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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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Normalization of the data is important because there will be variety in the shapes of distributions. To make statsitical analysis easier, we normalize using standardized method. | ||
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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 for feature extraction | ||
conda env create -f 4.processing_features.yml | ||
``` |
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WellRow,WellCol,well_position,gene_name,genotype | ||
C,6,C6,NF1,WT | ||
C,7,C7,NF1,Het | ||
D,6,D6,NF1,WT | ||
D,7,D7,NF1,Het | ||
E,6,E6,NF1,WT | ||
E,7,E7,NF1,Het | ||
F,6,F6,NF1,WT | ||
F,7,F7,NF1,Het | ||
WellRow,WellCol,well_position,gene_name,genotype | ||
C,6,C6,NF1,WT | ||
C,7,C7,NF1,Null | ||
D,6,D6,NF1,WT | ||
D,7,D7,NF1,Null | ||
E,6,E6,NF1,WT | ||
E,7,E7,NF1,Null | ||
F,6,F6,NF1,WT | ||
F,7,F7,NF1,Null |