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…odule Edit 4_processing_features module
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Plate,Well,Gene Identifier,Gene Symbol,Genotype,Channels | ||
1,C6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,C7,ENSG00000196712,NF1,Het,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,D6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,D7,ENSG00000196712,NF1,Het,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,E6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,E7,ENSG00000196712,NF1,Het,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,F6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,F7,ENSG00000196712,NF1,Het,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
Plate,Well,Gene Identifier,Gene Symbol,Genotype,Channels | ||
1,C6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,C7,ENSG00000196712,NF1,Null,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,D6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,D7,ENSG00000196712,NF1,Null,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,E6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,E7,ENSG00000196712,NF1,Null,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,F6,ENSG00000196712,NF1,WT,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) | ||
1,F7,ENSG00000196712,NF1,Null,DAPI (nuclei); GFP (endoplasmic reticulum); RFP (actin/cytoplasm) |
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#!/bin/bash | ||
jupyter nbconvert --to python extract_single_cell_features.ipynb | ||
python extract_single_cell_features.py |
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# 4. Processing Extracted Single Cell Features | ||
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In this module, we present our pipeline for processing outputted `.sqlite` file with single cell features from CellProfiler. | ||
The processed features are saved into compressed `.csv.gz` for use during statistical analysis. | ||
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## Pycytominer | ||
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We use [Pycytominer](https://github.com/cytomining/pycytominer) to perform the aggregation, merging, and normalization of the NF1 single cell features. | ||
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For more information regarding the functions that we used, please see [the documentation](https://pycytominer.readthedocs.io/en/latest/pycytominer.cyto_utils.html#pycytominer.cyto_utils.cells.SingleCells.merge_single_cells) from the Pycytominer team. | ||
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### Normalization | ||
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CellProfiler features can display a variety of distributions across cells. | ||
To facilitate analysis, we standardize all features (z-score) to the same scale. | ||
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--- | ||
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## Step 1: Setup Processing Feature Environment | ||
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### Step 1a: Create Environment | ||
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Make sure you are in the `4_processing_features` directory before performing the below command. | ||
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```sh | ||
# Run this command in terminal to create the conda environment | ||
conda env create -f 4.processing_features.yml | ||
``` | ||
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## Step 2: Normalize Single Cell Features | ||
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### Step 2a: Set Up Paths | ||
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Within the [extract_single_cell_features.ipynb](4_processing_features/extract_single_cell_features.ipynb) notebook, you can chnage the paths to reflect the local paths or names for your machine (***IF* you changed anything from the original pipeline**) for the various parameters (e.g. CellProfiler directory, output directory, path to sqlite file, etc.) | ||
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### Step 2b: Run Extract Single Cell Features | ||
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Using the code below, run the notebook to extract and normalize single cell features from CellProfiler. | ||
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```bash | ||
# Run this script in terminal | ||
bash 4.extract_sc_features.sh | ||
``` |
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