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Linear Model Coefficient Analysis #26

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merged 6 commits into from
Oct 25, 2023
Merged

Linear Model Coefficient Analysis #26

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453a370
update gitignore
jenna-tomkinson Oct 20, 2023
c199402
update README to include link to ML repo
jenna-tomkinson Oct 20, 2023
f1c2373
add LM results and visualization
jenna-tomkinson Oct 20, 2023
05d93ed
add nbconverted scripts
jenna-tomkinson Oct 20, 2023
48ac4b8
edits to documentation (Erik's suggestion)
jenna-tomkinson Oct 25, 2023
8f68bdc
nbconvert notebook to r script
jenna-tomkinson Oct 25, 2023
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36 changes: 20 additions & 16 deletions .gitignore
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Original file line number Diff line number Diff line change
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# ignore all plates as they are downloaded from figshare in instructions
0.download_data/Plate_1
0.download_data/Plate_2
0.download_data/Plate_3
0.download_data/Plate_3_prime
0.download_data/Plate_4

# ignore pycache
__pycache__

# ignore corrected plates if they are put into a separate folder
Corrected_Images

# ignore corrected plates images (like in download data)
1.cellprofiler_ic/Corrected_Plate_1
1.cellprofiler_ic/Corrected_Plate_2
1.cellprofiler_ic/Corrected_Plate_3
1.cellprofiler_ic/Corrected_Plate_3_prime
1.cellprofiler_ic/Corrected_Plate_4
# ignore VSCode files
.vscode

# ignore log files
*logs
Expand All @@ -27,3 +13,21 @@ Corrected_Images

# ignore jupyter files
.ipynb_checkpoints/

# ignore zip files (large)
*.zip

# ignore all plates as they are downloaded from figshare in instructions
0.download_data/Plate_1
0.download_data/Plate_2
0.download_data/Plate_3
0.download_data/Plate_3_prime
0.download_data/Plate_4

# ignore corrected plates images as they are generated in IC notebook
Corrected_Images/
Corrected_Plate_1
Corrected_Plate_2
Corrected_Plate_3
Corrected_Plate_3_prime
Corrected_Plate_4
21 changes: 21 additions & 0 deletions 4.analyze_data/README.md
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# Analyze NF1 data

In this module, we perform analysis on the data to uncover answers to our goals as specified in [the main README](../README.md).


The analyses we perform are:

1. Linear modeling per feature in each plate. The beta coefficients for WT treatment contribution and cell count contribution are plotted as a scatter plot.

# R visualization environment

To visualize results, we use R to create plots.
This means that we have a separate environment from the main Python environment specifically in this module that has an R kernel.
To install this environment, perform the following steps:

```bash
# Make sure you are in the 4.analyze_data module to access the env file
cd 4.analyze_data
# Run this command in terminal to create the conda environment
conda env create -f r_analysis_env.yml
```
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