Fast calculation of singscore

Calculates single-sample enrichment singscore (Fouratan et al., 2018) using plaid back-end. The computation is 10-100x faster than the original code.

Usage

replaid.sing(X, matG, assay = "logcounts", min.genes = 5, max.genes = 500)

Arguments

X

Gene or protein expression matrix. Generally log transformed. See details. Genes on rows, samples on columns. Also accepts SummarizedExperiment or SingleCellExperiment objects.

matG

Gene sets sparse matrix. Genes on rows, gene sets on columns. Also accepts BiocSet objects or GMT lists.

assay

Character: assay name for Bioconductor objects. Default "logcounts".

min.genes

Integer: minimum genes per gene set. Default 5.

max.genes

Integer: maximum genes per gene set. Default 500.

Value

Matrix of single-sample singscore enrichment scores. Gene sets on rows, samples on columns.

Details

Computing the singscore requires to compute the ranks of the expression matrix. We have wrapped this in a single convenience function.

We have extensively compared the results of replaid.sing and from the original singscore R package and we showed identical result in the score, logFC and p-values.

Examples

# Create example expression matrix
set.seed(123)
X <- matrix(rnorm(500), nrow = 50, ncol = 10)
rownames(X) <- paste0("GENE", 1:50)
colnames(X) <- paste0("Sample", 1:10)

# Create example gene sets
gmt <- list(
  "Pathway1" = paste0("GENE", 1:15),
  "Pathway2" = paste0("GENE", 10:25)
)
matG <- gmt2mat(gmt)

# Compute singscore
scores <- replaid.sing(X, matG)
print(scores[1:2, 1:5])
#>              Sample1     Sample2      Sample3    Sample4    Sample5
#> Pathway2 -0.03789474 -0.06315789  0.044210526 0.03368421 0.01684211
#> Pathway1  0.03000000 -0.03777778 -0.003333333 0.03888889 0.06888889