catcheR_pseudotime - Pseudotime

Since Monocle pseudotime calculation requires interactive root cell selection, the first step must be performed manually within monocle3.

Step-by-step

1. Load processed_cds.RData in R, then calculate pseudotime and plot it:

   library(monocle3)
   cds <- order_cells(cds)
   pt = as.data.frame(pseudotime(cds))
   names(pt) = c("pseudotime")
   write.csv(pt, paste0(dir, "/pseudotime.csv"))
   
   plot_cells(cds,
              color_cells_by = "pseudotime",
              label_cell_groups = FALSE,
              label_leaves = FALSE,
              label_branch_points = FALSE,
              graph_label_size = 1.5)
   

2. Your working folder should now contain:

   • Outputs from catcheR_load

   • The pseudotime.csv file created above

3. Run catcheR_pseudotime:

   catcheR_pseudotime(
       group = c("docker", "sudo"),
       folder,
       cds,
       pseudotime,
       all = FALSE
   )
   

Example usage:

catcheR_pseudotime(
    group = "docker",
    folder = "/path/to/working/folder/",
    cds = "processed_cds.RData",
    pseudotime = "pseudotime.csv"
)

The all argument is a logical value:

• FALSE (default): compare target vs. all controls together

• TRUE: compare target vs. each control separately

Outputs

The following files are generated by catcheR_pseudotime:

1. cumulative_frequency_pseudotime.pdf - Plots showing the number of cells at each pseudotime point, grouped by gene, shRNA, and clone.

   

2. ks_statistics_*.csv - Kolmogorov-Smirnov test results comparing cumulative frequencies along pseudotime between knockdown and controls.

3. Volcano plots - Visualize significance and effect size (fold change) of the Kolmogorov-Smirnov test, across different comparisons.

   

4. correlated_pseudotime_gene_exp.pdf - UMAP plot highlighting genes whose expression is most correlated with pseudotime.