### Install JUMP Profiling Recipe Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Clones the repository, navigates into it, sets up the Python virtual environment using uv, and activates it. ```bash git clone cd jump-profiling-recipe uv sync && uv pip install -e . source .venv/bin/activate ``` -------------------------------- ### Example Data Conversion with JUMP CLI Tool Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Provides a complete example command for converting Cell Painting data to JUMP-compatible format, demonstrating the use of various required and optional parameters like output directory, feature columns, source, plate type, and perturbation identifiers. ```bash python -m jump_profiling_recipe.cli.converter convert \ my_file_list.txt \ --output-dir my_converted_data \ --mandatory-feature-cols-file inputs/metadata/cpg0016_mandatory_feature_columns_without_image_columns.txt \ --source LAB01 \ --default-plate-type COMPOUND \ --jcp2022-cols Compound_ID,Concentration ``` -------------------------------- ### Copying Workflow Configuration File Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Demonstrates how to duplicate an existing Snakemake configuration file to serve as a starting point for a new custom pipeline. ```bash cp inputs/config/compound.json inputs/config/my_pipeline.json ``` -------------------------------- ### Creating Input File List for JUMP Data Converter Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Illustrates how to generate a simple text file containing a list of input data files, one path per line, which is required by the JUMP data converter. ```bash echo "/path/to/data/plate1.csv" > my_file_list.txt echo "/path/to/data/plate2.csv" >> my_file_list.txt ``` -------------------------------- ### Run Snakemake Workflow with Configuration Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Executes the Snakemake workflow using 4 cores and a specified JSON configuration file, such as 'compound.json' or 'pipeline_1.json'. ```bash snakemake -c4 --configfile inputs/config/compound.json ``` -------------------------------- ### Enabling Verbose Logging for Snakemake Troubleshooting Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Provides the command to run a Snakemake workflow with verbose logging enabled, which is highly useful for debugging and troubleshooting issues during execution. ```bash snakemake -c1 --configfile inputs/config/compound.json --verbose ``` -------------------------------- ### Snakemake Pipeline Dependency Graph Visualization Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md This Graphviz diagram illustrates the dependency graph for a Snakemake pipeline, specifically `profiles_var_mad_int_featselect_harmony`. It shows how different processing steps (rules) are connected, with arrows indicating dependencies, and how Snakemake resolves these steps to achieve the final output. ```graphviz digraph snakemake_dag { graph[bgcolor=white, margin=0]; node[shape=box, style=rounded, fontname=sans, fontsize=10, penwidth=2]; edge[penwidth=2, color=grey]; 0[label = "all", color = "0.03 0.6 0.85", style="rounded"]; 1[label = "reformat", color = "0.43 0.6 0.85", style="rounded"]; 2[label = "harmony\npipeline: profiles_var_mad_int_featselect", color = "0.30 0.6 0.85", style="rounded"]; 3[label = "featselect\npipeline: profiles_var_mad_int", color = "0.27 0.6 0.85", style="rounded"]; 4[label = "INT\npipeline: profiles_var_mad", color = "0.00 0.6 0.85", style="rounded"]; 5[label = "mad_normalize", color = "0.33 0.6 0.85", style="rounded"]; 6[label = "select_variant_feats", color = "0.53 0.6 0.85", style="rounded"]; 7[label = "write_parquet\nscenario: compound", color = "0.63 0.6 0.85", style="rounded"]; 8[label = "compute_norm_stats\npipeline: profiles", color = "0.20 0.6 0.85", style="rounded"]; 9[label = "compute_norm_stats\npipeline: profiles_var", color = "0.20 0.6 0.85", style="rounded"]; 10[label = "average_precision_nonrep\npipeline: profiles_var_mad_int_featselect_harmony\nprefix: compound", color = "0.13 0.6 0.85", style="rounded"]; 11[label = "mean_average_precision\nreftype: nonrep", color = "0.37 0.6 0.85", style="rounded"]; 1 -> 0 10 -> 0 11 -> 0 2 -> 1 3 -> 2 4 -> 3 5 -> 4 6 -> 5 9 -> 5 7 -> 6 8 -> 6 7 -> 8 6 -> 9 2 -> 10 10 -> 11 } ``` -------------------------------- ### Running Snakemake Workflow with Custom Configuration Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Shows the command to execute a Snakemake workflow, specifying a newly created or modified configuration file for custom pipeline execution. ```bash snakemake -c1 --configfile inputs/config/my_pipeline.json ``` -------------------------------- ### JUMP Data Converter CLI Tool API Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Documents the command-line interface for the JUMP Profiling Recipe's data converter, detailing its required and optional parameters for transforming non-JUMP Cell Painting data into a JUMP-compatible format. ```APIDOC CLI Tool: jump_profiling_recipe.cli.converter convert Required Parameters: file_list: Path to a text file containing a list of input files (one per line) --output-dir: Directory where processed files will be saved --source: Value to set in the `Metadata_Source` column (your data source identifier) --jcp2022-cols: Comma-separated list of columns that identify your perturbations (will be combined to create `Metadata_JCP2022`) Optional Parameters: --mandatory-feature-cols-file: Path to a file listing feature columns to keep (e.g., `inputs/metadata/cpg0016_mandatory_feature_columns.txt` or `inputs/metadata/cpg0016_mandatory_feature_columns_without_image_columns.txt`) --mandatory-metadata: Comma-separated list of required metadata columns (default: "Metadata_Plate,Metadata_Well") --default-plate-type: Value for `Metadata_PlateType` when not in source data (default: "UNKNOWN") --continue-on-error: Continue processing remaining files if an error occurs --verbose: Enable verbose logging ``` -------------------------------- ### JUMP Workflow Configuration Parameters Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Describes the key parameters used in the JSON configuration files for the JUMP profiling workflow, including their purpose and possible values. ```APIDOC Configuration Parameters: scenario: A user-defined name for your processing scenario with no constraints - choose any meaningful name to organize your outputs pipeline: Pipeline definition (e.g., profiles_var_mad_int_featselect_harmony) that automatically determines which processing steps will run sources: List of data generating centers to include. The JUMP dataset comprises multiple data generating centers (each referred to as a source), which serves as a high-level organization of the data plate_types: List of plate types to include. JUMP defines specific plate types in its metadata: TARGET1, TARGET2, POSCON8, DMSO, ORF, COMPOUND, COMPOUND_EMPTY. This parameter allows filtering based on these plate types batch_key: Column name for batch information used in Harmony batch effect correction (typically "Metadata_Batch"). ``` -------------------------------- ### Configuring Snakemake Workflow with New Data Source Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Shows how to update the Snakemake configuration file by adding a newly converted data source identifier to the `sources` list, enabling the workflow to process the new data. ```json { "sources": [ "source_1", "source_2", "LAB01" // Your source identifier ] } ``` -------------------------------- ### Execute Snakemake Workflow with Parallel Cores Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md This command executes a Snakemake workflow, leveraging 4 CPU cores for parallel processing. It uses the configuration file `inputs/config/compound.json` to define the workflow parameters and targets. ```bash snakemake -c4 --configfile inputs/config/compound.json ``` -------------------------------- ### Target Specific Output File in Snakemake Workflow Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md This command instructs Snakemake to build a specific output file, `outputs/compound/profiles_var_mad_int_featselect_harmony.parquet`, using 1 CPU core. Snakemake will automatically determine and execute all necessary preceding steps to generate this target file based on the provided configuration. ```bash snakemake -c1 outputs/compound/profiles_var_mad_int_featselect_harmony.parquet --configfile inputs/config/compound.json ``` -------------------------------- ### Available Snakemake Workflow Rules Reference Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md This section provides a comprehensive reference of the available rules within the Snakemake workflow. Rules are categorized by their primary function: Main Processing, Metrics, and Sphering. Each rule performs a specific transformation or calculation step within the pipeline. ```APIDOC Main Processing Rules: - write_parquet: Writes initial profile data to parquet format - compute_norm_stats: Computes normalization statistics - select_variant_feats: Selects variant features - mad_normalize: Performs MAD normalization - INT: Applies rank inverse normal transformation - well_correct: Corrects for well position effects - cc_regress: Performs cell count regression - remove_outliers: Removes outlier samples - annotate_genes: Adds gene annotations - pca_transform: Applies PCA transformation - correct_arm: Performs chromosome arm correction - featselect: Selects features to keep - harmony: Applies Harmony batch correction - reformat: Performs final formatting checks Metrics Rules: - average_precision_negcon: Calculates average precision w.r.t. negative controls - average_precision_nonrep: Calculates average precision w.r.t. non-replicate samples - mean_average_precision: Calculates mean average precision from AP scores Sphering Rules: - sphering_explore: Explores different regularization parameters for sphering - select_best_sphering: Selects the best sphering parameter based on metrics ``` -------------------------------- ### Download JUMP Well-Level Aggregated Profiles Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Executes a shell script to download well-level aggregated profiles for a specified perturbation type (e.g., compound, crispr, or orf). ```bash source download_data.sh compound # Replace with crispr or orf as needed ``` -------------------------------- ### Perform a Dry Run of Snakemake Workflow Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md This command performs a dry run of the Snakemake workflow, showing which rules would be executed without actually running them. It's useful for verifying the workflow logic and dependencies before a full execution, using the specified configuration file. ```bash snakemake -n --configfile inputs/config/compound.json ``` -------------------------------- ### Combining Multiple Perturbation IDs for JUMP Data Conversion Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/DOCUMENTATION.md Demonstrates a specific use case of the `--jcp2022-cols` parameter to combine values from multiple input columns (e.g., Compound_ID and Concentration) into a single `Metadata_JCP2022` identifier during the data conversion process. ```bash python -m jump_profiling_recipe.cli.converter convert \ my_file_list.txt \ --output-dir my_converted_data \ --source LAB01 \ --jcp2022-cols Compound_ID,Concentration ``` -------------------------------- ### Cells_Granularity Measurements Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns.txt Defines textural measurements based on granularity analysis for different cellular compartments or channels (AGP, DNA, ER, Mito, RNA). These metrics quantify the coarseness or fineness of textures within an image, across various scales (10-16). ```APIDOC Cells_Granularity: 10: AGP: float DNA: float ER: float Mito: float RNA: float 11: AGP: float DNA: float ER: float Mito: float RNA: float 12: AGP: float DNA: float ER: float Mito: float RNA: float 13: AGP: float DNA: float ER: float Mito: float RNA: float 14: AGP: float DNA: float ER: float Mito: float RNA: float 15: AGP: float DNA: float ER: float Mito: float RNA: float 16: AGP: float DNA: float ER: float Mito: float RNA: float ``` -------------------------------- ### Cells_AreaShape Measurements Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns.txt Defines a comprehensive set of morphological and shape-related measurements for individual cells. These metrics quantify various aspects of cell geometry, size, and form, including area, bounding box dimensions, eccentricity, compactness, and Zernike moments. ```APIDOC Cells_AreaShape: Area: float BoundingBoxArea: float BoundingBoxMaximum_X: float BoundingBoxMaximum_Y: float BoundingBoxMinimum_X: float BoundingBoxMinimum_Y: float Center_X: float Center_Y: float Compactness: float Eccentricity: float EquivalentDiameter: float EulerNumber: int Extent: float FormFactor: float MajorAxisLength: float MaxFeretDiameter: float MaximumRadius: float MeanRadius: float MedianRadius: float MinFeretDiameter: float MinorAxisLength: float Orientation: float Perimeter: float Solidity: float Zernike_0_0: float Zernike_1_1: float Zernike_2_0: float Zernike_2_2: float Zernike_3_1: float Zernike_3_3: float Zernike_4_0: float Zernike_4_2: float Zernike_4_4: float Zernike_5_1: float Zernike_5_3: float Zernike_5_5: float Zernike_6_0: float Zernike_6_2: float Zernike_6_4: float Zernike_6_6: float Zernike_7_1: float Zernike_7_3: float Zernike_7_5: float Zernike_7_7: float Zernike_8_0: float Zernike_8_2: float Zernike_8_4: float Zernike_8_6: float Zernike_8_8: float Zernike_9_1: float Zernike_9_3: float Zernike_9_5: float Zernike_9_7: float Zernike_9_9: float ``` -------------------------------- ### Cytoplasm Granularity Features Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns.txt Provides features that measure the texture and granularity of the cytoplasm at different spatial scales (represented by the numbers 1-12). These features are useful for characterizing the internal organization and heterogeneity of the cytoplasm across various fluorescent channels. ```APIDOC Cytoplasm_Granularity_10_AGP Cytoplasm_Granularity_10_DNA Cytoplasm_Granularity_10_ER Cytoplasm_Granularity_10_Mito Cytoplasm_Granularity_10_RNA Cytoplasm_Granularity_11_AGP Cytoplasm_Granularity_11_DNA Cytoplasm_Granularity_11_ER Cytoplasm_Granularity_11_Mito Cytoplasm_Granularity_11_RNA Cytoplasm_Granularity_12_AGP Cytoplasm_Granularity_12_DNA Cytoplasm_Granularity_12_ER Cytoplasm_Granularity_12_Mito ``` -------------------------------- ### Cytoplasm AreaShape Features Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns.txt Defines geometric and morphological features of the cytoplasm, such as size, shape, and spatial distribution. These features describe the physical characteristics of the segmented cytoplasm region, including bounding box dimensions, eccentricity, compactness, and Zernike moments. ```APIDOC Cytoplasm_AreaShape_BoundingBoxMaximum_X Cytoplasm_AreaShape_BoundingBoxMaximum_Y Cytoplasm_AreaShape_BoundingBoxMinimum_X Cytoplasm_AreaShape_BoundingBoxMinimum_Y Cytoplasm_AreaShape_Center_X Cytoplasm_AreaShape_Center_Y Cytoplasm_AreaShape_Compactness Cytoplasm_AreaShape_Eccentricity Cytoplasm_AreaShape_EquivalentDiameter Cytoplasm_AreaShape_EulerNumber Cytoplasm_AreaShape_Extent Cytoplasm_AreaShape_FormFactor Cytoplasm_AreaShape_MajorAxisLength Cytoplasm_AreaShape_MaxFeretDiameter Cytoplasm_AreaShape_MaximumRadius Cytoplasm_AreaShape_MeanRadius Cytoplasm_AreaShape_MedianRadius Cytoplasm_AreaShape_MinFeretDiameter Cytoplasm_AreaShape_MinorAxisLength Cytoplasm_AreaShape_Orientation Cytoplasm_AreaShape_Perimeter Cytoplasm_AreaShape_Solidity Cytoplasm_AreaShape_Zernike_0_0 Cytoplasm_AreaShape_Zernike_1_1 Cytoplasm_AreaShape_Zernike_2_0 Cytoplasm_AreaShape_Zernike_2_2 Cytoplasm_AreaShape_Zernike_3_1 Cytoplasm_AreaShape_Zernike_3_3 Cytoplasm_AreaShape_Zernike_4_0 Cytoplasm_AreaShape_Zernike_4_2 Cytoplasm_AreaShape_Zernike_4_4 Cytoplasm_AreaShape_Zernike_5_1 Cytoplasm_AreaShape_Zernike_5_3 Cytoplasm_AreaShape_Zernike_5_5 Cytoplasm_AreaShape_Zernike_6_0 Cytoplasm_AreaShape_Zernike_6_2 Cytoplasm_AreaShape_Zernike_6_4 Cytoplasm_AreaShape_Zernike_6_6 Cytoplasm_AreaShape_Zernike_7_1 Cytoplasm_AreaShape_Zernike_7_3 Cytoplasm_AreaShape_Zernike_7_5 Cytoplasm_AreaShape_Zernike_7_7 Cytoplasm_AreaShape_Zernike_8_0 Cytoplasm_AreaShape_Zernike_8_2 Cytoplasm_AreaShape_Zernike_8_4 Cytoplasm_AreaShape_Zernike_8_6 Cytoplasm_AreaShape_Zernike_8_8 Cytoplasm_AreaShape_Zernike_9_1 Cytoplasm_AreaShape_Zernike_9_3 Cytoplasm_AreaShape_Zernike_9_5 Cytoplasm_AreaShape_Zernike_9_7 Cytoplasm_AreaShape_Zernike_9_9 ``` -------------------------------- ### Cells Object Feature Definitions Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns_without_image_columns.txt This section details the various quantitative features extracted from 'Cells' objects in image-based profiling. These features are grouped by their measurement category (e.g., AreaShape for morphology, Correlation for intensity relationships, Granularity for texture) and are fundamental for characterizing cellular phenotypes in large-scale screens. ```APIDOC Cells Object Features: AreaShape Metrics: Area BoundingBoxArea BoundingBoxMaximum_X BoundingBoxMaximum_Y BoundingBoxMinimum_X BoundingBoxMinimum_Y Center_X Center_Y Compactness Eccentricity EquivalentDiameter EulerNumber Extent FormFactor MajorAxisLength MaxFeretDiameter MaximumRadius MeanRadius MedianRadius MinFeretDiameter MinorAxisLength Orientation Perimeter Solidity Zernike_0_0 Zernike_1_1 Zernike_2_0 Zernike_2_2 Zernike_3_1 Zernike_3_3 Zernike_4_0 Zernike_4_2 Zernike_4_4 Zernike_5_1 Zernike_5_3 Zernike_5_5 Zernike_6_0 Zernike_6_2 Zernike_6_4 Zernike_6_6 Zernike_7_1 Zernike_7_3 Zernike_7_5 Zernike_7_7 Zernike_8_0 Zernike_8_2 Zernike_8_4 Zernike_8_6 Zernike_8_8 Zernike_9_1 Zernike_9_3 Zernike_9_5 Zernike_9_7 Zernike_9_9 Children Metrics: Cytoplasm_Count Correlation Metrics: Correlation_AGP_DNA Correlation_AGP_ER Correlation_AGP_Mito Correlation_AGP_RNA Correlation_DNA_ER Correlation_DNA_Mito Correlation_DNA_RNA Correlation_ER_Mito Correlation_ER_RNA Correlation_Mito_RNA K_AGP_DNA K_AGP_ER K_AGP_Mito K_AGP_RNA K_DNA_AGP K_DNA_ER K_DNA_Mito K_DNA_RNA K_ER_AGP K_ER_DNA K_ER_Mito K_ER_RNA K_Mito_AGP K_Mito_DNA K_Mito_ER K_Mito_RNA K_RNA_AGP K_RNA_DNA K_RNA_ER K_RNA_Mito Manders_AGP_DNA Manders_AGP_ER Manders_AGP_Mito Manders_AGP_RNA Manders_DNA_AGP Manders_DNA_ER Manders_DNA_Mito Manders_DNA_RNA Manders_ER_AGP Manders_ER_DNA Manders_ER_Mito Manders_ER_RNA Manders_Mito_AGP Manders_Mito_DNA Manders_Mito_ER Manders_Mito_RNA Manders_RNA_AGP Manders_RNA_DNA Manders_RNA_ER Manders_RNA_Mito Overlap_AGP_DNA Overlap_AGP_ER Overlap_AGP_Mito Overlap_AGP_RNA Overlap_DNA_ER Overlap_DNA_Mito Overlap_DNA_RNA Overlap_ER_Mito Overlap_ER_RNA Overlap_Mito_RNA RWC_AGP_DNA RWC_AGP_ER RWC_AGP_Mito RWC_AGP_RNA RWC_DNA_AGP RWC_DNA_ER RWC_DNA_Mito RWC_DNA_RNA RWC_ER_AGP RWC_ER_DNA RWC_ER_Mito RWC_ER_RNA RWC_Mito_AGP RWC_Mito_DNA RWC_Mito_ER RWC_Mito_RNA RWC_RNA_AGP RWC_RNA_DNA RWC_RNA_ER RWC_RNA_Mito Granularity Metrics: Granularity_10_AGP Granularity_10_DNA Granularity_10_ER Granularity_10_Mito Granularity_10_RNA Granularity_11_AGP Granularity_11_DNA Granularity_11_ER Granularity_11_Mito Granularity_11_RNA Granularity_12_AGP Granularity_12_DNA Granularity_12_ER Granularity_12_Mito Granularity_12_RNA Granularity_13_AGP Granularity_13_DNA Granularity_13_ER Granularity_13_Mito Granularity_13_RNA Granularity_14_AGP Granularity_14_DNA Granularity_14_ER Granularity_14_Mito Granularity_14_RNA Granularity_15_AGP Granularity_15_DNA Granularity_15_ER Granularity_15_Mito Granularity_15_RNA Granularity_16_AGP Granularity_16_DNA Granularity_16_ER Granularity_16_Mito ``` -------------------------------- ### Cells_Children Measurements Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns.txt Defines measurements related to the count of child objects (e.g., cytoplasm) associated with a parent cell object. This metric is useful for understanding the composition or substructure within identified cells. ```APIDOC Cells_Children: Cytoplasm: Count: int ``` -------------------------------- ### Cells_Correlation Measurements Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns.txt Defines various colocalization and correlation measurements between different cellular compartments or channels (AGP, DNA, ER, Mito, RNA). These metrics quantify the degree of spatial overlap or relationship between specified signals, using methods like Pearson's Correlation, K-factor, Manders' coefficients, Overlap, and RWC. ```APIDOC Cells_Correlation: Correlation: AGP_DNA: float AGP_ER: float AGP_Mito: float AGP_RNA: float DNA_ER: float DNA_Mito: float DNA_RNA: float ER_Mito: float ER_RNA: float Mito_RNA: float K: AGP_DNA: float AGP_ER: float AGP_Mito: float AGP_RNA: float DNA_AGP: float DNA_ER: float DNA_Mito: float DNA_RNA: float ER_AGP: float ER_DNA: float ER_Mito: float ER_RNA: float Mito_AGP: float Mito_DNA: float Mito_ER: float Mito_RNA: float RNA_AGP: float RNA_DNA: float RNA_ER: float RNA_Mito: float Manders: AGP_DNA: float AGP_ER: float AGP_Mito: float AGP_RNA: float DNA_AGP: float DNA_ER: float DNA_Mito: float DNA_RNA: float ER_AGP: float ER_DNA: float ER_Mito: float ER_RNA: float Mito_AGP: float Mito_DNA: float Mito_ER: float Mito_RNA: float RNA_AGP: float RNA_DNA: float RNA_ER: float RNA_Mito: float Overlap: AGP_DNA: float AGP_ER: float AGP_Mito: float AGP_RNA: float DNA_ER: float DNA_Mito: float DNA_RNA: float ER_Mito: float ER_RNA: float Mito_RNA: float RWC: AGP_DNA: float AGP_ER: float AGP_Mito: float AGP_RNA: float DNA_AGP: float DNA_ER: float DNA_Mito: float DNA_RNA: float ER_AGP: float ER_DNA: float ER_Mito: float ER_RNA: float Mito_AGP: float Mito_DNA: float Mito_ER: float Mito_RNA: float RNA_AGP: float RNA_DNA: float RNA_ER: float RNA_Mito: float ``` -------------------------------- ### Cytoplasm Feature Definitions for Image Analysis Source: https://github.com/broadinstitute/jump-profiling-recipe/blob/main/inputs/metadata/cpg0016_mandatory_feature_columns_without_image_columns.txt This section provides a comprehensive list of feature names related to the cytoplasm compartment, as typically generated by image analysis software like CellProfiler. These features quantify various aspects of the cytoplasm's morphology (AreaShape), colocalization with other cellular components (Correlation), and texture (Granularity). Each feature name uniquely identifies a specific measurement. ```APIDOC Cytoplasm_AreaShape_BoundingBoxMaximum_X Cytoplasm_AreaShape_BoundingBoxMaximum_Y Cytoplasm_AreaShape_BoundingBoxMinimum_X Cytoplasm_AreaShape_BoundingBoxMinimum_Y Cytoplasm_AreaShape_Center_X Cytoplasm_AreaShape_Center_Y Cytoplasm_AreaShape_Compactness Cytoplasm_AreaShape_Eccentricity Cytoplasm_AreaShape_EquivalentDiameter Cytoplasm_AreaShape_EulerNumber Cytoplasm_AreaShape_Extent Cytoplasm_AreaShape_FormFactor Cytoplasm_AreaShape_MajorAxisLength Cytoplasm_AreaShape_MaxFeretDiameter Cytoplasm_AreaShape_MaximumRadius Cytoplasm_AreaShape_MeanRadius Cytoplasm_AreaShape_MedianRadius Cytoplasm_AreaShape_MinFeretDiameter Cytoplasm_AreaShape_MinorAxisLength Cytoplasm_AreaShape_Orientation Cytoplasm_AreaShape_Perimeter Cytoplasm_AreaShape_Solidity Cytoplasm_AreaShape_Zernike_0_0 Cytoplasm_AreaShape_Zernike_1_1 Cytoplasm_AreaShape_Zernike_2_0 Cytoplasm_AreaShape_Zernike_2_2 Cytoplasm_AreaShape_Zernike_3_1 Cytoplasm_AreaShape_Zernike_3_3 Cytoplasm_AreaShape_Zernike_4_0 Cytoplasm_AreaShape_Zernike_4_2 Cytoplasm_AreaShape_Zernike_4_4 Cytoplasm_AreaShape_Zernike_5_1 Cytoplasm_AreaShape_Zernike_5_3 Cytoplasm_AreaShape_Zernike_5_5 Cytoplasm_AreaShape_Zernike_6_0 Cytoplasm_AreaShape_Zernike_6_2 Cytoplasm_AreaShape_Zernike_6_4 Cytoplasm_AreaShape_Zernike_6_6 Cytoplasm_AreaShape_Zernike_7_1 Cytoplasm_AreaShape_Zernike_7_3 Cytoplasm_AreaShape_Zernike_7_5 Cytoplasm_AreaShape_Zernike_7_7 Cytoplasm_AreaShape_Zernike_8_0 Cytoplasm_AreaShape_Zernike_8_2 Cytoplasm_AreaShape_Zernike_8_4 Cytoplasm_AreaShape_Zernike_8_6 Cytoplasm_AreaShape_Zernike_8_8 Cytoplasm_AreaShape_Zernike_9_1 Cytoplasm_AreaShape_Zernike_9_3 Cytoplasm_AreaShape_Zernike_9_5 Cytoplasm_AreaShape_Zernike_9_7 Cytoplasm_AreaShape_Zernike_9_9 Cytoplasm_Correlation_Correlation_AGP_DNA Cytoplasm_Correlation_Correlation_AGP_ER Cytoplasm_Correlation_Correlation_AGP_Mito Cytoplasm_Correlation_Correlation_AGP_RNA Cytoplasm_Correlation_Correlation_DNA_ER Cytoplasm_Correlation_Correlation_DNA_Mito Cytoplasm_Correlation_Correlation_DNA_RNA Cytoplasm_Correlation_Correlation_ER_Mito Cytoplasm_Correlation_Correlation_ER_RNA Cytoplasm_Correlation_Correlation_Mito_RNA Cytoplasm_Correlation_K_AGP_DNA Cytoplasm_Correlation_K_AGP_ER Cytoplasm_Correlation_K_AGP_Mito Cytoplasm_Correlation_K_AGP_RNA Cytoplasm_Correlation_K_DNA_AGP Cytoplasm_Correlation_K_DNA_ER Cytoplasm_Correlation_K_DNA_Mito Cytoplasm_Correlation_K_DNA_RNA Cytoplasm_Correlation_K_ER_AGP Cytoplasm_Correlation_K_ER_DNA Cytoplasm_Correlation_K_ER_Mito Cytoplasm_Correlation_K_ER_RNA Cytoplasm_Correlation_K_Mito_AGP Cytoplasm_Correlation_K_Mito_DNA Cytoplasm_Correlation_K_Mito_ER Cytoplasm_Correlation_K_Mito_RNA Cytoplasm_Correlation_K_RNA_AGP Cytoplasm_Correlation_K_RNA_DNA Cytoplasm_Correlation_K_RNA_ER Cytoplasm_Correlation_K_RNA_Mito Cytoplasm_Correlation_Manders_AGP_DNA Cytoplasm_Correlation_Manders_AGP_ER Cytoplasm_Correlation_Manders_AGP_Mito Cytoplasm_Correlation_Manders_AGP_RNA Cytoplasm_Correlation_Manders_DNA_AGP Cytoplasm_Correlation_Manders_DNA_ER Cytoplasm_Correlation_Manders_DNA_Mito Cytoplasm_Correlation_Manders_DNA_RNA Cytoplasm_Correlation_Manders_ER_AGP Cytoplasm_Correlation_Manders_ER_DNA Cytoplasm_Correlation_Manders_ER_Mito Cytoplasm_Correlation_Manders_ER_RNA Cytoplasm_Correlation_Manders_Mito_AGP Cytoplasm_Correlation_Manders_Mito_DNA Cytoplasm_Correlation_Manders_Mito_ER Cytoplasm_Correlation_Manders_Mito_RNA Cytoplasm_Correlation_Manders_RNA_AGP Cytoplasm_Correlation_Manders_RNA_DNA Cytoplasm_Correlation_Manders_RNA_ER Cytoplasm_Correlation_Manders_RNA_Mito Cytoplasm_Correlation_Overlap_AGP_DNA Cytoplasm_Correlation_Overlap_AGP_ER Cytoplasm_Correlation_Overlap_AGP_Mito Cytoplasm_Correlation_Overlap_AGP_RNA Cytoplasm_Correlation_Overlap_DNA_ER Cytoplasm_Correlation_Overlap_DNA_Mito Cytoplasm_Correlation_Overlap_DNA_RNA Cytoplasm_Correlation_Overlap_ER_Mito Cytoplasm_Correlation_Overlap_ER_RNA Cytoplasm_Correlation_Overlap_Mito_RNA Cytoplasm_Correlation_RWC_AGP_DNA Cytoplasm_Correlation_RWC_AGP_ER Cytoplasm_Correlation_RWC_AGP_Mito Cytoplasm_Correlation_RWC_AGP_RNA Cytoplasm_Correlation_RWC_DNA_AGP Cytoplasm_Correlation_RWC_DNA_ER Cytoplasm_Correlation_RWC_DNA_Mito Cytoplasm_Correlation_RWC_DNA_RNA Cytoplasm_Correlation_RWC_ER_AGP Cytoplasm_Correlation_RWC_ER_DNA Cytoplasm_Correlation_RWC_ER_Mito Cytoplasm_Correlation_RWC_ER_RNA Cytoplasm_Correlation_RWC_Mito_AGP Cytoplasm_Correlation_RWC_Mito_DNA Cytoplasm_Correlation_RWC_Mito_ER Cytoplasm_Correlation_RWC_Mito_RNA Cytoplasm_Correlation_RWC_RNA_AGP Cytoplasm_Correlation_RWC_RNA_DNA Cytoplasm_Correlation_RWC_RNA_ER Cytoplasm_Correlation_RWC_RNA_Mito Cytoplasm_Granularity_10_AGP Cytoplasm_Granularity_10_DNA Cytoplasm_Granularity_10_ER Cytoplasm_Granularity_10_Mito Cytoplasm_Granularity_10_RNA Cytoplasm_Granularity_11_AGP Cytoplasm_Granularity_11_DNA Cytoplasm_Granularity_11_ER Cytoplasm_Granularity_11_Mito Cytoplasm_Granularity_11_RNA Cytoplasm_Granularity_12_AGP Cytoplasm_Granularity_12_DNA Cytoplasm_Granularity_12_ER Cytoplasm_Granularity_12_Mito ```