This function takes a Seurat object as an input, and compares gene sets over specified conditions/populations.
compare_seurat(
seurat_object,
assay = "RNA",
group1 = NULL,
group1_population = NULL,
group2 = NULL,
group2_population = NULL,
pathways,
downsample = 500,
min_genes = 15,
max_genes = 500,
parallel = FALSE,
cores = NULL
)Seurat object with populations defined in the meta data
Assay to pull expression data from
First comparison group as defined by meta data in Seurat object e.g. cell_type
Populations within group1 to compare e.g. c("t_cell", "b_cell")
Second comparison group as defined by column names in Seurat object e.g. hour
Population within group2 to compare e.g. 24
Pathway gene sets 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
Option to downsample cell numbers. Defaults to 500 cells per condition. If a population has < 500 cells, all cells from that condition are used.
Gene sets with fewer than this number of genes will be excluded
Gene sets with more than this number of genes will be excluded
Should parallel processing be used?
The number of cores used for parallel processing
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 output 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.
if (FALSE) {
scpa_out <- compare_sce(
group1 = "cell",
group1_population = c("t_cell", "b_cell"),
group2 = "hour",
group2_population = c("24"),
pathways = pathways)
}