compare_pathways: Use SCPA to compare gene sets

In jackbibby1/SCPA: Single Cell Pathway Analysis

Use SCPA to compare gene sets

Description

This function takes an input of samples and pathways to compare gene set perturbations over different conditions with SCPA.

Usage

compare_pathways(
  samples,
  pathways,
  downsample = 500,
  min_genes = 15,
  max_genes = 500,
  parallel = FALSE,
  cores = NULL
)

Arguments

samples	List of samples, each supplied as an expression matrix with cells in columns and genes in rows.
pathways	Pathways and their genes with each pathway in a separate list. For formatting of gene lists, see documentation at https://jackbibby1.github.io/SCPA/articles/using_gene_sets.html
downsample	Option to downsample cell numbers. Defaults to 500 cells per condition. If a population has < 500 cells, all cells from that condition are used.
min_genes	Gene sets with fewer than this number of genes will be excluded
max_genes	Gene sets with more than this number of genes will be excluded
parallel	Should parallel processing be used?
cores	The number of cores used for parallel processing

Value

Statistical results from the SCPA analysis. The qval should be the primary metric that is used to interpret pathway differences i.e. a higher qval translates to larger pathway differences between conditions. If only two samples are provided, a fold change (FC) enrichment score will also be calculated. The FC statistic is generated from a running sum of mean changes in gene expression from all genes of the pathway. It's calculated from average pathway expression in population1 - population2, so a negative FC means the pathway is higher in population2.

Examples

## Not run: 
scpa_result <- compare_pathways(
     list(sample1, sample2, sample3),
     pathways = pathways)

## End(Not run)

jackbibby1/SCPA documentation built on May 14, 2024, 7:16 a.m.