276e6e", "metadata": {}, "outputs": [], "source": [ + "# here we rename the variables to match the celltype_matrix.csv column names\n", + "# we remove the \"mean_\" part from \"mean_CD8\", resulting in \"CD8\", for each marker.\n", "adata_phenotyping.var['feature_name'] = [name.split(\"_\")[1] for name in adata_phenotyping.var_names]\n", - "adata_phenotyping.var.index = adata_phenotyping.var['feature_name'].values" + "# here we make it the index\n", + "adata_phenotyping.var.index = adata_phenotyping.var['feature_name'].values\n", + "\n", + "# we did not do this before, because the thresholds dataframe had the previous names" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 36, "id": "2fff0b1a", "metadata": {}, "outputs": [ @@ -3705,15 +1972,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/src/opendvp/tl/phenotype_cells.py:290: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/src/opendvp/tl/scimap_phenotype.py:290: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", " phenotype_labels_Consolidated = phenotype_labels.fillna(method='ffill', axis = 1)\n", - "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/src/opendvp/tl/phenotype_cells.py:290: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/src/opendvp/tl/scimap_phenotype.py:290: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " phenotype_labels_Consolidated = phenotype_labels.fillna(method='ffill', axis = 1)\n" ] } ], "source": [ - "adata_phenotyped = dvp.tl.phenotype_cells(\n", + "adata_phenotyped = dvp.tl.scimap_phenotype(\n", " adata_phenotyping, \n", " phenotype=phenotype, \n", " label=\"phenotype\",\n", @@ -3722,8 +1989,175 @@ }, { "cell_type": "code", - "execution_count": 51, - "id": "6d99c71b", + "execution_count": 37, + "id": "569cbf91", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "phenotype\n", + "Epithelial 7141\n", + "Unknown 3598\n", + "CD4_T_cell 2874\n", + "Fibroblasts 752\n", + "Mesenchymal 589\n", + "CD8_T_cell 165\n", + "B_cell 109\n", + "Macrophage 16\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets check the results\n", + "adata_phenotyped.obs.phenotype.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "c4ef4aec", + "metadata": {}, + "outputs": [], + "source": [ + "# lets transfer those phenotype labels and image label to our original adata_processed\n", + "adata_processed.obs['phenotype'] = adata_phenotyped.obs['phenotype'].copy()\n", + "adata_processed.obs['sample_id'] = \"TD_15_TNBC_subset\"" + ] + }, + { + "cell_type": "markdown", + "id": "48834a21", + "metadata": {}, + "source": [ + "### Quality control of phenotypes using QuPath" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "46e0324b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelobjectTypegeometry
01detectionPOLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...
12detectionPOLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...
23detectionPOLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...
34detectionPOLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1...
45detectionPOLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ...
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" + ], + "text/plain": [ + " label objectType geometry\n", + "0 1 detection POLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...\n", + "1 2 detection POLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...\n", + "2 3 detection POLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...\n", + "3 4 detection POLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1...\n", + "4 5 detection POLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ..." + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "12f25b75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m16:13:13.71\u001b[0m | \u001b[33m\u001b[1mWARNING\u001b[0m | phenotype is not a categorical, converting to categorical\n", + "\u001b[32m16:13:13.72\u001b[0m | \u001b[1mINFO\u001b[0m | Found 15244 matching IDs between adata.obs['CellID'] and geodataframe['label'].\n", + "\u001b[32m16:13:13.72\u001b[0m | \u001b[1mINFO\u001b[0m | Classes now in shapes: ['Epithelial' 'Unknown' 'CD4_T_cell' 'Mesenchymal' 'Fibroblasts'\n", + " 'CD8_T_cell' 'Macrophage' 'B_cell']\n", + "\u001b[32m16:13:13.72\u001b[0m | \u001b[1mINFO\u001b[0m | Parsing colors compatible with QuPath\n", + "\u001b[32m16:13:13.72\u001b[0m | \u001b[1mINFO\u001b[0m | No color_dict found, using defaults\n", + "\u001b[32m16:13:13.73\u001b[0m | \u001b[1mINFO\u001b[0m | color_dict created: {'B_cell': [31, 119, 180], 'CD4_T_cell': [255, 127, 14], 'CD8_T_cell': [44, 160, 44], 'Epithelial': [214, 39, 40], 'Fibroblasts': [148, 103, 189], 'Macrophage': [31, 119, 180], 'Mesenchymal': [255, 127, 14], 'Unknown': [44, 160, 44]}\n" + ] + } + ], + "source": [ + "# Let's create a geodataframe with cell segmentation, classified and coloured by phenotype\n", + "phenotypes = dvp.io.adata_to_qupath(\n", + " adata=adata_processed, \n", + " geodataframe=gdf,\n", + " adataobs_on=\"CellID\",\n", + " gdf_on=\"label\",\n", + " classify_by=\"phenotype\",\n", + " simplify_value=None,)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "66169a5e", "metadata": {}, "outputs": [ { @@ -3747,465 +2181,300 @@ " \n", " \n", " \n", - " CellID\n", - " Y_centroid\n", - " X_centroid\n", - " Area\n", - " MajorAxisLength\n", - " MinorAxisLength\n", - " Eccentricity\n", - " Orientation\n", - " Extent\n", - " Solidity\n", - " ...\n", - " mean_DAPI_bg_filter\n", - " DAPI_ratio\n", - " DAPI_ratio_pass\n", - " Antibody_clumps\n", - " CD8_noise\n", - " folded_tissue\n", - " ANY\n", - " annotation\n", - " sample_id\n", - " phenotype\n", + " label\n", + " objectType\n", + " geometry\n", + " classification\n", " \n", " \n", " \n", " \n", " 6\n", " 7\n", - " 28.019009\n", - " 321.572980\n", - " 1473.0\n", - " 57.990373\n", - " 34.056846\n", - " 0.809381\n", - " 0.702143\n", - " 160.367532\n", - " 0.943022\n", - " ...\n", - " True\n", - " 0.503327\n", - " True\n", - " False\n", - " False\n", - " False\n", - " False\n", - " Unannotated\n", - " TD_15_TNBC_subset\n", - " Epithelial\n", + " detection\n", + " POLYGON ((338 51.5, 326 50.5, 303.5 39, 301.5 ...\n", + " {'name': 'Epithelial', 'color': [214, 39, 40]}\n", " \n", " \n", " 7\n", " 8\n", - " 13.004021\n", - " 351.318365\n", - " 1492.0\n", - " 60.422292\n", - " 35.485098\n", - " 0.809380\n", - " 1.459721\n", - " 167.917785\n", - " 0.965071\n", - " ...\n", - " True\n", - " 0.563896\n", - " True\n", - " False\n", - " False\n", - " False\n", - " False\n", - " Unannotated\n", - " TD_15_TNBC_subset\n", - " Epithelial\n", + " detection\n", + " POLYGON ((354 35.5, 347 35.5, 337.5 23, 321 8....\n", + " {'name': 'Epithelial', 'color': [214, 39, 40]}\n", " \n", " \n", " 8\n", " 9\n", - " 8.195719\n", - " 415.693170\n", - " 981.0\n", - " 62.489467\n", - " 20.854657\n", - " 0.942668\n", - " -1.541497\n", - " 144.769553\n", - " 0.974181\n", - " ...\n", - " True\n", - " 0.473116\n", - " True\n", - " False\n", - " False\n", - " False\n", - " False\n", - " Unannotated\n", - " TD_15_TNBC_subset\n", - " Unknown\n", + " detection\n", + " POLYGON ((428 20.5, 403 18.5, 390 15.5, 386.5 ...\n", + " {'name': 'Unknown', 'color': [44, 160, 44]}\n", " \n", " \n", " 9\n", " 10\n", - " 9.020833\n", - " 482.729167\n", - " 576.0\n", - " 34.660038\n", - " 21.747314\n", - " 0.778660\n", - " -1.556045\n", - " 93.698485\n", - " 0.963211\n", - " ...\n", - " True\n", - " 0.526049\n", - " True\n", - " False\n", - " False\n", - " False\n", - " False\n", - " Unannotated\n", - " TD_15_TNBC_subset\n", - " CD4_T_cell\n", + " detection\n", + " POLYGON ((495 18.5, 472.5 21, 468.5 13, 467.5 ...\n", + " {'name': 'CD4_T_cell', 'color': [255, 127, 14]}\n", " \n", " \n", " 10\n", " 11\n", - " 11.670357\n", - " 554.346863\n", - " 813.0\n", - " 36.506883\n", - " 29.531264\n", - " 0.587914\n", - " -1.567206\n", - " 109.355339\n", - " 0.975990\n", - " ...\n", - " True\n", - " 0.649908\n", - " True\n", - " False\n", - " False\n", - " False\n", - " False\n", - " Unannotated\n", - " TD_15_TNBC_subset\n", - " Unknown\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 16801\n", - " 16802\n", - " 4992.956322\n", - " 561.156322\n", - " 435.0\n", - " 36.423914\n", - " 15.810187\n", - " 0.900884\n", - " -1.561879\n", - " 87.798990\n", - " 0.966667\n", - " ...\n", - " True\n", - " 0.461031\n", - " True\n", - " False\n", - " True\n", - " False\n", - " True\n", - " CD8_noise\n", - " TD_15_TNBC_subset\n", - " Fibroblasts\n", - " \n", - " \n", - " 16804\n", - " 16805\n", - " 4993.413242\n", - " 810.385845\n", - " 438.0\n", - " 40.105247\n", - " 14.334812\n", - " 0.933940\n", - " 1.531591\n", - " 90.970563\n", - " 0.962637\n", - " ...\n", - " True\n", - " 0.477926\n", - " True\n", - " False\n", - " True\n", - " False\n", - " True\n", - " CD8_noise\n", - " TD_15_TNBC_subset\n", - " Unknown\n", - " \n", - " \n", - " 16805\n", - " 16806\n", - " 4994.153535\n", - " 645.010101\n", - " 495.0\n", - " 50.864135\n", - " 13.112118\n", - " 0.966202\n", - " 1.538209\n", - " 111.248737\n", - " 0.951923\n", - " ...\n", - " True\n", - " 0.268536\n", - " True\n", - " False\n", - " True\n", - " False\n", - " True\n", - " CD8_noise\n", - " TD_15_TNBC_subset\n", - " Fibroblasts\n", - " \n", - " \n", - " 16806\n", - " 16807\n", - " 4993.835570\n", - " 1244.718121\n", - " 298.0\n", - " 28.947821\n", - " 13.495126\n", - " 0.884686\n", - " -1.561806\n", - " 69.213203\n", - " 0.973856\n", - " ...\n", - " True\n", - " 0.523596\n", - " True\n", - " False\n", - " True\n", - " False\n", - " True\n", - " CD8_noise\n", - " TD_15_TNBC_subset\n", - " Unknown\n", - " \n", - " \n", - " 16807\n", - " 16808\n", - " 4994.250774\n", - " 4186.876161\n", - " 323.0\n", - " 33.325316\n", - " 13.027177\n", - " 0.920429\n", - " -1.564638\n", - " 77.213203\n", - " 0.984756\n", - " ...\n", - " True\n", - " 0.401436\n", - " True\n", - " False\n", - " False\n", - " False\n", - " False\n", - " Unannotated\n", - " TD_15_TNBC_subset\n", - " Unknown\n", + " detection\n", + " POLYGON ((559 28.5, 550 27.5, 541.5 21, 538.5 ...\n", + " {'name': 'Unknown', 'color': [44, 160, 44]}\n", " \n", " \n", "\n", - "

15244 rows × 21 columns

\n", "" ], "text/plain": [ - " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", - "6 7 28.019009 321.572980 1473.0 57.990373 \n", - "7 8 13.004021 351.318365 1492.0 60.422292 \n", - "8 9 8.195719 415.693170 981.0 62.489467 \n", - "9 10 9.020833 482.729167 576.0 34.660038 \n", - "10 11 11.670357 554.346863 813.0 36.506883 \n", - "... ... ... ... ... ... \n", - "16801 16802 4992.956322 561.156322 435.0 36.423914 \n", - "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", - "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", - "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", - "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", - "\n", - " MinorAxisLength Eccentricity Orientation Extent Solidity ... \\\n", - "6 34.056846 0.809381 0.702143 160.367532 0.943022 ... \n", - "7 35.485098 0.809380 1.459721 167.917785 0.965071 ... \n", - "8 20.854657 0.942668 -1.541497 144.769553 0.974181 ... \n", - "9 21.747314 0.778660 -1.556045 93.698485 0.963211 ... \n", - "10 29.531264 0.587914 -1.567206 109.355339 0.975990 ... \n", - "... ... ... ... ... ... ... \n", - "16801 15.810187 0.900884 -1.561879 87.798990 0.966667 ... \n", - "16804 14.334812 0.933940 1.531591 90.970563 0.962637 ... \n", - "16805 13.112118 0.966202 1.538209 111.248737 0.951923 ... \n", - "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 ... \n", - "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 ... \n", - "\n", - " mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass Antibody_clumps \\\n", - "6 True 0.503327 True False \n", - "7 True 0.563896 True False \n", - "8 True 0.473116 True False \n", - "9 True 0.526049 True False \n", - "10 True 0.649908 True False \n", - "... ... ... ... ... \n", - "16801 True 0.461031 True False \n", - "16804 True 0.477926 True False \n", - "16805 True 0.268536 True False \n", - "16806 True 0.523596 True False \n", - "16807 True 0.401436 True False \n", + " label objectType geometry \\\n", + "6 7 detection POLYGON ((338 51.5, 326 50.5, 303.5 39, 301.5 ... \n", + "7 8 detection POLYGON ((354 35.5, 347 35.5, 337.5 23, 321 8.... \n", + "8 9 detection POLYGON ((428 20.5, 403 18.5, 390 15.5, 386.5 ... \n", + "9 10 detection POLYGON ((495 18.5, 472.5 21, 468.5 13, 467.5 ... \n", + "10 11 detection POLYGON ((559 28.5, 550 27.5, 541.5 21, 538.5 ... \n", "\n", - " CD8_noise folded_tissue ANY annotation sample_id \\\n", - "6 False False False Unannotated TD_15_TNBC_subset \n", - "7 False False False Unannotated TD_15_TNBC_subset \n", - "8 False False False Unannotated TD_15_TNBC_subset \n", - "9 False False False Unannotated TD_15_TNBC_subset \n", - "10 False False False Unannotated TD_15_TNBC_subset \n", - "... ... ... ... ... ... \n", - "16801 True False True CD8_noise TD_15_TNBC_subset \n", - "16804 True False True CD8_noise TD_15_TNBC_subset \n", - "16805 True False True CD8_noise TD_15_TNBC_subset \n", - "16806 True False True CD8_noise TD_15_TNBC_subset \n", - "16807 False False False Unannotated TD_15_TNBC_subset \n", - "\n", - " phenotype \n", - "6 Epithelial \n", - "7 Epithelial \n", - "8 Unknown \n", - "9 CD4_T_cell \n", - "10 Unknown \n", - "... ... \n", - "16801 Fibroblasts \n", - "16804 Unknown \n", - "16805 Fibroblasts \n", - "16806 Unknown \n", - "16807 Unknown \n", - "\n", - "[15244 rows x 21 columns]" + " classification \n", + "6 {'name': 'Epithelial', 'color': [214, 39, 40]} \n", + "7 {'name': 'Epithelial', 'color': [214, 39, 40]} \n", + "8 {'name': 'Unknown', 'color': [44, 160, 44]} \n", + "9 {'name': 'CD4_T_cell', 'color': [255, 127, 14]} \n", + "10 {'name': 'Unknown', 'color': [44, 160, 44]} " ] }, - "execution_count": 51, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "adata_phenotyped.obs" + "# it looks like this\n", + "phenotypes.head()" ] }, { "cell_type": "code", - "execution_count": 55, - "id": "0e65afa5", + "execution_count": 42, + "id": "a7598b49", "metadata": {}, "outputs": [], "source": [ - "adata = adata_CD8.copy()" + "phenotypes.to_file(\"outputs/phenotypes.geojson\")" + ] + }, + { + "cell_type": "markdown", + "id": "05cc765d", + "metadata": {}, + "source": [ + "\"Phenotypes" + ] + }, + { + "cell_type": "markdown", + "id": "3a27987d", + "metadata": {}, + "source": [ + "## Cellular neighborhoods" + ] + }, + { + "cell_type": "markdown", + "id": "c1864054", + "metadata": {}, + "source": [ + "Now we want to perform a simple cellular neighborhood analysis. \n", + "Similarly to the phenotyping step, there are many ways to do this, and we suggest one of the simplest SpatialLDA." + ] + }, + { + "cell_type": "markdown", + "id": "a90d0845", + "metadata": {}, + "source": [ + "Two decisions you must make is: \n", + "\n", + "(A) What is your criteria for a neighbor? \n", + "There are two common options:\n", + "1. radius, anything close enough to center of cell, less than x pixels away.\n", + "2. KNN, k-number of nearest neighbors, the closest x number of cells. \n", + "\n", + "In our example we will measure the 50 nearest neighbor of each cell.\n", + "\n", + "(B) How many groups do you want to cluster your cells/tissue into? \n", + "This depends on your biology/tissue and how granular you want your analysis to be. \n", + "In our example we picked k=4." ] }, { "cell_type": "code", - "execution_count": 56, - "id": "3ac68f44", + "execution_count": 131, + "id": "562caad8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing: ['TD_15_TNBC_subset']\n", + "Identifying the 50 nearest neighbours for every cell\n", + "Pre-Processing Spatial LDA\n", + "Training Spatial LDA\n", + "Calculating the Coherence Score\n", + "\n", + "Coherence Score: 0.43997410449257224\n", + "Gathering the latent weights\n" + ] + } + ], "source": [ - "adata.obs = adata_phenotyped.obs.copy()" + "adata_neighborhoods = dvp.tl.scimap_spatial_lda(\n", + " adata=adata_processed, \n", + " phenotype='phenotype', \n", + " method='knn', knn=50,\n", + " imageid='sample_id',\n", + " )" ] }, { "cell_type": "code", - "execution_count": 57, - "id": "beb01d33", + "execution_count": 132, + "id": "c800a125", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 15244 × 15\n", - " obs: 'CellID', 'Y_centroid', 'X_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Orientation', 'Extent', 'Solidity', 'Area_filter', 'mean_DAPI_bg_filter', 'DAPI_ratio', 'DAPI_ratio_pass', 'Antibody_clumps', 'CD8_noise', 'folded_tissue', 'ANY', 'annotation', 'sample_id', 'phenotype'" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Kmeans clustering\n" + ] } ], "source": [ - "adata" + "adata_neighborhoods = dvp.tl.scimap_spatial_cluster(\n", + " adata=adata_neighborhoods,\n", + " method=\"kmeans\",\n", + " k=4,\n", + " label=\"spatial_lda\",\n", + " use_raw=False\n", + ")" ] }, { "cell_type": "markdown", - "id": "48834a21", + "id": "0e78fbe5", "metadata": {}, "source": [ - "### QC phenotypes" + "### Quality control with merged shapes" ] }, { "cell_type": "code", - "execution_count": null, - "id": "05cc765d", + "execution_count": 133, + "id": "10a22421", "metadata": {}, "outputs": [], "source": [ - "#Qupath shapes" + "color_dict = {\n", + " \"0\": \"#F93A3A\",\n", + " \"1\": \"#71F976\",\n", + " \"2\": \"#3E9BFF\",\n", + " \"3\": \"#FFDD00\",\n", + "}" ] }, { - "cell_type": "markdown", - "id": "3a27987d", + "cell_type": "code", + "execution_count": null, + "id": "7bf51ac0", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m16:38:13.46\u001b[0m | \u001b[33m\u001b[1mWARNING\u001b[0m | spatial_lda is not a categorical, converting to categorical\n", + "\u001b[32m16:38:13.46\u001b[0m | \u001b[1mINFO\u001b[0m | Running Voronoi\n", + "\u001b[32m16:38:13.53\u001b[0m | \u001b[1mINFO\u001b[0m | Voronoi done\n", + "\u001b[32m16:38:14.00\u001b[0m | \u001b[1mINFO\u001b[0m | Transformed to geodataframe\n", + "\u001b[32m16:38:14.03\u001b[0m | \u001b[1mINFO\u001b[0m | Retaining 14871 valid polygons after filtering infinite ones.\n", + "\u001b[32m16:38:14.04\u001b[0m | \u001b[1mINFO\u001b[0m | Filtered out large polygons larger than 0.98 quantile\n", + "\u001b[32m16:38:14.04\u001b[0m | \u001b[1mINFO\u001b[0m | Merging polygons adjacent of the same category\n", + "\u001b[32m16:38:14.58\u001b[0m | \u001b[1mINFO\u001b[0m | Parsing colors compatible with QuPath\n", + "\u001b[32m16:38:14.58\u001b[0m | \u001b[1mINFO\u001b[0m | Custom color dictionary passed, adapting to QuPath color format\n" + ] + } + ], "source": [ - "## Cellular neighborhoods" + "voronoi = dvp.io.adata_to_voronoi(\n", + " adata=adata_neighborhoods,\n", + " classify_by=\"spatial_lda\",\n", + " color_dict=color_dict,\n", + " merge_adjacent_shapes=True,\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "c1864054", + "id": "002a4110", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# quick edits to visualize in notebook\n", + "import ast\n", + "voronoi['class_name'] = voronoi['classification'].apply(\n", + " lambda x: ast.literal_eval(x).get('name') if isinstance(x, str) else x.get('name'))\n", + "voronoi['color'] = voronoi['class_name'].map(color_dict)\n", + "voronoi.plot(color=voronoi['color'])" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "562caad8", + "execution_count": 136, + "id": "a0164589", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/.pixi/envs/spatialdata/lib/python3.12/site-packages/pyogrio/geopandas.py:710: UserWarning: 'crs' was not provided. The output dataset will not have projection information defined and may not be usable in other systems.\n", + " write(\n" + ] + } + ], + "source": [ + "#save to file\n", + "voronoi.to_file(\"outputs/voronoi_neighborhoods.geojson\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "opendvp_tut", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -4219,7 +2488,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.12.11" } }, "nbformat": 4, diff --git a/docs/Tutorials/T2_ProteomicsIntegration.ipynb b/docs/Tutorials/T2_ProteomicsIntegration.ipynb index cee0d6c..bf5f2cd 100644 --- a/docs/Tutorials/T2_ProteomicsIntegration.ipynb +++ b/docs/Tutorials/T2_ProteomicsIntegration.ipynb @@ -1,13 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "a44cbb39", - "metadata": {}, - "source": [ - "# Integrating proteomics and imaging data" - ] - }, { "cell_type": "markdown", "id": "e07c2e72", diff --git a/docs/Tutorials/T3_DownstreamProteomics.ipynb b/docs/Tutorials/T3_DownstreamProteomics.ipynb index f36c94b..c7dfc25 100644 --- a/docs/Tutorials/T3_DownstreamProteomics.ipynb +++ b/docs/Tutorials/T3_DownstreamProteomics.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "912f7995", "metadata": {}, "outputs": [ @@ -92,6 +92,24 @@ "### First step, describe the dataset" ] }, + { + "cell_type": "markdown", + "id": "f8858be8", + "metadata": {}, + "source": [ + "SMALL DESCRIPTION OF WHAT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dee02aff", + "metadata": {}, + "outputs": [], + "source": [ + "# EXPLAIN RCNS OR SIMPLY GROUP NAMES" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -134,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "e2a64dbc", "metadata": {}, "outputs": [ @@ -160,10 +178,259 @@ } ], "source": [ - "dvp.plotting.density(adata=adata, color_by=\"RCN\")\n", + "dvp.plotting.density(adata=adata, color_by=\"RCN\") #AGREGATION ? \n", "dvp.plotting.density(adata=adata, color_by=\"QuPath_class\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "dec7c722", + "metadata": {}, + "outputs": [], + "source": [ + "# rankplot" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3987e978", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.5.0'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dvp.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6458fb31", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Any\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from adjustText import adjust_text\n", + "from anndata import AnnData\n", + "from matplotlib.figure import Figure\n", + "\n", + "from opendvp.tl import filter_features_byNaNs, impute_gaussian\n", + "\n", + "#TODO Assume adata has non-filtered non-imputed data\n", + "\n", + "def rankplot(\n", + " adata: AnnData,\n", + " adata_obs_key: str,\n", + " groups: list[str] | None = None,\n", + " proteins_to_label: list[str] | None = None,\n", + " group_colors: dict[str, str] | None = None,\n", + " group_offset: dict[str, float] | None = None,\n", + " return_fig: bool = False,\n", + " ax: Any | None = None,\n", + " **kwargs\n", + ") -> Figure | None:\n", + " \"\"\"Plot a rank plot of average protein abundance in an AnnData object.\n", + "\n", + " Parameters\n", + " ----------\n", + " adata : AnnData\n", + " Annotated data matrix.\n", + " adata_obs_key : str\n", + " Key in adata.obs indicating group labels.\n", + " groups : list of str\n", + " Groups from adata.obs[adata_obs_key] to include.\n", + " proteins_to_label : list of str, optional\n", + " List of feature names (in adata.var_names) to label on the plot.\n", + " group_colors : dict, optional\n", + " Dictionary mapping group names to colors.\n", + " group_offset : dict, optional\n", + " Dictionary mapping group names to x-axis offset (for shifting lines).\n", + " return_fig : bool, optional\n", + " If True, returns the matplotlib figure object for further customization.\n", + " ax : matplotlib.axes.Axes, optional\n", + " Axes object to plot on. If None, a new figure and axes are created.\n", + " **kwargs\n", + " Additional keyword arguments passed to matplotlib plot.\n", + "\n", + " Returns:\n", + " -------\n", + " fig : matplotlib.figure.Figure or None\n", + " The figure object if return_fig is True, otherwise None.\n", + " \"\"\"\n", + " if ax is None:\n", + " fig, ax = plt.subplots(figsize=(10, 10))\n", + " else:\n", + " fig = ax.figure\n", + " texts = []\n", + "\n", + " adata_copy = adata.copy()\n", + "\n", + " if groups is None:\n", + " groups = adata_copy.obs[adata_obs_key].unique().tolist()\n", + "\n", + " df_sns = pd.DataFrame(columns=[\"group\", \"rank\", \"mean\", \"protein\"])\n", + "\n", + " for group in groups:\n", + " #split adata\n", + " adata_group = adata_copy[adata_copy.obs[adata_obs_key] == group].copy()\n", + " #filter\n", + " adata_group = filter_features_byNaNs(adata_group)\n", + " #impute\n", + " adata_group = impute_gaussian(adata_group)\n", + "\n", + " X_group = np.asarray(adata_group.X)\n", + " mean_vals = X_group.mean(axis=0)\n", + " ranks = pd.Series(mean_vals, index=adata_group.var_names).rank(ascending=False, method='min').astype(int)\n", + "\n", + " rank_col_name = f\"ranking_{group}\" if len(groups) > 1 else \"ranking\"\n", + " adata_group.var[rank_col_name] = ranks\n", + "\n", + " df_plot = pd.DataFrame({\n", + " \"group\": group,\n", + " 'rank': ranks,\n", + " 'mean': mean_vals,\n", + " 'protein': adata_group.var_names\n", + " }).sort_values('rank')\n", + "\n", + " df_sns = pd.concat([df_sns,df_plot])\n", + "\n", + " if group_colors:\n", + " sns.scatterplot(data=df_sns, x=\"rank\", y=\"mean\", hue=\"group\", palette=group_colors, ax=ax, size=5, linewidth=0.1)\n", + " else:\n", + " sns.scatterplot(data=df_sns, x=\"rank\", y=\"mean\", hue=\"group\", ax=ax, size=5, linewidth=0.1)\n", + "\n", + " if proteins_to_label:\n", + " labeled_df = df_sns[df_sns['protein'].isin(proteins_to_label)]\n", + " for _, row in labeled_df.iterrows():\n", + " group = row['group']\n", + " label_color = group_colors[group] if group_colors and group in group_colors else 'black'\n", + "\n", + " texts.append(\n", + " ax.text(\n", + " row['rank'],\n", + " row['mean'],\n", + " row['protein'],\n", + " fontsize=15,\n", + " ha='center',\n", + " color=label_color,\n", + " bbox=dict(\n", + " facecolor='white',\n", + " edgecolor='none',\n", + " alpha=0.6,\n", + " boxstyle='round,pad=0.3',\n", + " ),\n", + " )\n", + " )\n", + "\n", + "\n", + " if proteins_to_label and texts:\n", + " adjust_text(texts, arrowprops=dict(arrowstyle='->', color='black'))\n", + "\n", + " ax.set_xlabel(\"Rank (1 = most abundant)\")\n", + " ax.set_ylabel(\"Average abundance\")\n", + " ax.set_title(\"Protein abundance ranking\")\n", + " \n", + " if len(groups) > 1:\n", + " ax.legend(title=adata_obs_key)\n", + "\n", + " plt.tight_layout()\n", + " if return_fig:\n", + " return fig\n", + " else:\n", + " plt.show()\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a4a652d0", + "metadata": {}, + "outputs": [], + "source": [ + "adata.X = np.log2(adata.X)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "066a8121", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m09:34:10.16\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering protein with at least 70.0% valid values in ANY group\n", + "\u001b[32m09:34:10.16\u001b[0m | \u001b[1mINFO\u001b[0m | Calculating overall QC metrics for all features.\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | No grouping variable was provided, filtering across all samples using overall validity.\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | Complete QC metrics for all initial features stored in `adata.uns['filter_features_byNaNs_qc_metrics']`.\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | 4301 proteins were kept.\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | 2462 proteins were removed.\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | filter_features_byNaNs complete.\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | Storing original data in `adata.layers['unimputed']`.\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | Imputation with Gaussian distribution PER PROTEIN\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | Mean number of missing values per sample: 102.6 out of 4301 proteins\n", + "\u001b[32m09:34:10.17\u001b[0m | \u001b[1mINFO\u001b[0m | Mean number of missing values per protein: 0.12 out of 5 samples\n", + "\u001b[32m09:34:12.03\u001b[0m | \u001b[1mINFO\u001b[0m | Imputation complete. QC metrics stored in `adata.uns['impute_gaussian_qc_metrics']`.\n", + "\u001b[32m09:34:12.04\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering protein with at least 70.0% valid values in ANY group\n", + "\u001b[32m09:34:12.04\u001b[0m | \u001b[1mINFO\u001b[0m | Calculating overall QC metrics for all features.\n", + "\u001b[32m09:34:12.04\u001b[0m | \u001b[1mINFO\u001b[0m | No grouping variable was provided, filtering across all samples using overall validity.\n", + "\u001b[32m09:34:12.04\u001b[0m | \u001b[1mINFO\u001b[0m | Complete QC metrics for all initial features stored in `adata.uns['filter_features_byNaNs_qc_metrics']`.\n", + "\u001b[32m09:34:12.04\u001b[0m | \u001b[1mINFO\u001b[0m | 3627 proteins were kept.\n", + "\u001b[32m09:34:12.05\u001b[0m | \u001b[1mINFO\u001b[0m | 3136 proteins were removed.\n", + "\u001b[32m09:34:12.05\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | filter_features_byNaNs complete.\n", + "\u001b[32m09:34:12.05\u001b[0m | \u001b[1mINFO\u001b[0m | Storing original data in `adata.layers['unimputed']`.\n", + "\u001b[32m09:34:12.05\u001b[0m | \u001b[1mINFO\u001b[0m | Imputation with Gaussian distribution PER PROTEIN\n", + "\u001b[32m09:34:12.05\u001b[0m | \u001b[1mINFO\u001b[0m | Mean number of missing values per sample: 103.4 out of 3627 proteins\n", + "\u001b[32m09:34:12.05\u001b[0m | \u001b[1mINFO\u001b[0m | Mean number of missing values per protein: 0.14 out of 5 samples\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/x7/grkjlk8s223dy6234rnz1885mxz2_6/T/ipykernel_10065/1099291906.py:88: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df_sns = pd.concat([df_sns,df_plot])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m09:34:13.55\u001b[0m | \u001b[1mINFO\u001b[0m | Imputation complete. QC metrics stored in `adata.uns['impute_gaussian_qc_metrics']`.\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rankplot(adata=adata, adata_obs_key=\"RCN\")" + ] + }, { "cell_type": "markdown", "id": "c19e322b", @@ -794,6 +1061,14 @@ "dvp.io.export_adata(adata=adata_imputed, path_to_dir=\"data/checkpoints\", checkpoint_name=\"3_imputed\")" ] }, + { + "cell_type": "markdown", + "id": "0dc989a2", + "metadata": {}, + "source": [ + "## Let's dig into the biology" + ] + }, { "cell_type": "markdown", "id": "5918dc5b", @@ -873,7 +1148,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "651cce25", "metadata": {}, "outputs": [ @@ -897,7 +1172,7 @@ } ], "source": [ - "# let's plot the direction of each feature\n", + "# let's plot the contribution of each protein to the PC1 and PC2\n", "dvp.plotting.pca_loadings(adata_imputed)" ] }, @@ -928,6 +1203,16 @@ "### heatmap" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "29508d33", + "metadata": {}, + "outputs": [], + "source": [ + "# hierarchical clustering of proteins and samples to show how the features group from a high level" + ] + }, { "cell_type": "code", "execution_count": 21, @@ -1050,7 +1335,7 @@ "id": "c74a6047", "metadata": {}, "source": [ - "### plotting with volcano plot" + "### Volcano plot" ] }, { diff --git a/docs/Tutorials/index.md b/docs/Tutorials/index.md index 14d5a0b..32cb9bd 100644 --- a/docs/Tutorials/index.md +++ b/docs/Tutorials/index.md @@ -2,23 +2,24 @@ This section provides hands-on tutorials to guide you through the core workflows of `openDVP`. Each tutorial uses our demo dataset and covers a key aspect of a Deep Visual Proteomics analysis. -### Tutorial 1: Image Analysis +## Tutorial 1: Image Analysis Learn the fundamentals of processing and analyzing high-plex immunofluorescence images. This tutorial covers essential steps like quality control, cell segmentation, and feature extraction to prepare your imaging data for integration. -### Tutorial 2: Integration of Imaging with Proteomics +## Tutorial 2: Integration of Imaging with Proteomics + Discover how to link your imaging-derived cellular data with quantitative proteomics measurements. This tutorial leverages `SpatialData` to create a unified data object, enabling powerful spatial-omics analysis. -### Tutorial 3: Downstream Proteomics Analysis +## Tutorial 3: Downstream Proteomics Analysis + Dive into the analysis of your integrated proteomics data. This tutorial walks you through common downstream tasks such as data normalization, identifying cell populations, and performing differential abundance analysis to uncover biological insights. -## Tutorials +## Tutorial links - [Tutorial 1: Image analysis](T1_ImageAnalysis) - [Tutorial 2: Integration of imaging with proteomics](T2_ProteomicsIntegration) - [Tutorial 3: Downstream proteomics analysis](T3_DownstreamProteomics) - ```{toctree} :maxdepth: 2 :hidden: @@ -26,4 +27,4 @@ Dive into the analysis of your integrated proteomics data. This tutorial walks y T1_ImageAnalysis T2_ProteomicsIntegration T3_DownstreamProteomics -``` \ No newline at end of file +``` diff --git a/docs/Tutorials/old/T1_ImageAnalysis.ipynb b/docs/Tutorials/old/T1_ImageAnalysis.ipynb new file mode 100644 index 0000000..b837899 --- /dev/null +++ b/docs/Tutorials/old/T1_ImageAnalysis.ipynb @@ -0,0 +1,4227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "29cad22f", + "metadata": {}, + "source": [ + "# Image Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "28f2691f", + "metadata": {}, + "source": [ + "In this tutorial I will show you how to perform quality control on your image processing and segmentation results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8e40b139", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "#lets take a quick look\n", + "import skimage.io as io\n", + "import numpy as np\n", + "import opendvp as dvp\n", + "\n", + "import napari\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import geopandas as gpd" + ] + }, + { + "cell_type": "markdown", + "id": "369fbdfd", + "metadata": {}, + "source": [ + "## Part 1: Visualize segmentation in QuPath" + ] + }, + { + "cell_type": "markdown", + "id": "1326fcee", + "metadata": {}, + "source": [ + "QuPath is a great piece of software created to allow users to see their images in a smooth manner." + ] + }, + { + "cell_type": "markdown", + "id": "0ba4c6ea", + "metadata": {}, + "source": [ + "Here is our demo data, this is how it should look like" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a41afe36", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;36m.\u001b[0m\n", + "├── Tutorial_1.ipynb\n", + "├── \u001b[1;36mdata\u001b[0m\n", + "│   ├── \u001b[1;36mimage\u001b[0m\n", + "│   │   └── \u001b[31mmIF.ome.tif\u001b[0m\n", + "│   ├── \u001b[1;36mmanual_artefact_annotations\u001b[0m\n", + "│   │   └── \u001b[31martefacts.geojson\u001b[0m\n", + "│   ├── \u001b[1;36mquantification\u001b[0m\n", + "│   │   └── \u001b[31mquant.csv\u001b[0m\n", + "│   └── \u001b[1;36msegmentation\u001b[0m\n", + "│   └── \u001b[31msegmentation_mask.tif\u001b[0m\n", + "└── \u001b[1;36moutputs\u001b[0m\n", + " └── segmentation_for_qupath.geojson\n", + "\n", + "7 directories, 6 files\n" + ] + } + ], + "source": [ + "! tree" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2383e316", + "metadata": {}, + "outputs": [], + "source": [ + "# let's perform some quick QC\n", + "path_to_segmentation = \"data/segmentation/segmentation_mask.tif\"\n", + "seg = io.imread(path_to_segmentation)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "32344e09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of pixels in x,y: (5000, 5000)\n", + "Number of segmented objects 16808\n" + ] + } + ], + "source": [ + "print(f\"Number of pixels in x,y: {seg.shape}\")\n", + "print(f\"Number of segmented objects {np.unique(seg).size -1}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "413d5d1d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/x7/grkjlk8s223dy6234rnz1885mxz2_6/T/ipykernel_14111/2237838827.py:2: FutureWarning: `imshow` is deprecated since version 0.25 and will be removed in version 0.27. Please use `matplotlib`, `napari`, etc. to visualize images.\n", + " io.imshow(seg, vmax=1)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#quick look\n", + "io.imshow(seg, vmax=1)" + ] + }, + { + "cell_type": "markdown", + "id": "f77cf31f", + "metadata": {}, + "source": [ + "## Let's visualize interactively in Napari or Qupath" + ] + }, + { + "cell_type": "markdown", + "id": "bafeede8", + "metadata": {}, + "source": [ + "### For Napari" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "28c2f46e", + "metadata": {}, + "outputs": [], + "source": [ + "#load image\n", + "image = io.imread(\"data/image/mIF.ome.tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c307056c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# this should produce a napari window with image and segmentation mask\n", + "viewer = napari.Viewer()\n", + "viewer.add_image(image, name=\"mIF_image\")\n", + "viewer.add_labels(seg, name='Segmentation')" + ] + }, + { + "cell_type": "markdown", + "id": "7084a8db", + "metadata": {}, + "source": [ + "### For QuPath" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "76dc97ba", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/dask/dataframe/__init__.py:31: FutureWarning: The legacy Dask DataFrame implementation is deprecated and will be removed in a future version. Set the configuration option `dataframe.query-planning` to `True` or None to enable the new Dask Dataframe implementation and silence this warning.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mINFO \u001b[0m no axes information specified in the object, setting `dims` to: \u001b[1m(\u001b[0m\u001b[32m'y'\u001b[0m, \u001b[32m'x'\u001b[0m\u001b[1m)\u001b[0m \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import DistributionNotFound, get_distribution\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m13:18:26.98\u001b[0m | \u001b[1mINFO\u001b[0m | Simplifying the geometry with tolerance 1\n" + ] + } + ], + "source": [ + "# transform mask into shapes\n", + "gdf = dvp.io.segmask_to_qupath(path_to_segmentation, simplify_value=1, save_as_detection=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e16acc04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " geometry objectType\n", + "label \n", + "1 POLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ... detection\n", + "2 POLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11... detection\n", + "3 POLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ... detection\n", + "4 POLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1... detection\n", + "5 POLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ... detection" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b84aeade", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/pyogrio/geopandas.py:710: UserWarning: 'crs' was not provided. The output dataset will not have projection information defined and may not be usable in other systems.\n", + " write(\n" + ] + } + ], + "source": [ + "gdf.to_file(\"outputs/segmentation_for_qupath.geojson\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1e11457", + "metadata": {}, + "source": [ + "This file you just drag and drop into qupath after you have loaded the image." + ] + }, + { + "cell_type": "markdown", + "id": "d2b42ba2", + "metadata": {}, + "source": [ + "## Quant to adata" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ed1f9c70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:06.74\u001b[0m | \u001b[1mINFO\u001b[0m | Detected 0 in 'CellID' — shifting all values by +1 for 1-based indexing.\n", + "\u001b[32m15:14:06.75\u001b[0m | \u001b[1mINFO\u001b[0m | 16808 cells and 15 variables\n" + ] + } + ], + "source": [ + "adata = dvp.io.quant_to_adata(\"data/quantification/quant.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "11c88879", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidity
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154
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344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751
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" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 \n", + "1 2 6.598958 126.006944 576.0 45.835698 \n", + "2 3 17.416667 156.656504 984.0 40.751104 \n", + "3 4 4.982558 179.337209 344.0 34.620290 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 \n", + "... ... ... ... ... ... \n", + "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", + "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", + "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", + "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", + "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", + "\n", + " MinorAxisLength Eccentricity Orientation Extent Solidity \n", + "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", + "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", + "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", + "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", + "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", + "... ... ... ... ... ... \n", + "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", + "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", + "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", + "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", + "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", + "\n", + "[16808 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs" + ] + }, + { + "cell_type": "markdown", + "id": "fcd7d1c3", + "metadata": {}, + "source": [ + "## Filter cells" + ] + }, + { + "cell_type": "markdown", + "id": "bdf02b44", + "metadata": {}, + "source": [ + "### Filter cells too big" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dc172d8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidity
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601
.................................
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16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756
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16808 rows × 10 columns

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" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 \n", + "1 2 6.598958 126.006944 576.0 45.835698 \n", + "2 3 17.416667 156.656504 984.0 40.751104 \n", + "3 4 4.982558 179.337209 344.0 34.620290 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 \n", + "... ... ... ... ... ... \n", + "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", + "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", + "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", + "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", + "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", + "\n", + " MinorAxisLength Eccentricity Orientation Extent Solidity \n", + "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", + "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", + "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", + "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", + "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", + "... ... ... ... ... ... \n", + "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", + "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", + "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", + "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", + "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", + "\n", + "[16808 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f645161a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_abs_value for feature 'Area'...\n", + "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Feature 'Area' identified from adata.obs.\n", + "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'Area' >= 295.0000 (from quantile bound: 0.01).\n", + "\u001b[32m15:14:06.77\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'Area' <= 2376.4400 (from quantile bound: 0.99).\n", + "\u001b[32m15:14:06.77\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | 16475 of 16808 cells (98.02%) passed the filter.\n", + "\u001b[32m15:14:06.78\u001b[0m | \u001b[1mINFO\u001b[0m | New boolean column 'Area_filter' added to adata.obs.\n" + ] + } + ], + "source": [ + "adata = dvp.tl.filter_by_abs_value(adata=adata,feature_name=\"Area\", lower_bound=0.01, upper_bound=0.99, mode=\"quantile\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "810c79f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filter
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178True
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154True
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340True
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751True
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601True
....................................
16803168044993.579685106.373030571.052.29105014.7197190.959562-1.563557116.8700580.976068True
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637True
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923True
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856True
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756True
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16808 rows × 11 columns

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mean_DAPI_2
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: []\n", + "Index: [mean_750_bg, mean_647_bg, mean_555_bg, mean_488_bg, mean_DAPI_bg, mean_Vimentin, mean_CD3e, mean_panCK, mean_CD8, mean_DAPI_1, mean_COL1A1, mean_CD20, mean_CD68, mean_Ki67, mean_DAPI_2]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.var" + ] + }, + { + "cell_type": "markdown", + "id": "3f0aa7e5", + "metadata": {}, + "source": [ + "### Filter by initial nuclear stain signal" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "20844eba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(data=adata.X, columns=adata.var_names)\n", + "sns.histplot(data=df, x=\"mean_DAPI_bg\", bins=200)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "844759f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_abs_value for feature 'mean_DAPI_bg'...\n", + "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Feature 'mean_DAPI_bg' identified from adata.X.\n", + "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'mean_DAPI_bg' >= 8.0822 (from quantile bound: 0.01).\n", + "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping cells with 'mean_DAPI_bg' <= 47.2041 (from quantile bound: 0.99).\n", + "\u001b[32m15:14:07.13\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | 16470 of 16808 cells (97.99%) passed the filter.\n", + "\u001b[32m15:14:07.13\u001b[0m | \u001b[1mINFO\u001b[0m | New boolean column 'mean_DAPI_bg_filter' added to adata.obs.\n" + ] + } + ], + "source": [ + "adata = dvp.tl.filter_by_abs_value(adata=adata,feature_name=\"mean_DAPI_bg\", lower_bound=0.01, upper_bound=0.99, mode=\"quantile\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "96a3c953", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filter
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue
.......................................
16803168044993.579685106.373030571.052.29105014.7197190.959562-1.563557116.8700580.976068TrueTrue
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637TrueTrue
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923TrueTrue
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856TrueTrue
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756TrueTrue
\n", + "

16808 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 \n", + "1 2 6.598958 126.006944 576.0 45.835698 \n", + "2 3 17.416667 156.656504 984.0 40.751104 \n", + "3 4 4.982558 179.337209 344.0 34.620290 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 \n", + "... ... ... ... ... ... \n", + "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", + "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", + "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", + "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", + "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", + "\n", + " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", + "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", + "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", + "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", + "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", + "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", + "... ... ... ... ... ... \n", + "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", + "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", + "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", + "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", + "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", + "\n", + " Area_filter mean_DAPI_bg_filter \n", + "0 True True \n", + "1 True True \n", + "2 True True \n", + "3 True True \n", + "4 True True \n", + "... ... ... \n", + "16803 True True \n", + "16804 True True \n", + "16805 True True \n", + "16806 True True \n", + "16807 True True \n", + "\n", + "[16808 rows x 12 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs" + ] + }, + { + "cell_type": "markdown", + "id": "c6dab6a3", + "metadata": {}, + "source": [ + "### Filter by ratio of nuclear stain between last and first DAPI images" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e852c5ba", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.DataFrame(data=adata.X, columns=adata.var_names)\n", + "df['ratio'] = df['mean_DAPI_2'] / df['mean_DAPI_bg']\n", + "\n", + "fig,ax = plt.subplots()\n", + "sns.histplot(data=df, x=\"ratio\", bins=200, ax=ax)\n", + "ax.set_xlim(0,1.5)\n", + "ax.set_yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c2224ca4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Starting filter_by_ratio...\n", + "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Number of cells with DAPI ratio < 0.25: 1035\n", + "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Number of cells with DAPI ratio > 1.05: 28\n", + "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Cells with DAPI ratio between 0.25 and 1.05: 15745\n", + "\u001b[32m15:14:07.44\u001b[0m | \u001b[1mINFO\u001b[0m | Cells filtered: 6.32%\n", + "\u001b[32m15:14:07.44\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | filter_by_ratio complete.\n" + ] + } + ], + "source": [ + "adata = dvp.tl.filter_by_ratio(adata=adata, end_cycle=\"mean_DAPI_2\", start_cycle=\"mean_DAPI_bg\", label=\"DAPI\", min_ratio=0.25, max_ratio=1.05)" + ] + }, + { + "cell_type": "markdown", + "id": "554670a5", + "metadata": {}, + "source": [ + "### Filter by manual annotations" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6ad690cd", + "metadata": {}, + "outputs": [], + "source": [ + "#check annotations\n", + "gdf = gpd.read_file(\"data/manual_artefact_annotations/artefacts.geojson\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "78be7461", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id objectType \\\n", + "0 9dbac0eb-6171-4da8-9c3f-846ecdb81dfb annotation \n", + "1 cc4df5d0-fe6b-4285-849a-698851827e9c annotation \n", + "2 e6aaf657-f4e7-401f-834b-a2fd5a072300 annotation \n", + "3 be635097-4631-46e7-b1a8-878363184124 annotation \n", + "4 baff029c-3349-4fa2-946a-0f5e55c46dc8 annotation \n", + "5 ad114d98-3048-4e3c-9a94-982e72a95515 annotation \n", + "6 99a65f87-acf2-434d-9ab5-7692e39af63b annotation \n", + "7 7908eec6-399d-4e17-90ba-26446b5dcacd annotation \n", + "8 f1021867-a0ec-4323-8cf9-d9d70063566a annotation \n", + "9 46b88b7d-3f7f-4a7d-812d-5ae37a6090cb annotation \n", + "\n", + " classification \\\n", + "0 { \"name\": \"folded_tissue\", \"color\": [ 176, 102... \n", + "1 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", + "2 { \"name\": \"folded_tissue\", \"color\": [ 176, 102... \n", + "3 { \"name\": \"CD8_noise\", \"color\": [ 51, 236, 220... \n", + "4 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", + "5 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", + "6 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", + "7 { \"name\": \"Antibody_clumps\", \"color\": [ 32, 19... \n", + "8 { \"name\": \"CD8_noise\", \"color\": [ 51, 236, 220... \n", + "9 { \"name\": \"folded_tissue\", \"color\": [ 176, 102... \n", + "\n", + " geometry \n", + "0 POLYGON ((722 2645, 702 2647, 689.93 2650.81, ... \n", + "1 POLYGON ((4685 2530, 4682 2531, 4677 2531, 467... \n", + "2 POLYGON ((3127 3675, 3119 3676, 3116 3677, 311... \n", + "3 POLYGON ((117 3008, 110 3009.62, 105 3010, 96.... \n", + "4 POLYGON ((3987 4058, 3984 4059, 3979 4059, 397... \n", + "5 POLYGON ((4791 1546, 4788.47 1546.95, 4788 154... \n", + "6 POLYGON ((4636 1840, 4628 1843, 4620 1847, 461... \n", + "7 POLYGON ((4693 2599, 4690 2600, 4685 2600, 468... \n", + "8 POLYGON ((3 1994, 0 1994.23, 0 2047.23, 0 2052... \n", + "9 POLYGON ((1745 18, 1743.71 18.43, 1738 19, 172... " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d5fae116", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots()\n", + "gdf.plot(column=\"classification\", legend=True, figsize=(8, 6), ax=ax)\n", + "ax.invert_yaxis()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5502375b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filterDAPI_ratioDAPI_ratio_pass
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue0.065991False
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue0.107462False
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue0.098039False
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue0.136228False
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue0.104794False
.............................................
16803168044993.579685106.373030571.052.29105014.7197190.959562-1.563557116.8700580.976068TrueTrue0.209647False
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637TrueTrue0.477926True
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923TrueTrue0.268536True
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856TrueTrue0.523596True
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756TrueTrue0.401436True
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16808 rows × 14 columns

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" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 \n", + "1 2 6.598958 126.006944 576.0 45.835698 \n", + "2 3 17.416667 156.656504 984.0 40.751104 \n", + "3 4 4.982558 179.337209 344.0 34.620290 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 \n", + "... ... ... ... ... ... \n", + "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", + "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", + "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", + "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", + "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", + "\n", + " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", + "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", + "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", + "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", + "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", + "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", + "... ... ... ... ... ... \n", + "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", + "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", + "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", + "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", + "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", + "\n", + " Area_filter mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass \n", + "0 True True 0.065991 False \n", + "1 True True 0.107462 False \n", + "2 True True 0.098039 False \n", + "3 True True 0.136228 False \n", + "4 True True 0.104794 False \n", + "... ... ... ... ... \n", + "16803 True True 0.209647 False \n", + "16804 True True 0.477926 True \n", + "16805 True True 0.268536 True \n", + "16806 True True 0.523596 True \n", + "16807 True True 0.401436 True \n", + "\n", + "[16808 rows x 14 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "07e5d802", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:08.83\u001b[0m | \u001b[1mINFO\u001b[0m | Each class of annotation will be a different column in adata.obs\n", + "\u001b[32m15:14:08.83\u001b[0m | \u001b[1mINFO\u001b[0m | TRUE means cell was inside annotation, FALSE means cell not in annotation\n", + "\u001b[32m15:14:08.84\u001b[0m | \u001b[1mINFO\u001b[0m | GeoJSON loaded, detected: 10 annotations\n" + ] + } + ], + "source": [ + "adata = dvp.tl.filter_by_annotation(adata=adata, path_to_geojson=\"data/manual_artefact_annotations/artefacts.geojson\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0a0169b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filterDAPI_ratioDAPI_ratio_passAntibody_clumpsCD8_noisefolded_tissueANYannotation
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue0.065991FalseFalseFalseFalseFalseUnannotated
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue0.107462FalseFalseFalseFalseFalseUnannotated
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue0.098039FalseFalseFalseFalseFalseUnannotated
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue0.136228FalseFalseFalseFalseFalseUnannotated
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue0.104794FalseFalseFalseFalseFalseUnannotated
............................................................
16803168044993.579685106.373030571.052.29105014.7197190.959562-1.563557116.8700580.976068TrueTrue0.209647FalseFalseTrueFalseTrueCD8_noise
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637TrueTrue0.477926TrueFalseTrueFalseTrueCD8_noise
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923TrueTrue0.268536TrueFalseTrueFalseTrueCD8_noise
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856TrueTrue0.523596TrueFalseTrueFalseTrueCD8_noise
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756TrueTrue0.401436TrueFalseFalseFalseFalseUnannotated
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16808 rows × 19 columns

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" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 \n", + "1 2 6.598958 126.006944 576.0 45.835698 \n", + "2 3 17.416667 156.656504 984.0 40.751104 \n", + "3 4 4.982558 179.337209 344.0 34.620290 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 \n", + "... ... ... ... ... ... \n", + "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", + "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", + "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", + "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", + "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", + "\n", + " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", + "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", + "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", + "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", + "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", + "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", + "... ... ... ... ... ... \n", + "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", + "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", + "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", + "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", + "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", + "\n", + " Area_filter mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass \\\n", + "0 True True 0.065991 False \n", + "1 True True 0.107462 False \n", + "2 True True 0.098039 False \n", + "3 True True 0.136228 False \n", + "4 True True 0.104794 False \n", + "... ... ... ... ... \n", + "16803 True True 0.209647 False \n", + "16804 True True 0.477926 True \n", + "16805 True True 0.268536 True \n", + "16806 True True 0.523596 True \n", + "16807 True True 0.401436 True \n", + "\n", + " Antibody_clumps CD8_noise folded_tissue ANY annotation \n", + "0 False False False False Unannotated \n", + "1 False False False False Unannotated \n", + "2 False False False False Unannotated \n", + "3 False False False False Unannotated \n", + "4 False False False False Unannotated \n", + "... ... ... ... ... ... \n", + "16803 False True False True CD8_noise \n", + "16804 False True False True CD8_noise \n", + "16805 False True False True CD8_noise \n", + "16806 False True False True CD8_noise \n", + "16807 False False False False Unannotated \n", + "\n", + "[16808 rows x 19 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "292055ac", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n" + ] + } + ], + "source": [ + "# new processed adata\n", + "adata_processed = adata[\n", + " (adata.obs[\"Area_filter\"])\n", + " & (adata.obs[\"DAPI_ratio_pass\"])\n", + " & (~adata.obs[\"Antibody_clumps\"])\n", + " & (~adata.obs[\"folded_tissue\"])\n", + "].copy() # type: ignore" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c163d788", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 16808 × 15\n", + " obs: 'CellID', 'Y_centroid', 'X_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Orientation', 'Extent', 'Solidity', 'Area_filter', 'mean_DAPI_bg_filter', 'DAPI_ratio', 'DAPI_ratio_pass', 'Antibody_clumps', 'CD8_noise', 'folded_tissue', 'ANY', 'annotation'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2232f39e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 15244 × 15\n", + " obs: 'CellID', 'Y_centroid', 'X_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Orientation', 'Extent', 'Solidity', 'Area_filter', 'mean_DAPI_bg_filter', 'DAPI_ratio', 'DAPI_ratio_pass', 'Antibody_clumps', 'CD8_noise', 'folded_tissue', 'ANY', 'annotation'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_processed" + ] + }, + { + "cell_type": "markdown", + "id": "24a5aa14", + "metadata": {}, + "source": [ + "### QuPath QC" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "482a0b18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelobjectTypegeometry
01detectionPOLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...
12detectionPOLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...
23detectionPOLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...
34detectionPOLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1...
45detectionPOLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ...
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" + ], + "text/plain": [ + " label objectType geometry\n", + "0 1 detection POLYGON ((60 43.5, 54 43.5, 46.5 39, 42.5 30, ...\n", + "1 2 detection POLYGON ((134 19.5, 129 19.5, 126 15.5, 119 11...\n", + "2 3 detection POLYGON ((167 31.5, 148 33.5, 142 30.5, 137.5 ...\n", + "3 4 detection POLYGON ((188 13.5, 178 13.5, 167 7.5, 160.5 1...\n", + "4 5 detection POLYGON ((235 48.5, 231 47.5, 220 39.5, 202.5 ..." + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf = gpd.read_file(\"outputs/segmentation_for_qupath.geojson\")\n", + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "893392ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filterDAPI_ratioDAPI_ratio_passAntibody_clumpsCD8_noisefolded_tissueANYannotation
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue0.065991FalseFalseFalseFalseFalseUnannotated
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue0.107462FalseFalseFalseFalseFalseUnannotated
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue0.098039FalseFalseFalseFalseFalseUnannotated
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue0.136228FalseFalseFalseFalseFalseUnannotated
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue0.104794FalseFalseFalseFalseFalseUnannotated
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" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength MinorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 36.841132 \n", + "1 2 6.598958 126.006944 576.0 45.835698 18.372329 \n", + "2 3 17.416667 156.656504 984.0 40.751104 31.700565 \n", + "3 4 4.982558 179.337209 344.0 34.620290 13.577757 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 49.053930 \n", + "\n", + " Eccentricity Orientation Extent Solidity Area_filter \\\n", + "0 0.644782 0.359469 146.669048 0.949178 True \n", + "1 0.916152 1.513685 113.112698 0.886154 True \n", + "2 0.628380 -1.528462 121.396970 0.955340 True \n", + "3 0.919884 1.474818 82.627417 0.971751 True \n", + "4 0.433912 1.287374 196.610173 0.896601 True \n", + "\n", + " mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass Antibody_clumps \\\n", + "0 True 0.065991 False False \n", + "1 True 0.107462 False False \n", + "2 True 0.098039 False False \n", + "3 True 0.136228 False False \n", + "4 True 0.104794 False False \n", + "\n", + " CD8_noise folded_tissue ANY annotation \n", + "0 False False False Unannotated \n", + "1 False False False Unannotated \n", + "2 False False False Unannotated \n", + "3 False False False Unannotated \n", + "4 False False False Unannotated " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "37bf07f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:09.23\u001b[0m | \u001b[1mINFO\u001b[0m | Found 15244 matching IDs between adata.obs['CellID'] and geodataframe['label'].\n" + ] + } + ], + "source": [ + "# check processed cells in qupath\n", + "cells = dvp.io.adata_to_qupath(\n", + " adata=adata_processed, \n", + " geodataframe=gdf,\n", + " adataobs_on=\"CellID\",\n", + " gdf_on=\"label\",\n", + " classify_by=None,\n", + " simplify_value=None,\n", + " save_as_detection=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "44441f39", + "metadata": {}, + "outputs": [], + "source": [ + "cells.to_file(\"outputs/filtered_cells.geojson\")" + ] + }, + { + "cell_type": "markdown", + "id": "82494daf", + "metadata": {}, + "source": [ + "### Napari QC not quite possible without spatialdata object, check tutorial #3" + ] + }, + { + "cell_type": "markdown", + "id": "3b4ba360", + "metadata": {}, + "source": [ + "## Phenotype my cells" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "388c2b6a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function impute_marker_with_annotation in module opendvp.pp.impute_marker_with_annotation:\n", + "\n", + "impute_marker_with_annotation(adata: anndata._core.anndata.AnnData, target_variable: str, target_annotation_column: str, quantile_for_imputation: float = 0.05) -> anndata._core.anndata.AnnData\n", + " Change value of a feature in an AnnData object for rows matching a specific annotation.\n", + " \n", + " Using a specified quantile value from the variable's distribution.\n", + " \n", + " Parameters:\n", + " ----------\n", + " adata : ad.AnnData\n", + " The annotated data matrix.\n", + " target_variable : str\n", + " The variable (gene/feature) to impute.\n", + " target_annotation_column : str\n", + " The column in adata.obs to use for selecting rows to impute.\n", + " quantile_for_imputation : float, optional\n", + " The quantile to use for imputation (default is 0.05).\n", + " \n", + " Returns:\n", + " -------\n", + " ad.AnnData\n", + " A copy of the AnnData object with imputed values.\n", + "\n" + ] + } + ], + "source": [ + "help(dvp.pp.impute_marker_with_annotation)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "a187eae5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidityArea_filtermean_DAPI_bg_filterDAPI_ratioDAPI_ratio_passAntibody_clumpsCD8_noisefolded_tissueANYannotation
0117.61259853.3370081270.048.19826936.8411320.6447820.359469146.6690480.949178TrueTrue0.065991FalseFalseFalseFalseFalseUnannotated
126.598958126.006944576.045.83569818.3723290.9161521.513685113.1126980.886154TrueTrue0.107462FalseFalseFalseFalseFalseUnannotated
2317.416667156.656504984.040.75110431.7005650.628380-1.528462121.3969700.955340TrueTrue0.098039FalseFalseFalseFalseFalseUnannotated
344.982558179.337209344.034.62029013.5777570.9198841.47481882.6274170.971751TrueTrue0.136228FalseFalseFalseFalseFalseUnannotated
4519.159558228.5982101899.054.44657849.0539300.4339121.287374196.6101730.896601TrueTrue0.104794FalseFalseFalseFalseFalseUnannotated
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16803168044993.579685106.373030571.052.29105014.7197190.959562-1.563557116.8700580.976068TrueTrue0.209647FalseFalseTrueFalseTrueCD8_noise
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637TrueTrue0.477926TrueFalseTrueFalseTrueCD8_noise
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923TrueTrue0.268536TrueFalseTrueFalseTrueCD8_noise
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856TrueTrue0.523596TrueFalseTrueFalseTrueCD8_noise
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756TrueTrue0.401436TrueFalseFalseFalseFalseUnannotated
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16808 rows × 19 columns

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" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", + "0 1 17.612598 53.337008 1270.0 48.198269 \n", + "1 2 6.598958 126.006944 576.0 45.835698 \n", + "2 3 17.416667 156.656504 984.0 40.751104 \n", + "3 4 4.982558 179.337209 344.0 34.620290 \n", + "4 5 19.159558 228.598210 1899.0 54.446578 \n", + "... ... ... ... ... ... \n", + "16803 16804 4993.579685 106.373030 571.0 52.291050 \n", + "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", + "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", + "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", + "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", + "\n", + " MinorAxisLength Eccentricity Orientation Extent Solidity \\\n", + "0 36.841132 0.644782 0.359469 146.669048 0.949178 \n", + "1 18.372329 0.916152 1.513685 113.112698 0.886154 \n", + "2 31.700565 0.628380 -1.528462 121.396970 0.955340 \n", + "3 13.577757 0.919884 1.474818 82.627417 0.971751 \n", + "4 49.053930 0.433912 1.287374 196.610173 0.896601 \n", + "... ... ... ... ... ... \n", + "16803 14.719719 0.959562 -1.563557 116.870058 0.976068 \n", + "16804 14.334812 0.933940 1.531591 90.970563 0.962637 \n", + "16805 13.112118 0.966202 1.538209 111.248737 0.951923 \n", + "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 \n", + "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 \n", + "\n", + " Area_filter mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass \\\n", + "0 True True 0.065991 False \n", + "1 True True 0.107462 False \n", + "2 True True 0.098039 False \n", + "3 True True 0.136228 False \n", + "4 True True 0.104794 False \n", + "... ... ... ... ... \n", + "16803 True True 0.209647 False \n", + "16804 True True 0.477926 True \n", + "16805 True True 0.268536 True \n", + "16806 True True 0.523596 True \n", + "16807 True True 0.401436 True \n", + "\n", + " Antibody_clumps CD8_noise folded_tissue ANY annotation \n", + "0 False False False False Unannotated \n", + "1 False False False False Unannotated \n", + "2 False False False False Unannotated \n", + "3 False False False False Unannotated \n", + "4 False False False False Unannotated \n", + "... ... ... ... ... ... \n", + "16803 False True False True CD8_noise \n", + "16804 False True False True CD8_noise \n", + "16805 False True False True CD8_noise \n", + "16806 False True False True CD8_noise \n", + "16807 False False False False Unannotated \n", + "\n", + "[16808 rows x 19 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "44deb355", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:09.64\u001b[0m | \u001b[1mINFO\u001b[0m | Imputing with 0.15% percentile value = 7.545454545454546\n" + ] + } + ], + "source": [ + "adata_CD8 = dvp.pp.impute_marker_with_annotation(\n", + " adata=adata_processed,\n", + " target_variable=\"mean_CD8\", \n", + " target_annotation_column=\"CD8_noise\",\n", + " quantile_for_imputation=0.15\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0da35a57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering out all rows with value 0.0 (assuming not gated)\n", + "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Found 8 valid gates\n", + "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Markers found: ['mean_Vimentin' 'mean_CD3e' 'mean_panCK' 'mean_CD8' 'mean_COL1A1'\n", + " 'mean_CD20' 'mean_CD68' 'mean_Ki67']\n", + "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Samples found: ['TD_15_TNBC_subset']\n", + "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Applying log1p transformation to gate values and formatting for scimap.\n", + "\u001b[32m15:14:09.66\u001b[0m | \u001b[1mINFO\u001b[0m | Output DataFrame columns: ['markers', 'TD_15_TNBC_subset']\n" + ] + }, + { + "data": { + "text/html": [ + "
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markersTD_15_TNBC_subset
5mean_Vimentin1.915886
6mean_CD3e2.138796
7mean_panCK1.287972
8mean_CD82.890372
10mean_COL1A13.169333
11mean_CD203.205329
12mean_CD681.436576
13mean_Ki671.093354
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" + ], + "text/plain": [ + " markers TD_15_TNBC_subset\n", + "5 mean_Vimentin 1.915886\n", + "6 mean_CD3e 2.138796\n", + "7 mean_panCK 1.287972\n", + "8 mean_CD8 2.890372\n", + "10 mean_COL1A1 3.169333\n", + "11 mean_CD20 3.205329\n", + "12 mean_CD68 1.436576\n", + "13 mean_Ki67 1.093354" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gates = dvp.io.import_thresholds(gates_csv_path=\"data/phenotyping/gates.csv\")\n", + "gates" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "014fc45a", + "metadata": {}, + "outputs": [], + "source": [ + "adata_phenotyping = adata_CD8[:,adata_CD8.var_names.isin(gates['markers'])].copy()\n", + "adata_phenotyping.obs['sample_id'] = \"TD_15_TNBC_subset\"" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7fb8aa73", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Scaling Image: TD_15_TNBC_subset\n", + "Scaling mean_Vimentin (gate: 1.916)\n", + "Scaling mean_CD3e (gate: 2.139)\n", + "Scaling mean_panCK (gate: 1.288)\n", + "Scaling mean_CD8 (gate: 2.890)\n", + "Scaling mean_COL1A1 (gate: 3.169)\n", + "Scaling mean_CD20 (gate: 3.205)\n", + "Scaling mean_CD68 (gate: 1.437)\n", + "Scaling mean_Ki67 (gate: 1.093)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/src/opendvp/pp/rescale.py:145: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " gate_mapping['gate'] = gate_mapping['gate'].fillna(gate_mapping['m_gate'])\n" + ] + } + ], + "source": [ + "# seems that I will have to:\n", + "# create adata for gating by filtering unused columns\n", + "adata_rescaled = dvp.pp.rescale(\n", + " adata=adata_phenotyping,\n", + " gate=gates,\n", + " method=\"all\",\n", + " imageid=\"sample_id\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "fd0f28f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 Unnamed: 0Unnamed: 1VimentinCD3epanCKCD8COL1A1CD20CD68Ki67
0allEpithelialpos
1allMesenchymalpos
2allImmuneanyposanyposanyposanypos
3allFibroblastspos
4ImmuneCD4_T_cellposneg
5ImmuneCD8_T_cellpos
6ImmuneB_cellpos
7ImmuneMacrophagepos
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the phenotyping workflow\n", + "phenotype = pd.read_csv('data/phenotyping/celltype_matrix.csv')\n", + "phenotype.style.format(na_rep='')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "98276e6e", + "metadata": {}, + "outputs": [], + "source": [ + "adata_phenotyping.var['feature_name'] = [name.split(\"_\")[1] for name in adata_phenotyping.var_names]\n", + "adata_phenotyping.var.index = adata_phenotyping.var['feature_name'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "2fff0b1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phenotyping Epithelial\n", + "Phenotyping Mesenchymal\n", + "Phenotyping Immune\n", + "Phenotyping Fibroblasts\n", + "-- Subsetting Immune\n", + "Phenotyping CD4_T_cell\n", + "Phenotyping CD8_T_cell\n", + "Phenotyping B_cell\n", + "Phenotyping Macrophage\n", + "Consolidating the phenotypes across all groups\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/src/opendvp/tl/phenotype_cells.py:290: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n", + " phenotype_labels_Consolidated = phenotype_labels.fillna(method='ffill', axis = 1)\n", + "/Users/jnimoca/Jose_BI/1_Pipelines/openDVP/src/opendvp/tl/phenotype_cells.py:290: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " phenotype_labels_Consolidated = phenotype_labels.fillna(method='ffill', axis = 1)\n" + ] + } + ], + "source": [ + "adata_phenotyped = dvp.tl.phenotype_cells(\n", + " adata_phenotyping, \n", + " phenotype=phenotype, \n", + " label=\"phenotype\",\n", + " verbose=True) " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "6d99c71b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CellIDY_centroidX_centroidAreaMajorAxisLengthMinorAxisLengthEccentricityOrientationExtentSolidity...mean_DAPI_bg_filterDAPI_ratioDAPI_ratio_passAntibody_clumpsCD8_noisefolded_tissueANYannotationsample_idphenotype
6728.019009321.5729801473.057.99037334.0568460.8093810.702143160.3675320.943022...True0.503327TrueFalseFalseFalseFalseUnannotatedTD_15_TNBC_subsetEpithelial
7813.004021351.3183651492.060.42229235.4850980.8093801.459721167.9177850.965071...True0.563896TrueFalseFalseFalseFalseUnannotatedTD_15_TNBC_subsetEpithelial
898.195719415.693170981.062.48946720.8546570.942668-1.541497144.7695530.974181...True0.473116TrueFalseFalseFalseFalseUnannotatedTD_15_TNBC_subsetUnknown
9109.020833482.729167576.034.66003821.7473140.778660-1.55604593.6984850.963211...True0.526049TrueFalseFalseFalseFalseUnannotatedTD_15_TNBC_subsetCD4_T_cell
101111.670357554.346863813.036.50688329.5312640.587914-1.567206109.3553390.975990...True0.649908TrueFalseFalseFalseFalseUnannotatedTD_15_TNBC_subsetUnknown
..................................................................
16801168024992.956322561.156322435.036.42391415.8101870.900884-1.56187987.7989900.966667...True0.461031TrueFalseTrueFalseTrueCD8_noiseTD_15_TNBC_subsetFibroblasts
16804168054993.413242810.385845438.040.10524714.3348120.9339401.53159190.9705630.962637...True0.477926TrueFalseTrueFalseTrueCD8_noiseTD_15_TNBC_subsetUnknown
16805168064994.153535645.010101495.050.86413513.1121180.9662021.538209111.2487370.951923...True0.268536TrueFalseTrueFalseTrueCD8_noiseTD_15_TNBC_subsetFibroblasts
16806168074993.8355701244.718121298.028.94782113.4951260.884686-1.56180669.2132030.973856...True0.523596TrueFalseTrueFalseTrueCD8_noiseTD_15_TNBC_subsetUnknown
16807168084994.2507744186.876161323.033.32531613.0271770.920429-1.56463877.2132030.984756...True0.401436TrueFalseFalseFalseFalseUnannotatedTD_15_TNBC_subsetUnknown
\n", + "

15244 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " CellID Y_centroid X_centroid Area MajorAxisLength \\\n", + "6 7 28.019009 321.572980 1473.0 57.990373 \n", + "7 8 13.004021 351.318365 1492.0 60.422292 \n", + "8 9 8.195719 415.693170 981.0 62.489467 \n", + "9 10 9.020833 482.729167 576.0 34.660038 \n", + "10 11 11.670357 554.346863 813.0 36.506883 \n", + "... ... ... ... ... ... \n", + "16801 16802 4992.956322 561.156322 435.0 36.423914 \n", + "16804 16805 4993.413242 810.385845 438.0 40.105247 \n", + "16805 16806 4994.153535 645.010101 495.0 50.864135 \n", + "16806 16807 4993.835570 1244.718121 298.0 28.947821 \n", + "16807 16808 4994.250774 4186.876161 323.0 33.325316 \n", + "\n", + " MinorAxisLength Eccentricity Orientation Extent Solidity ... \\\n", + "6 34.056846 0.809381 0.702143 160.367532 0.943022 ... \n", + "7 35.485098 0.809380 1.459721 167.917785 0.965071 ... \n", + "8 20.854657 0.942668 -1.541497 144.769553 0.974181 ... \n", + "9 21.747314 0.778660 -1.556045 93.698485 0.963211 ... \n", + "10 29.531264 0.587914 -1.567206 109.355339 0.975990 ... \n", + "... ... ... ... ... ... ... \n", + "16801 15.810187 0.900884 -1.561879 87.798990 0.966667 ... \n", + "16804 14.334812 0.933940 1.531591 90.970563 0.962637 ... \n", + "16805 13.112118 0.966202 1.538209 111.248737 0.951923 ... \n", + "16806 13.495126 0.884686 -1.561806 69.213203 0.973856 ... \n", + "16807 13.027177 0.920429 -1.564638 77.213203 0.984756 ... \n", + "\n", + " mean_DAPI_bg_filter DAPI_ratio DAPI_ratio_pass Antibody_clumps \\\n", + "6 True 0.503327 True False \n", + "7 True 0.563896 True False \n", + "8 True 0.473116 True False \n", + "9 True 0.526049 True False \n", + "10 True 0.649908 True False \n", + "... ... ... ... ... \n", + "16801 True 0.461031 True False \n", + "16804 True 0.477926 True False \n", + "16805 True 0.268536 True False \n", + "16806 True 0.523596 True False \n", + "16807 True 0.401436 True False \n", + "\n", + " CD8_noise folded_tissue ANY annotation sample_id \\\n", + "6 False False False Unannotated TD_15_TNBC_subset \n", + "7 False False False Unannotated TD_15_TNBC_subset \n", + "8 False False False Unannotated TD_15_TNBC_subset \n", + "9 False False False Unannotated TD_15_TNBC_subset \n", + "10 False False False Unannotated TD_15_TNBC_subset \n", + "... ... ... ... ... ... \n", + "16801 True False True CD8_noise TD_15_TNBC_subset \n", + "16804 True False True CD8_noise TD_15_TNBC_subset \n", + "16805 True False True CD8_noise TD_15_TNBC_subset \n", + "16806 True False True CD8_noise TD_15_TNBC_subset \n", + "16807 False False False Unannotated TD_15_TNBC_subset \n", + "\n", + " phenotype \n", + "6 Epithelial \n", + "7 Epithelial \n", + "8 Unknown \n", + "9 CD4_T_cell \n", + "10 Unknown \n", + "... ... \n", + "16801 Fibroblasts \n", + "16804 Unknown \n", + "16805 Fibroblasts \n", + "16806 Unknown \n", + "16807 Unknown \n", + "\n", + "[15244 rows x 21 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_phenotyped.obs" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "0e65afa5", + "metadata": {}, + "outputs": [], + "source": [ + "adata = adata_CD8.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "3ac68f44", + "metadata": {}, + "outputs": [], + "source": [ + "adata.obs = adata_phenotyped.obs.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "beb01d33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 15244 × 15\n", + " obs: 'CellID', 'Y_centroid', 'X_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Orientation', 'Extent', 'Solidity', 'Area_filter', 'mean_DAPI_bg_filter', 'DAPI_ratio', 'DAPI_ratio_pass', 'Antibody_clumps', 'CD8_noise', 'folded_tissue', 'ANY', 'annotation', 'sample_id', 'phenotype'" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata" + ] + }, + { + "cell_type": "markdown", + "id": "48834a21", + "metadata": {}, + "source": [ + "### QC phenotypes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05cc765d", + "metadata": {}, + "outputs": [], + "source": [ + "#Qupath shapes" + ] + }, + { + "cell_type": "markdown", + "id": "3a27987d", + "metadata": {}, + "source": [ + "## Cellular neighborhoods" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1864054", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "562caad8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "opendvp_tut", + "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/docs/Tutorials/old/T2_ProteomicsIntegration.ipynb b/docs/Tutorials/old/T2_ProteomicsIntegration.ipynb new file mode 100644 index 0000000..cee0d6c --- /dev/null +++ b/docs/Tutorials/old/T2_ProteomicsIntegration.ipynb @@ -0,0 +1,1292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a44cbb39", + "metadata": {}, + "source": [ + "# Integrating proteomics and imaging data" + ] + }, + { + "cell_type": "markdown", + "id": "e07c2e72", + "metadata": {}, + "source": [ + "### Import necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3c56f7e9", + "metadata": {}, + "outputs": [], + "source": [ + "import opendvp as dvp\n", + "import geopandas as gpd\n", + "import ast\n", + "import spatialdata\n", + "import napari_spatialdata\n", + "from dask_image import imread" + ] + }, + { + "cell_type": "markdown", + "id": "e8556899", + "metadata": {}, + "source": [ + "### Load DIANN data into an adata object" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "565044a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m13:50:10.86\u001b[0m | \u001b[1mINFO\u001b[0m | Starting DIANN matrix shape (7030, 14)\n", + "\u001b[32m13:50:10.87\u001b[0m | \u001b[1mINFO\u001b[0m | Removing 264 contaminants\n", + "\u001b[32m13:50:10.87\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering 3 genes that are NaN\n", + "\u001b[32m13:50:10.87\u001b[0m | \u001b[1mINFO\u001b[0m | ['A0A0G2JRQ6_HUMAN', 'A0A0J9YY99_HUMAN', 'YJ005_HUMAN']\n", + "\u001b[32m13:50:10.87\u001b[0m | \u001b[1mINFO\u001b[0m | 10 samples, and 6763 proteins\n", + "\u001b[32m13:50:10.88\u001b[0m | \u001b[1mINFO\u001b[0m | 52 gene lists (eg 'TMA7;TMA7B') were simplified to ('TMA7').\n", + "\u001b[32m13:50:10.88\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | Anndata object has been created :) \n" + ] + }, + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 10 × 6763\n", + " obs: 'Precursors.Identified', 'Proteins.Identified', 'Average.Missed.Tryptic.Cleavages', 'LCMS_run_id', 'RCN', 'RCN_long', 'QuPath_class'\n", + " var: 'Protein.Group', 'Protein.Names', 'Genes', 'First.Protein.Description'" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata = dvp.io.DIANN_to_adata(\n", + " DIANN_path=\"data/proteomics/DIANN_subset_demo.pg_matrix.csv\",\n", + " DIANN_sep=\"\\t\",\n", + " metadata_path=\"data/proteomics/DIANN_metadata_subset_demo.csv\",\n", + " metadata_sep=\";\",\n", + " n_of_protein_metadata_cols=4\n", + ")\n", + "adata" + ] + }, + { + "cell_type": "markdown", + "id": "17be62f7", + "metadata": {}, + "source": [ + "Let's see the loaded metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "da3c770e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Precursors.IdentifiedProteins.IdentifiedAverage.Missed.Tryptic.CleavagesLCMS_run_idRCNRCN_longQuPath_class
Sample_filepath
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241204_JoN_Evo2_80SPD_Rplate_P26E17_K20_S6-G4_1_8674.d2635847600.1398674RCN1Tumor enrichedP12_Tumor_1
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_H21_S3-F9_1_8452.d2470545530.1348452RCN1Tumor enrichedP12_Tumor_2
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_E4_S2-C2_1_8414.d2675048350.1348414RCN1Tumor enrichedP12_Tumor_3
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241204_JoN_Evo2_80SPD_Rplate_P26E17_L10_S4-H8_1_8551.d2426845020.1168551RCN1Tumor enrichedP12_Tumor_4
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_F4_S2-D2_1_8424.d2788348580.1368424RCN1Tumor enrichedP12_Tumor_5
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_C22_S3-A10_1_8521.d2046041160.1298521RCN3Immune enrichedP12_Immune_1
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_D4_S2-B2_1_8404.d2044639550.1298404RCN3Immune enrichedP12_Immune_2
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_D3_S2-B1_1_8403.d2107742120.1278403RCN3Immune enrichedP12_Immune_3
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_C10_S2-A8_1_8390.d1801337490.1178390RCN3Immune enrichedP12_Immune_4
E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_20241129_JoN_Evo2_80SPD_P26E17_991_C23_S3-A11_1_8520.d1974037990.1238520RCN3Immune enrichedP12_Immune_5
\n", + "
" + ], + "text/plain": [ + " Precursors.Identified \\\n", + "Sample_filepath \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 26358 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 24705 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 26750 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 24268 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 27883 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 20460 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 20446 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 21077 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 18013 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 19740 \n", + "\n", + " Proteins.Identified \\\n", + "Sample_filepath \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 4760 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 4553 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 4835 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 4502 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 4858 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 4116 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 3955 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 4212 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 3749 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 3799 \n", + "\n", + " Average.Missed.Tryptic.Cleavages \\\n", + "Sample_filepath \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 0.139 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.134 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.134 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 0.116 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.136 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.129 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.129 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.127 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.117 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 0.123 \n", + "\n", + " LCMS_run_id RCN \\\n", + "Sample_filepath \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 8674 RCN1 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8452 RCN1 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8414 RCN1 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... 8551 RCN1 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8424 RCN1 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8521 RCN3 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8404 RCN3 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8403 RCN3 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8390 RCN3 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... 8520 RCN3 \n", + "\n", + " RCN_long \\\n", + "Sample_filepath \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... Tumor enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Tumor enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Tumor enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... Tumor enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Tumor enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Immune enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Immune enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Immune enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Immune enriched \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... Immune enriched \n", + "\n", + " QuPath_class \n", + "Sample_filepath \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... P12_Tumor_1 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Tumor_2 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Tumor_3 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202412... P12_Tumor_4 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Tumor_5 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Immune_1 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Immune_2 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Immune_3 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Immune_4 \n", + "E:\\Proteomics\\Jose\\P26E17\\rawfiles\\Olive_202411... P12_Immune_5 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata.obs" + ] + }, + { + "cell_type": "markdown", + "id": "d7129b8f", + "metadata": {}, + "source": [ + "### Export adata for transparency" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11008fb6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m12:52:16.33\u001b[0m | \u001b[1mINFO\u001b[0m | Writing h5ad\n", + "\u001b[32m12:52:16.37\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | Wrote h5ad file\n" + ] + } + ], + "source": [ + "dvp.io.export_adata(adata=adata, path_to_dir=\"data/checkpoints\", checkpoint_name=\"1_loaded\")" + ] + }, + { + "cell_type": "markdown", + "id": "4d73cbff", + "metadata": {}, + "source": [ + "### Load shapes of cut samples" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "899bc996", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobjectTypeclassificationgeometry
078422794-3ed4-4e09-a9aa-6486c467149dannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ...
170a57ffd-50a0-4449-b873-2dd2a38bf190annotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ...
2edfda1ca-8de8-44f5-af4e-f16e15bcd4caannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ...
3a0a868c6-c7e3-4fb0-8f4f-071454565080annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8...
4ce97d787-321e-4b7d-97e7-55b203fb0373annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2...
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" + ], + "text/plain": [ + " id objectType \\\n", + "0 78422794-3ed4-4e09-a9aa-6486c467149d annotation \n", + "1 70a57ffd-50a0-4449-b873-2dd2a38bf190 annotation \n", + "2 edfda1ca-8de8-44f5-af4e-f16e15bcd4ca annotation \n", + "3 a0a868c6-c7e3-4fb0-8f4f-071454565080 annotation \n", + "4 ce97d787-321e-4b7d-97e7-55b203fb0373 annotation \n", + "\n", + " classification \\\n", + "0 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "1 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "2 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "3 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "4 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "\n", + " geometry \n", + "0 POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ... \n", + "1 POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ... \n", + "2 POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ... \n", + "3 POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8... \n", + "4 POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2... " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf = gpd.read_file(\"data/proteomics/collection_shapes.geojson\")\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "356e22ea", + "metadata": {}, + "source": [ + "### Create spatialdata object" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "112b915c", + "metadata": {}, + "outputs": [], + "source": [ + "sdata = spatialdata.SpatialData()" + ] + }, + { + "cell_type": "markdown", + "id": "8516c0be", + "metadata": {}, + "source": [ + "### Load multiplex immunofluorescence into spatialdata object" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0683a726", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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Array Chunk
Bytes 357.63 MiB 23.84 MiB
Shape (15, 5000, 5000) (1, 5000, 5000)
Dask graph 15 chunks in 4 graph layers
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" + ], + "text/plain": [ + "dask.array<_map_read_frame, shape=(15, 5000, 5000), dtype=uint8, chunksize=(1, 5000, 5000), chunktype=numpy.ndarray>" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# first load as array using dask-image\n", + "image_array = imread.imread(\"data/image/mIF.ome.tif\")\n", + "image_array" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4102accf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mINFO \u001b[0m no axes information specified in the object, setting `dims` to: \u001b[1m(\u001b[0m\u001b[32m'c'\u001b[0m, \u001b[32m'y'\u001b[0m, \u001b[32m'x'\u001b[0m\u001b[1m)\u001b[0m \n" + ] + } + ], + "source": [ + "# load image to spatialdata object\n", + "sdata['mIF'] = spatialdata.models.Image2DModel.parse(image_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1e6dd165", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SpatialData object\n", + "└── Images\n", + " └── 'mIF': DataArray[cyx] (15, 5000, 5000)\n", + "with coordinate systems:\n", + " ▸ 'global', with elements:\n", + " mIF (Images)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata" + ] + }, + { + "cell_type": "markdown", + "id": "fdca3ed9", + "metadata": {}, + "source": [ + "### Load proteomics matrix (adata object) to spatialdata object" + ] + }, + { + "cell_type": "markdown", + "id": "c890906f", + "metadata": {}, + "source": [ + "First we must label the matrix, to let spatialdata know which coordinate system to use. \n", + "In this case, this means labelling which slide it was." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4f4c977c", + "metadata": {}, + "outputs": [], + "source": [ + "adata.obs[\"Slide_id\"] = \"Slide_P12\"" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "5e2db2d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 10 × 6763\n", + " obs: 'Precursors.Identified', 'Proteins.Identified', 'Average.Missed.Tryptic.Cleavages', 'LCMS_run_id', 'RCN', 'RCN_long', 'QuPath_class', 'Slide_id'\n", + " var: 'Protein.Group', 'Protein.Names', 'Genes', 'First.Protein.Description'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata" + ] + }, + { + "cell_type": "markdown", + "id": "11aa76da", + "metadata": {}, + "source": [ + "Now we will pass the matrix to spatialdata. \n", + "We need to tell spatialdata what parts of the adata object to use for visualization. \n", + "`region` is which set of shapes to project these data into, in this case `Slide_P12`. \n", + "`region_key` is which column in adata.obs to use for finding the `region` parameter. \n", + "`instance_key` refers to the column that links this adata object to the shapes. \n", + "We use `QuPath_class` because that is the column that matches between the adata and the geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8f7ca2b8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/spatialdata/models/models.py:1144: UserWarning: Converting `region_key: Slide_id` to categorical dtype.\n", + " return convert_region_column_to_categorical(adata)\n" + ] + } + ], + "source": [ + "sdata['proteomics'] = spatialdata.models.TableModel.parse(\n", + " adata=adata, \n", + " region=\"Slide_P12\",\n", + " region_key=\"Slide_id\",\n", + " instance_key=\"QuPath_class\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "92e32aa6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SpatialData object\n", + "├── Images\n", + "│ └── 'mIF': DataArray[cyx] (15, 5000, 5000)\n", + "└── Tables\n", + " └── 'proteomics': AnnData (10, 6763)\n", + "with coordinate systems:\n", + " ▸ 'global', with elements:\n", + " mIF (Images)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata" + ] + }, + { + "cell_type": "markdown", + "id": "a2588fab", + "metadata": {}, + "source": [ + "### Load the geodataframe with shapes that match proteomic samples" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "aea2e4e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobjectTypeclassificationgeometry
078422794-3ed4-4e09-a9aa-6486c467149dannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ...
170a57ffd-50a0-4449-b873-2dd2a38bf190annotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ...
2edfda1ca-8de8-44f5-af4e-f16e15bcd4caannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ...
3a0a868c6-c7e3-4fb0-8f4f-071454565080annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8...
4ce97d787-321e-4b7d-97e7-55b203fb0373annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2...
\n", + "
" + ], + "text/plain": [ + " id objectType \\\n", + "0 78422794-3ed4-4e09-a9aa-6486c467149d annotation \n", + "1 70a57ffd-50a0-4449-b873-2dd2a38bf190 annotation \n", + "2 edfda1ca-8de8-44f5-af4e-f16e15bcd4ca annotation \n", + "3 a0a868c6-c7e3-4fb0-8f4f-071454565080 annotation \n", + "4 ce97d787-321e-4b7d-97e7-55b203fb0373 annotation \n", + "\n", + " classification \\\n", + "0 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "1 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "2 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "3 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "4 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "\n", + " geometry \n", + "0 POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ... \n", + "1 POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ... \n", + "2 POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ... \n", + "3 POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8... \n", + "4 POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2... " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "ba8928f5", + "metadata": {}, + "source": [ + "Here we see that the QuPath class name is inside that classification column. \n", + "Let's get it out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c88f97a1", + "metadata": {}, + "outputs": [], + "source": [ + "gdf['QuPath_class'] = gdf['classification'].apply(\n", + " lambda row: ast.literal_eval(row).get('name') if isinstance(row,str) else row.get('name'))\n", + "\n", + "# this line of code says, if classification cells are strings, \n", + "# convert to a dictionary and get name attribute, \n", + "# otherwise we assume it is already a dictionary and we get the name attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ea31d7d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobjectTypeclassificationgeometryQuPath_class
078422794-3ed4-4e09-a9aa-6486c467149dannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ...P12_Immune_3
170a57ffd-50a0-4449-b873-2dd2a38bf190annotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ...P12_Immune_3
2edfda1ca-8de8-44f5-af4e-f16e15bcd4caannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ...P12_Immune_3
3a0a868c6-c7e3-4fb0-8f4f-071454565080annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8...P12_Immune_4
4ce97d787-321e-4b7d-97e7-55b203fb0373annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2...P12_Immune_4
\n", + "
" + ], + "text/plain": [ + " id objectType \\\n", + "0 78422794-3ed4-4e09-a9aa-6486c467149d annotation \n", + "1 70a57ffd-50a0-4449-b873-2dd2a38bf190 annotation \n", + "2 edfda1ca-8de8-44f5-af4e-f16e15bcd4ca annotation \n", + "3 a0a868c6-c7e3-4fb0-8f4f-071454565080 annotation \n", + "4 ce97d787-321e-4b7d-97e7-55b203fb0373 annotation \n", + "\n", + " classification \\\n", + "0 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "1 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "2 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "3 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "4 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "\n", + " geometry QuPath_class \n", + "0 POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ... P12_Immune_3 \n", + "1 POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ... P12_Immune_3 \n", + "2 POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ... P12_Immune_3 \n", + "3 POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8... P12_Immune_4 \n", + "4 POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2... P12_Immune_4 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "0cae66dc", + "metadata": {}, + "source": [ + "Now we see that the `QuPath_class` column has our matching names. \n", + "One last thing we must do, is set these names as the index of the geodataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "cde5ba46", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idobjectTypeclassificationgeometry
QuPath_class
P12_Immune_378422794-3ed4-4e09-a9aa-6486c467149dannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ...
P12_Immune_370a57ffd-50a0-4449-b873-2dd2a38bf190annotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ...
P12_Immune_3edfda1ca-8de8-44f5-af4e-f16e15bcd4caannotation{ \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2...POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ...
P12_Immune_4a0a868c6-c7e3-4fb0-8f4f-071454565080annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8...
P12_Immune_4ce97d787-321e-4b7d-97e7-55b203fb0373annotation{ \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2...POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2...
\n", + "
" + ], + "text/plain": [ + " id objectType \\\n", + "QuPath_class \n", + "P12_Immune_3 78422794-3ed4-4e09-a9aa-6486c467149d annotation \n", + "P12_Immune_3 70a57ffd-50a0-4449-b873-2dd2a38bf190 annotation \n", + "P12_Immune_3 edfda1ca-8de8-44f5-af4e-f16e15bcd4ca annotation \n", + "P12_Immune_4 a0a868c6-c7e3-4fb0-8f4f-071454565080 annotation \n", + "P12_Immune_4 ce97d787-321e-4b7d-97e7-55b203fb0373 annotation \n", + "\n", + " classification \\\n", + "QuPath_class \n", + "P12_Immune_3 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "P12_Immune_3 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "P12_Immune_3 { \"name\": \"P12_Immune_3\", \"color\": [ 0, 255, 2... \n", + "P12_Immune_4 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "P12_Immune_4 { \"name\": \"P12_Immune_4\", \"color\": [ 0, 255, 2... \n", + "\n", + " geometry \n", + "QuPath_class \n", + "P12_Immune_3 POLYGON ((3417 955.5, 3417.23 962.94, 3417.93 ... \n", + "P12_Immune_3 POLYGON ((3547 715.5, 3547.23 722.94, 3547.93 ... \n", + "P12_Immune_3 POLYGON ((3324 568.5, 3324.23 575.94, 3324.93 ... \n", + "P12_Immune_4 POLYGON ((4398 2191.5, 4398.21 2198.06, 4398.8... \n", + "P12_Immune_4 POLYGON ((4173 2349, 4173.22 2356.16, 4173.9 2... " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf = gdf.set_index(\"QuPath_class\")\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "cf4a89fe", + "metadata": {}, + "source": [ + "Now we see that `QuPath_class` is the index. \n", + "Spatialdata needs this to match the proteomics adata object to these shapes. \n", + "Let's add these prepared shapes to the spatialdata object." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "8bfdfebe", + "metadata": {}, + "outputs": [], + "source": [ + "sdata['Slide_P12'] = spatialdata.models.ShapesModel.parse(gdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6cad5db4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SpatialData object\n", + "├── Images\n", + "│ └── 'mIF': DataArray[cyx] (15, 5000, 5000)\n", + "├── Shapes\n", + "│ └── 'Slide_P12': GeoDataFrame shape: (20, 4) (2D shapes)\n", + "└── Tables\n", + " └── 'proteomics': AnnData (10, 6763)\n", + "with coordinate systems:\n", + " ▸ 'global', with elements:\n", + " mIF (Images), Slide_P12 (Shapes)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata" + ] + }, + { + "cell_type": "markdown", + "id": "ac8fe6c9", + "metadata": {}, + "source": [ + "### Visualize data interactively" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "9fe9e307", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "napari_spatialdata.Interactive(sdata)" + ] + }, + { + "cell_type": "markdown", + "id": "f6946404", + "metadata": {}, + "source": [ + "### Write spatialdata object for future use" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "be59d307", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mINFO \u001b[0m The Zarr backing store has been changed from \u001b[3;35mNone\u001b[0m the new file path: outputs/spatialdata.zarr \n" + ] + } + ], + "source": [ + "sdata.write(\"outputs/spatialdata.zarr\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "opendvp_tut", + "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/docs/Tutorials/old/T3_DownstreamProteomics.ipynb b/docs/Tutorials/old/T3_DownstreamProteomics.ipynb new file mode 100644 index 0000000..f36c94b --- /dev/null +++ b/docs/Tutorials/old/T3_DownstreamProteomics.ipynb @@ -0,0 +1,1099 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5f42b271", + "metadata": {}, + "source": [ + "# Proccessing of proteomic data" + ] + }, + { + "cell_type": "markdown", + "id": "47f7141e", + "metadata": {}, + "source": [ + "### Package import " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "912f7995", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import opendvp as dvp\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import anndata as ad\n", + "import numpy as np\n", + "import scanpy as sc\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "d3df1564", + "metadata": {}, + "source": [ + "### Load adata object" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f961332b", + "metadata": {}, + "outputs": [], + "source": [ + "adata = ad.read_h5ad(\"data/checkpoints/1_loaded/20250701_1252_1_loaded_adata.h5ad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1078829c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 10 × 6763\n", + " obs: 'Precursors.Identified', 'Proteins.Identified', 'Average.Missed.Tryptic.Cleavages', 'LCMS_run_id', 'RCN', 'RCN_long', 'QuPath_class'\n", + " var: 'Protein.Group', 'Protein.Names', 'Genes', 'First.Protein.Description'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata" + ] + }, + { + "cell_type": "markdown", + "id": "aeb0b2a6", + "metadata": {}, + "source": [ + "### First step, describe the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e5baf04a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dvp.plotting.dual_axis_boxplots(adata_obs=adata.obs, feature_key=\"RCN\")" + ] + }, + { + "cell_type": "markdown", + "id": "d99a0fc8", + "metadata": {}, + "source": [ + "### Let's plot some data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "24d0f71c", + "metadata": {}, + "outputs": [], + "source": [ + "# Log2 transform the data\n", + "adata.X = np.log2(adata.X)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e2a64dbc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dvp.plotting.density(adata=adata, color_by=\"RCN\")\n", + "dvp.plotting.density(adata=adata, color_by=\"QuPath_class\")" + ] + }, + { + "cell_type": "markdown", + "id": "c19e322b", + "metadata": {}, + "source": [ + "## Filter dataset by NaNs" + ] + }, + { + "cell_type": "markdown", + "id": "ba29cdf2", + "metadata": {}, + "source": [ + "We expect a lot of proteins to be removed because this is a subsampled dataset. \n", + "Many protein hits were present in other groups not present here." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4835b673", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:17:07.39\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering protein with at least 70.0% valid values in ANY group\n", + "\u001b[32m15:17:07.39\u001b[0m | \u001b[1mINFO\u001b[0m | Calculating overall QC metrics for all features.\n", + "\u001b[32m15:17:07.40\u001b[0m | \u001b[1mINFO\u001b[0m | Filtering by groups, RCN: ['RCN1', 'RCN3']\n", + "\u001b[32m15:17:07.40\u001b[0m | \u001b[1mINFO\u001b[0m | RCN1 has 5 samples\n", + "\u001b[32m15:17:07.40\u001b[0m | \u001b[1mINFO\u001b[0m | RCN3 has 5 samples\n", + "\u001b[32m15:17:07.41\u001b[0m | \u001b[1mINFO\u001b[0m | Keeping proteins that pass 'ANY' group criteria.\n", + "\u001b[32m15:17:07.41\u001b[0m | \u001b[1mINFO\u001b[0m | Complete QC metrics for all initial features stored in `adata.uns['filter_features_byNaNs_qc_metrics']`.\n", + "\u001b[32m15:17:07.41\u001b[0m | \u001b[1mINFO\u001b[0m | 4637 proteins were kept.\n", + "\u001b[32m15:17:07.41\u001b[0m | \u001b[1mINFO\u001b[0m | 2126 proteins were removed.\n", + "\u001b[32m15:17:07.41\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | filter_features_byNaNs complete.\n" + ] + } + ], + "source": [ + "adata_filtered = dvp.tl.filter_features_byNaNs(adata=adata, threshold=0.7, grouping=\"RCN\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "846b854e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Protein.GroupProtein.NamesGenesFirst.Protein.Descriptionoverall_meanoverall_nan_countoverall_valid_countoverall_nan_proportionsoverall_validRCN1_mean...RCN1_nan_proportionsRCN1_validRCN3_meanRCN3_nan_countRCN3_valid_countRCN3_nan_proportionsRCN3_validvalid_in_all_groupsvalid_in_any_groupnot_valid_in_any_group
Gene
TMA7A0A024R1R8;Q9Y2S6TMA7B_HUMAN;TMA7_HUMANTMA7;TMA7BTranslation machinery-associated protein 7B13.391550.5False13.029...0.4False13.933320.6FalseFalseFalseTrue
IGLV8-61A0A075B6I0LV861_HUMANIGLV8-61Immunoglobulin lambda variable 8-6113.666640.6False12.395...0.8False14.090230.4FalseFalseFalseTrue
IGLV3-10A0A075B6K4LV310_HUMANIGLV3-10Immunoglobulin lambda variable 3-1014.216640.6False13.335...0.6False15.097320.6FalseFalseFalseTrue
IGLV3-9A0A075B6K5LV39_HUMANIGLV3-9Immunoglobulin lambda variable 3-915.2930100.0True13.680...0.0True16.906050.0TrueTrueTrueFalse
IGKV2-28A0A075B6P5;P01615KV228_HUMAN;KVD28_HUMANIGKV2-28;IGKV2D-28Immunoglobulin kappa variable 2-2815.343190.1True14.014...0.2True16.407050.0TrueTrueTrueFalse
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5 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " Protein.Group Protein.Names Genes \\\n", + "Gene \n", + "TMA7 A0A024R1R8;Q9Y2S6 TMA7B_HUMAN;TMA7_HUMAN TMA7;TMA7B \n", + "IGLV8-61 A0A075B6I0 LV861_HUMAN IGLV8-61 \n", + "IGLV3-10 A0A075B6K4 LV310_HUMAN IGLV3-10 \n", + "IGLV3-9 A0A075B6K5 LV39_HUMAN IGLV3-9 \n", + "IGKV2-28 A0A075B6P5;P01615 KV228_HUMAN;KVD28_HUMAN IGKV2-28;IGKV2D-28 \n", + "\n", + " First.Protein.Description overall_mean \\\n", + "Gene \n", + "TMA7 Translation machinery-associated protein 7B 13.391 \n", + "IGLV8-61 Immunoglobulin lambda variable 8-61 13.666 \n", + "IGLV3-10 Immunoglobulin lambda variable 3-10 14.216 \n", + "IGLV3-9 Immunoglobulin lambda variable 3-9 15.293 \n", + "IGKV2-28 Immunoglobulin kappa variable 2-28 15.343 \n", + "\n", + " overall_nan_count overall_valid_count overall_nan_proportions \\\n", + "Gene \n", + "TMA7 5 5 0.5 \n", + "IGLV8-61 6 4 0.6 \n", + "IGLV3-10 6 4 0.6 \n", + "IGLV3-9 0 10 0.0 \n", + "IGKV2-28 1 9 0.1 \n", + "\n", + " overall_valid RCN1_mean ... RCN1_nan_proportions RCN1_valid \\\n", + "Gene ... \n", + "TMA7 False 13.029 ... 0.4 False \n", + "IGLV8-61 False 12.395 ... 0.8 False \n", + "IGLV3-10 False 13.335 ... 0.6 False \n", + "IGLV3-9 True 13.680 ... 0.0 True \n", + "IGKV2-28 True 14.014 ... 0.2 True \n", + "\n", + " RCN3_mean RCN3_nan_count RCN3_valid_count RCN3_nan_proportions \\\n", + "Gene \n", + "TMA7 13.933 3 2 0.6 \n", + "IGLV8-61 14.090 2 3 0.4 \n", + "IGLV3-10 15.097 3 2 0.6 \n", + "IGLV3-9 16.906 0 5 0.0 \n", + "IGKV2-28 16.407 0 5 0.0 \n", + "\n", + " RCN3_valid valid_in_all_groups valid_in_any_group \\\n", + "Gene \n", + "TMA7 False False False \n", + "IGLV8-61 False False False \n", + "IGLV3-10 False False False \n", + "IGLV3-9 True True True \n", + "IGKV2-28 True True True \n", + "\n", + " not_valid_in_any_group \n", + "Gene \n", + "TMA7 True \n", + "IGLV8-61 True \n", + "IGLV3-10 True \n", + "IGLV3-9 False \n", + "IGKV2-28 False \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_filtered.uns['filter_features_byNaNs_qc_metrics'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e1afd342", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Protein.Group', 'Protein.Names', 'Genes', 'First.Protein.Description',\n", + " 'overall_mean', 'overall_nan_count', 'overall_valid_count',\n", + " 'overall_nan_proportions', 'overall_valid', 'RCN1_mean',\n", + " 'RCN1_nan_count', 'RCN1_valid_count', 'RCN1_nan_proportions',\n", + " 'RCN1_valid', 'RCN3_mean', 'RCN3_nan_count', 'RCN3_valid_count',\n", + " 'RCN3_nan_proportions', 'RCN3_valid', 'valid_in_all_groups',\n", + " 'valid_in_any_group', 'not_valid_in_any_group'],\n", + " dtype='object')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_filtered.uns['filter_features_byNaNs_qc_metrics'].columns" + ] + }, + { + "cell_type": "markdown", + "id": "fbc1506f", + "metadata": {}, + "source": [ + "In this dataframe, stored away in adata.uns, you can see all th qc metrics of the filtering" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "76906110", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:17:12.78\u001b[0m | \u001b[1mINFO\u001b[0m | Writing h5ad\n", + "\u001b[32m15:17:12.84\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | Wrote h5ad file\n" + ] + } + ], + "source": [ + "# Store filtered adata\n", + "dvp.io.export_adata(adata=adata_filtered, path_to_dir=\"data/checkpoints\", checkpoint_name=\"2_filtered\")" + ] + }, + { + "cell_type": "markdown", + "id": "1a0c37ef", + "metadata": {}, + "source": [ + "## Imputation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b23fde66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:17:31.45\u001b[0m | \u001b[1mINFO\u001b[0m | Storing original data in `adata.layers['unimputed']`.\n", + "\u001b[32m15:17:31.45\u001b[0m | \u001b[1mINFO\u001b[0m | Imputation with Gaussian distribution PER PROTEIN\n", + "\u001b[32m15:17:31.46\u001b[0m | \u001b[1mINFO\u001b[0m | Mean number of missing values per sample: 572.6 out of 4637 proteins\n", + "\u001b[32m15:17:31.46\u001b[0m | \u001b[1mINFO\u001b[0m | Mean number of missing values per protein: 1.23 out of 10 samples\n", + "\u001b[32m15:17:33.35\u001b[0m | \u001b[1mINFO\u001b[0m | Imputation complete. QC metrics stored in `adata.uns['impute_gaussian_qc_metrics']`.\n" + ] + } + ], + "source": [ + "adata_imputed = dvp.tl.impute_gaussian(adata=adata_filtered, mean_shift=-1.8, std_dev_shift=0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "65ae0c0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 10 × 4637\n", + " obs: 'Precursors.Identified', 'Proteins.Identified', 'Average.Missed.Tryptic.Cleavages', 'LCMS_run_id', 'RCN', 'RCN_long', 'QuPath_class'\n", + " var: 'Protein.Group', 'Protein.Names', 'Genes', 'First.Protein.Description', 'mean', 'nan_proportions'\n", + " uns: 'filter_features_byNaNs_qc_metrics', 'impute_gaussian_qc_metrics'\n", + " layers: 'unimputed'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_imputed" + ] + }, + { + "cell_type": "markdown", + "id": "64bd7a67", + "metadata": {}, + "source": [ + "Like the previous process, the imputation stores two quality control datasets. \n", + "First, the `impute_gaussian_qc_metrics`\n", + "Showing you per protein:\n", + "- how many values were imputed\n", + "- the distribution used\n", + "- the values used to impute with" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2a64a38d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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n_imputedimputation_meanimputation_stddevimputed_values
Gene
IGLV3-9015.2927781.840310NAN
IGKV2-28115.3431031.346909[12.4703]
IGHV3-64613.7100451.247562[11.1396, 12.2713, 12.2389, 10.8992, 11.9149, ...
IGKV2D-29016.2133941.481762NAN
IGKV1-27013.4522611.394043NAN
...............
WASF2015.1354110.255652NAN
MAU2112.4754820.455856[11.636]
ENPP4012.0083730.630813NAN
MORC2112.3307340.783775[10.7335]
SEC23IP014.0501910.227216NAN
\n", + "

4637 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " n_imputed imputation_mean imputation_stddev \\\n", + "Gene \n", + "IGLV3-9 0 15.292778 1.840310 \n", + "IGKV2-28 1 15.343103 1.346909 \n", + "IGHV3-64 6 13.710045 1.247562 \n", + "IGKV2D-29 0 16.213394 1.481762 \n", + "IGKV1-27 0 13.452261 1.394043 \n", + "... ... ... ... \n", + "WASF2 0 15.135411 0.255652 \n", + "MAU2 1 12.475482 0.455856 \n", + "ENPP4 0 12.008373 0.630813 \n", + "MORC2 1 12.330734 0.783775 \n", + "SEC23IP 0 14.050191 0.227216 \n", + "\n", + " imputed_values \n", + "Gene \n", + "IGLV3-9 NAN \n", + "IGKV2-28 [12.4703] \n", + "IGHV3-64 [11.1396, 12.2713, 12.2389, 10.8992, 11.9149, ... \n", + "IGKV2D-29 NAN \n", + "IGKV1-27 NAN \n", + "... ... \n", + "WASF2 NAN \n", + "MAU2 [11.636] \n", + "ENPP4 NAN \n", + "MORC2 [10.7335] \n", + "SEC23IP NAN \n", + "\n", + "[4637 rows x 4 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_imputed.uns['impute_gaussian_qc_metrics']" + ] + }, + { + "cell_type": "markdown", + "id": "511e3038", + "metadata": {}, + "source": [ + "Second, the unimputed values are stored inside the layers compartment of the adata object. \n", + "This is a backup in case imputation has done something wrong. \n", + "You can always call those values by `adata_imputed.layers['unimputed']`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d8d33036", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:18:23.45\u001b[0m | \u001b[1mINFO\u001b[0m | Writing h5ad\n", + "\u001b[32m15:18:23.51\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | Wrote h5ad file\n" + ] + } + ], + "source": [ + "dvp.io.export_adata(adata=adata_imputed, path_to_dir=\"data/checkpoints\", checkpoint_name=\"3_imputed\")" + ] + }, + { + "cell_type": "markdown", + "id": "5918dc5b", + "metadata": {}, + "source": [ + "### Scanpy's PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f010c0c0", + "metadata": {}, + "outputs": [], + "source": [ + "sc.pp.pca(adata_imputed)" + ] + }, + { + "cell_type": "markdown", + "id": "2fc6b3d3", + "metadata": {}, + "source": [ + "Scanpy is a very powerful data analysis package created for single-cell RNA sequencing. \n", + "We use it here because it is very convenient, and it already expects the AnnData format we have. \n", + "Beware of using Scanpy for proteomics datasets, assumptions will vary." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "364a148c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 10 × 4637\n", + " obs: 'Precursors.Identified', 'Proteins.Identified', 'Average.Missed.Tryptic.Cleavages', 'LCMS_run_id', 'RCN', 'RCN_long', 'QuPath_class'\n", + " var: 'Protein.Group', 'Protein.Names', 'Genes', 'First.Protein.Description', 'mean', 'nan_proportions'\n", + " uns: 'filter_features_byNaNs_qc_metrics', 'impute_gaussian_qc_metrics', 'pca'\n", + " obsm: 'X_pca'\n", + " varm: 'PCs'\n", + " layers: 'unimputed'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_imputed" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4e0c5912", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# let's plot it\n", + "sc.pl.pca(adata_imputed, color=\"RCN\", annotate_var_explained=True, size=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "651cce25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 [-0.38130396 -0.40484619]\n", + "52 [-0.39167196 0.581337 ]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# let's plot the direction of each feature\n", + "dvp.plotting.pca_loadings(adata_imputed)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "07695c6b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:19:19.65\u001b[0m | \u001b[1mINFO\u001b[0m | Writing h5ad\n", + "\u001b[32m15:19:19.71\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | Wrote h5ad file\n" + ] + } + ], + "source": [ + "dvp.io.export_adata(adata=adata_imputed, path_to_dir=\"data/checkpoints\", checkpoint_name=\"4_pca\")" + ] + }, + { + "cell_type": "markdown", + "id": "243d5551", + "metadata": {}, + "source": [ + "### heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "951e0205", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", + " warnings.warn(msg)\n", + "/opt/homebrew/Caskroom/mambaforge/base/envs/opendvp_tut/lib/python3.11/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataframe = pd.DataFrame(data=adata_imputed.X, columns=adata_imputed.var_names, index=adata_imputed.obs.RCN)\n", + "sns.clustermap(data=dataframe.T, z_score=0, cmap=\"bwr\", vmin=-2, vmax=2)" + ] + }, + { + "cell_type": "markdown", + "id": "f96a1b18", + "metadata": {}, + "source": [ + "## Differential analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2934c024", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:20:10.81\u001b[0m | \u001b[1mINFO\u001b[0m | Using pingouin.ttest to perform unpaired two-sided t-test between RCN1 and RCN3\n", + "\u001b[32m15:20:10.81\u001b[0m | \u001b[1mINFO\u001b[0m | Using Benjamini-Hochberg for FDR correction, with a threshold of 0.05\n", + "\u001b[32m15:20:10.81\u001b[0m | \u001b[1mINFO\u001b[0m | The test found 1997 proteins to be significantly\n" + ] + } + ], + "source": [ + "# ttest\n", + "adata_DAP = dvp.tl.stats_ttest(adata_imputed, grouping=\"RCN\", group1=\"RCN1\", group2=\"RCN3\", FDR_threshold=0.05)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "7109b6d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 10 × 4637\n", + " obs: 'Precursors.Identified', 'Proteins.Identified', 'Average.Missed.Tryptic.Cleavages', 'LCMS_run_id', 'RCN', 'RCN_long', 'QuPath_class'\n", + " var: 'Protein.Group', 'Protein.Names', 'Genes', 'First.Protein.Description', 'mean', 'nan_proportions', 't_val', 'p_val', 'mean_diff', 'sig', 'p_corr', '-log10_p_corr'\n", + " uns: 'filter_features_byNaNs_qc_metrics', 'impute_gaussian_qc_metrics', 'pca', 'RCN_colors'\n", + " obsm: 'X_pca'\n", + " varm: 'PCs'\n", + " layers: 'unimputed'" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata_DAP" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "dadbb214", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m15:21:26.28\u001b[0m | \u001b[1mINFO\u001b[0m | Writing h5ad\n", + "\u001b[32m15:21:26.37\u001b[0m | \u001b[32m\u001b[1mSUCCESS\u001b[0m | Wrote h5ad file\n" + ] + } + ], + "source": [ + "dvp.io.export_adata(adata=adata_DAP, path_to_dir=\"data/checkpoints\", checkpoint_name=\"5_DAP\")" + ] + }, + { + "cell_type": "markdown", + "id": "c74a6047", + "metadata": {}, + "source": [ + "### plotting with volcano plot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "89f4062f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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