fibroblast-perturb-seq-analysis

This code supplements the upcoming publication by Aguado Alvaro, Garitano, Esser-Skala et al.

Folders

(Not all of these folders appear in the git repository.)

• data_generated: output files generated by the scripts in this repository
• data_raw: raw input data
• metadata: additional required data
• plots: generated plots
• renv: R environment data
• scripts_python: Python scripts
• scripts_r: R scripts

Download data

Create a folder data_raw that will contain raw data in the following subfolders:

• rna: Download GSE261783_RAW.tar from GEO Series GSE261783 and extract all files.
• signatures:
  • markers_Forte.xlsx: Table S3 from Forte et al (https://doi.org/10.1016/j.celrep.2020.02.008)
  • markers_Koenig.xlsx: Table S27 from Koenig et al (https://doi.org/10.1038/s44161-022-00028-6)
  • markers_Buechler.xlsx: Table S5 from Buechler et al (https://doi.org/10.1038/s41586-021-03549-5)

Optionally, obtain intermediary data: Extract the contents of data_generated.tgz from Zenodo repository https://doi.org/XXX to folder data_generated.

Analysis workflow

Python

TBD

R

Run the following scripts in the folder scripts_r in order to run the R analysis pipeline. utils.R contains auxiliary functions and definitions required by several other scripts.

1. create_sce.R: interface to Python scripts; creates a SingleCellObject with the same data and metadata as the AnnData object
2. plot_signatures.R: fibroblast signatures (figures 3f, g and S3)
3. plot_ko_distribution.R: UMAPs with knockout distribution (figures 4a and S6)
4. plot_ko_enrichment.R: summary of knockout enrichment (figures 4b and S7)
5. run_gsea.R: perform gene set enrichment analysis
6. plot_gsea.R: plot GSEA results (figure 4d)
7. plot_genes.R: Volcano plot (figure 5f)
8. plot_ntc_depletion.R: NTC depletion (figure S7x)