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Generate pathway score using gene set enrichement analysis

Source: R/run_scfeatures.R
run_pathway_gsva.Rd

This function calculates pathway scores for a given input dataset and gene set using gene set enrichment analysis (GSVA). It supports scRNA-seq, spatial proteomics and spatial transcriptomics. It currently supports two pathway analysis methods: ssgsea and aucell. 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_gsva(
  data,
  method = "aucell",
  geneset = NULL,
  species = "Homo sapiens",
  type = "scrna",
  subsample = TRUE,
  ncores = 1
)

Arguments

data

A list object containing data matrix and celltype and sample vector.

method

Type of pathway analysis method, currently support ssgsea and aucell, default to aucell

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("CAPN1", ...), "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".

subsample

Whether to subsample, either TRUE or FALSE. For larger datasets (eg, over 30,000 cells), the subsample function can be used to increase speed.

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 gene set enrichment score of a given pathway in cells from a given celltype.

Examples


utils::data("example_scrnaseq" , package = "scFeatures")
data <- example_scrnaseq[, 1:20]
celltype <- data$celltype
sample <- data$sample
data <- data@assays$RNA@data

alldata <- scFeatures:::formatData(data = data, celltype = celltype, sample = sample )

feature_pathway_gsva <- run_pathway_gsva(
    alldata,
    geneset = NULL, species = "Homo sapiens",
    type = "scrna", subsample = FALSE, ncores = 1
 )
#> Warning: nCores is no longer used. It will be deprecated in the next AUCell version.
#> Warning: Using only the first 176.15 genes (aucMaxRank) to calculate the AUC.
#> Warning: The following gene sets will be excluded from the analysis(less than 20% of their genes are available):
#> HALLMARK_ANGIOGENESIS, HALLMARK_APICAL_JUNCTION, HALLMARK_APICAL_SURFACE, HALLMARK_BILE_ACID_METABOLISM, HALLMARK_COAGULATION, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_ESTROGEN_RESPONSE_EARLY, HALLMARK_ESTROGEN_RESPONSE_LATE, HALLMARK_GLYCOLYSIS, HALLMARK_HEDGEHOG_SIGNALING, HALLMARK_KRAS_SIGNALING_DN, HALLMARK_KRAS_SIGNALING_UP, HALLMARK_MYOGENESIS, HALLMARK_NOTCH_SIGNALING, HALLMARK_PANCREAS_BETA_CELLS, HALLMARK_SPERMATOGENESIS, HALLMARK_UV_RESPONSE_DN, HALLMARK_WNT_BETA_CATENIN_SIGNALING, HALLMARK_XENOBIOTIC_METABOLISM