catcheR_modules - Gene Modules

The catcheR_modules function uses Monocle to identify gene modules—groups of genes whose expression varies across different perturbation groups (e.g., genes, shRNAs, or clones). These modules can be used to further analyze expression trends and cumulative frequency distributions.

Step-by-step

  1. Run catcheR_modules:

    catcheR_modules(
    group = c("docker", "sudo"),
    folder,
    cds,
    resolution = 1e-2
)

Example usage:

catcheR_modules(
    group = "docker",
    folder = "/30tb/3tb/data/ratto/testing/",
    cds = "processed_cds.RData"
)

The resolution parameter is passed to Monocle’s find_gene_modules function.

  • A higher resolution will return more, smaller modules

  • A lower resolution will return fewer, broader modules

Outputs

catcheR_modules generates the following outputs:

  1. gene_modules.csv

    • Lists the genes belonging to each identified module.

  2. Heatmap plots

    • Show Z-scores of module expression across perturbation groups.

    • Includes either all modules or the top 10 most variable modules.

    _images/pheatmap_gene_module_top10.pdf _images/pheatmap_shRNA_module_top10.pdf

  3. modules_cells/ folder

    • Contains tables with aggregated expression values per cell for each gene module.

    • These CSV files can be used as input to catcheR_pseudotime, allowing the analysis of cumulative frequency based on module expression rather than pseudotime.

Integration with catcheR_pseudotime

To analyze cumulative module expression trends, you can run catcheR_pseudotime using the CSV files in the modules_cells folder as pseudotime input.