Generate pathway score using expression level — run_pathway

This function calculates pathway scores for a given dataset and gene set using gene expression levels. It supports scRNA-seq, spatial transcriptomics and spatial proteomics and spatial transcriptomics). By default, it uses the 50 hallmark gene sets from msigdb. Alternatively, users can provide their own gene sets of interest in a list format.

Usage

run_pathway_mean(
  data,
  geneset = NULL,
  species = "Homo sapiens",
  type = "scrna",
  ncores = 1
)

Arguments

data: A list object containing data matrix and celltype and sample vector.
geneset: By default (when the geneset argument is not specified), we use the 50 hallmark gene set from msigdb. The users can also provide their geneset of interest in a list format, with each list entry containing a vector of the names of genes in a gene set. eg, geneset <- list("pathway_a" = c("CANS1", ...), "pathway_b" = c("PEX6"))
species: Whether the species is "Homo sapiens" or "Mus musculus". Default is "Homo sapiens".
type: The type of dataset, either "scrna", "spatial_t", or "spatial_p".
ncores: Number of cores for parallel processing.

Value

a dataframe of samples x features The features are in the form of pathway 1 celltype a, pathway 2 celltype b ... etc, with the number representing the averaged expression of a given pathway in cells from a given celltype.

Examples


utils::data("example_scrnaseq" , package = "scFeatures")
data <- example_scrnaseq[1:500, 1:200]
celltype <- data$celltype
sample <- data$sample
data <- data@assays$RNA@data

alldata <- scFeatures:::formatData(data = data, celltype = celltype, sample = sample )
 feature_pathway_mean <- run_pathway_mean(
    alldata ,
    geneset = NULL, species = "Homo sapiens",
    type = "scrna", ncores = 1
 )