{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# `coSMicQC` in a nutshell\n", "\n", "This notebook demonstrates various capabilities of `coSMicQC` using examples." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2026-04-17T11:59:33.192067Z", "iopub.status.busy": "2026-04-17T11:59:33.191940Z", "iopub.status.idle": "2026-04-17T11:59:47.670521Z", "shell.execute_reply": "2026-04-17T11:59:47.670032Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b7892a151c5146848635f108a87b4300", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(IntSlider(value=50, continuous_update=False, description='Image adjustment:', la…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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41Plate_2441A1299131327385.0...-12.181288-11.6999930.9926240.657791122412241224
..................................................................
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1714 rows × 2076 columns

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pathlib\n", "\n", "import pandas as pd\n", "from cytodataframe import CytoDataFrame\n", "\n", "import cosmicqc\n", "\n", "# set a path for the parquet-based dataset\n", "# (in this case, CellProfiler SQLite data processed by CytoTable)\n", "data_path = (\n", " \"../../../tests/data/cytotable/NF1_cellpainting_data/\"\n", " \"Plate_2_with_image_data.parquet\"\n", ")\n", "\n", "# set a context directory for images associated with the dataset\n", "image_context_dir = pathlib.Path(data_path).parent / \"Plate_2_images\"\n", "mask_context_dir = pathlib.Path(data_path).parent / \"Plate_2_masks\"\n", "\n", "# create a cosmicqc CytoDataFrame (single-cell DataFrame)\n", "scdf = CytoDataFrame(\n", " data=data_path,\n", " data_context_dir=image_context_dir,\n", " data_mask_context_dir=mask_context_dir,\n", ")\n", "\n", "# display the dataframe\n", "scdf" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2026-04-17T11:59:47.687454Z", "iopub.status.busy": "2026-04-17T11:59:47.687299Z", "iopub.status.idle": "2026-04-17T11:59:48.165685Z", "shell.execute_reply": "2026-04-17T11:59:48.165223Z" } }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1085 False\n", "1083 False\n", "1082 False\n", "1080 False\n", " ... \n", "572 True\n", "571 True\n", "567 True\n", "280 True\n", "856 True\n", "Name: Metadata_cqc_custom_is_outlier, Length: 1714, dtype: bool" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Identify which rows include outliers for a given threshold definition\n", "# which references a column name and a z-score number which is considered\n", "# the limit.\n", "cosmicqc.analyze.identify_outliers(\n", " df=scdf,\n", " feature_thresholds={\"Nuclei_AreaShape_Area\": -1},\n", ").sort_values()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2026-04-17T11:59:48.167358Z", "iopub.status.busy": "2026-04-17T11:59:48.167274Z", "iopub.status.idle": "2026-04-17T11:59:48.765503Z", "shell.execute_reply": "2026-04-17T11:59:48.764914Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of outliers: 328 (19.14%)\n", "Outliers Range:\n", "Nuclei_AreaShape_Area Min: 734.0\n", "Nuclei_AreaShape_Area Max: 1904.0\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Clamping filter plot threshold for column 'Nuclei_AreaShape_Area' from 1908.5286283956243 to 1904.0 because it is outside data range [734.0, 1904.0].\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "11e78caddc30410a83fc95137ba09d33", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(IntSlider(value=50, continuous_update=False, description='Image adjustment:', la…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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Nuclei_AreaShape_AreaMetadata_ImageNumberImage_Metadata_Plate_x
23921.02Plate_2
28845.02Plate_2
291024.02Plate_2
32787.02Plate_2
371347.02Plate_2
............
16821497.0127Plate_2
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328 rows × 3 columns

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show the number of outliers given a column name and a specified threshold\n", "# via the `feature_thresholds` parameter and the `find_outliers` function.\n", "cosmicqc.analyze.find_outliers(\n", " df=scdf,\n", " metadata_columns=[\"Metadata_ImageNumber\", \"Image_Metadata_Plate_x\"],\n", " feature_thresholds={\"Nuclei_AreaShape_Area\": -1},\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2026-04-17T11:59:48.767441Z", "iopub.status.busy": "2026-04-17T11:59:48.767321Z", "iopub.status.idle": "2026-04-17T11:59:50.251061Z", "shell.execute_reply": "2026-04-17T11:59:50.250540Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0ccf5ff3661248a1a57c94f7daddb851", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(IntSlider(value=50, continuous_update=False, description='Image adjustment:', la…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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Metadata_ImageNumberImage_Metadata_Plate_xMetadata_number_of_singlecellsImage_Metadata_Site_xImage_Metadata_Well_xMetadata_Cells_Number_Object_NumberMetadata_Cytoplasm_Parent_CellsMetadata_Cytoplasm_Parent_NucleiMetadata_Nuclei_Number_Object_NumberCytoplasm_AreaShape_Area...Image_Threshold_WeightedVariance_NucleiImage_URL_DAPIImage_URL_GFPImage_URL_RFPImage_Width_DAPIImage_Width_GFPImage_Width_RFPMetadata_cqc_large_nuclei_Nuclei_AreaShape_Area_zscoreMetadata_cqc_large_nuclei_Nuclei_AreaShape_FormFactor_zscoreMetadata_cqc_large_nuclei_is_outlier
01Plate_2441A12112221024.0...0.6577911224122412240.8488200.219903False
11Plate_2441A12447712754.0...0.657791122412241224-0.252521-1.280795False
21Plate_2441A1277101023976.0...0.657791122412241224-0.402491-0.325652False
31Plate_2441A1288121219374.0...0.6577911224122412240.329549-0.268920False
41Plate_2441A1299131327385.0...0.6577911224122412241.1534460.028845False
..................................................................
1709128Plate_2594H71010141424942.0...0.5451861224122412240.598557-0.280063False
1710128Plate_2594H7111115156627.0...0.545186122412241224-0.7164900.068293False
1711128Plate_2594H71212161611216.0...0.5451861224122412241.1871890.833264False
1712128Plate_2594H71313171715279.0...0.545186122412241224-0.6996190.534479False
1713128Plate_2594H7141420207106.0...0.545186122412241224-0.9901850.356614False
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1714 rows × 2079 columns

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create a labeled dataset which includes z-scores and whether those scores\n", "# are interpreted as outliers or inliers. We use pre-defined threshold sets\n", "# loaded from defaults (cosmicqc can accept user-defined thresholds too!).\n", "labeled_scdf = cosmicqc.analyze.label_outliers(\n", " df=scdf, include_threshold_scores=True, feature_thresholds=\"large_nuclei\"\n", ")\n", "labeled_scdf" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2026-04-17T11:59:50.252805Z", "iopub.status.busy": "2026-04-17T11:59:50.252697Z", "iopub.status.idle": "2026-04-17T11:59:53.713408Z", "shell.execute_reply": "2026-04-17T11:59:53.712958Z" } }, "outputs": [ { "data": { "text/plain": [] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show cropped images through CytoDataFrame from the dataset to help analyze outliers\n", "# labeled_scdf._enbable_debug_mode()\n", "labeled_scdf.sort_values(by=\"Metadata_cqc_large_nuclei_is_outlier\", ascending=False)[\n", " [\n", " \"Metadata_ImageNumber\",\n", " \"Metadata_Cells_Number_Object_Number\",\n", " \"Metadata_cqc_large_nuclei_is_outlier\",\n", " \"Image_FileName_GFP\",\n", " \"Image_FileName_RFP\",\n", " \"Image_FileName_DAPI\",\n", " ]\n", "]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2026-04-17T12:19:50.309609Z", "iopub.status.busy": "2026-04-17T12:19:50.308362Z", "iopub.status.idle": "2026-04-17T12:19:50.332727Z", "shell.execute_reply": "2026-04-17T12:19:50.332113Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "
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1714 rows × 2076 columns

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" ], "text/plain": [ " Metadata_ImageNumber Image_Metadata_Plate_x \\\n", "0 1 Plate_2 \n", "1 1 Plate_2 \n", "2 1 Plate_2 \n", "3 1 Plate_2 \n", "4 1 Plate_2 \n", "... ... ... \n", "1709 128 Plate_2 \n", "1710 128 Plate_2 \n", "1711 128 Plate_2 \n", "1712 128 Plate_2 \n", "1713 128 Plate_2 \n", "\n", " Metadata_number_of_singlecells Image_Metadata_Site_x \\\n", "0 44 1 \n", "1 44 1 \n", "2 44 1 \n", "3 44 1 \n", "4 44 1 \n", "... ... ... \n", "1709 59 4 \n", "1710 59 4 \n", "1711 59 4 \n", "1712 59 4 \n", "1713 59 4 \n", "\n", " Image_Metadata_Well_x Metadata_Cells_Number_Object_Number \\\n", "0 A12 1 \n", "1 A12 4 \n", "2 A12 7 \n", "3 A12 8 \n", "4 A12 9 \n", "... ... ... \n", "1709 H7 10 \n", "1710 H7 11 \n", "1711 H7 12 \n", "1712 H7 13 \n", "1713 H7 14 \n", "\n", " Metadata_Cytoplasm_Parent_Cells Metadata_Cytoplasm_Parent_Nuclei \\\n", "0 1 2 \n", "1 4 7 \n", "2 7 10 \n", "3 8 12 \n", "4 9 13 \n", "... ... ... \n", "1709 10 14 \n", "1710 11 15 \n", "1711 12 16 \n", "1712 13 17 \n", "1713 14 20 \n", "\n", " Metadata_Nuclei_Number_Object_Number Cytoplasm_AreaShape_Area ... \\\n", "0 2 21024.0 ... \n", "1 7 12754.0 ... \n", "2 10 23976.0 ... \n", "3 12 19374.0 ... \n", "4 13 27385.0 ... \n", "... ... ... ... \n", "1709 14 24942.0 ... \n", "1710 15 6627.0 ... \n", "1711 16 11216.0 ... \n", "1712 17 15279.0 ... \n", "1713 20 7106.0 ... \n", "\n", " Image_Threshold_SumOfEntropies_Cells \\\n", "0 -12.181288 \n", "1 -12.181288 \n", "2 -12.181288 \n", "3 -12.181288 \n", "4 -12.181288 \n", "... ... \n", "1709 -12.566582 \n", "1710 -12.566582 \n", "1711 -12.566582 \n", "1712 -12.566582 \n", "1713 -12.566582 \n", "\n", " Image_Threshold_SumOfEntropies_Nuclei \\\n", "0 -11.699993 \n", "1 -11.699993 \n", "2 -11.699993 \n", "3 -11.699993 \n", "4 -11.699993 \n", "... ... \n", "1709 -11.633043 \n", "1710 -11.633043 \n", "1711 -11.633043 \n", "1712 -11.633043 \n", "1713 -11.633043 \n", "\n", " Image_Threshold_WeightedVariance_Cells \\\n", "0 0.992624 \n", "1 0.992624 \n", "2 0.992624 \n", "3 0.992624 \n", "4 0.992624 \n", "... ... \n", "1709 1.624310 \n", "1710 1.624310 \n", "1711 1.624310 \n", "1712 1.624310 \n", "1713 1.624310 \n", "\n", " Image_Threshold_WeightedVariance_Nuclei \\\n", "0 0.657791 \n", "1 0.657791 \n", "2 0.657791 \n", "3 0.657791 \n", "4 0.657791 \n", "... ... \n", "1709 0.545186 \n", "1710 0.545186 \n", "1711 0.545186 \n", "1712 0.545186 \n", "1713 0.545186 \n", "\n", " Image_URL_DAPI \\\n", "0 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "2 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "3 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "4 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "... ... \n", "1709 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1710 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1711 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1712 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1713 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "\n", " Image_URL_GFP \\\n", "0 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "2 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "3 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "4 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "... ... \n", "1709 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1710 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1711 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1712 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "1713 file:/home/jenna/nf1_cellpainting_data/1.cellp... \n", "\n", " Image_URL_RFP Image_Width_DAPI \\\n", "0 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "1 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "2 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "3 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "4 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "... ... ... \n", "1709 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "1710 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "1711 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "1712 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "1713 file:/home/jenna/nf1_cellpainting_data/1.cellp... 1224 \n", "\n", " Image_Width_GFP Image_Width_RFP \n", "0 1224 1224 \n", "1 1224 1224 \n", "2 1224 1224 \n", "3 1224 1224 \n", "4 1224 1224 \n", "... ... ... \n", "1709 1224 1224 \n", "1710 1224 1224 \n", "1711 1224 1224 \n", "1712 1224 1224 \n", "1713 1224 1224 \n", "\n", "[1714 rows x 2076 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# One can convert from cosmicqc.CytoDataFrame to pd.DataFrame's\n", "# (when or if needed!)\n", "df = pd.DataFrame(scdf)\n", "print(type(df))\n", "df" ] } ], "metadata": { "jupytext": { "formats": 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