Fast calculation of ssGSEA

Calculates single-sample enrichment singscore (Barbie et al., 2009; Hänzelmann et al., 2013) using plaid back-end. The computation is 10-100x faster than the original code.

Usage

replaid.ssgsea(
  X,
  matG,
  alpha = 0,
  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.

alpha

Weighting factor for exponential weighting of ranks

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 ssGSEA enrichment scores. Gene sets on rows, samples on columns.

Details

Computing ssGSEA score requires to compute the ranks of the expression matrix and weighting of the ranks. We have wrapped this in a single convenience function.

We have extensively compared the results of replaid.ssgsea and from the original GSVA R package and we showed highly similar results in the score, logFC and p-values. For alpha=0 we obtain exact results, for alpha>0 the results are highly similar but not exactly the same.

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 ssGSEA scores (alpha = 0)
scores <- replaid.ssgsea(X, matG, alpha = 0)
print(scores[1:2, 1:5])
#>            Sample1  Sample2   Sample3   Sample4   Sample5
#> Pathway2 -9.375109 -9.39161 -9.313848 -9.385681 -9.390127
#> Pathway1 -9.307214 -9.36623 -9.361391 -9.380477 -9.338080

# Compute ssGSEA scores with weighting (alpha = 0.25)
scores_weighted <- replaid.ssgsea(X, matG, alpha = 0.25)
print(scores_weighted[1:2, 1:5])
#>            Sample1   Sample2   Sample3   Sample4   Sample5
#> Pathway2 -9.316151 -9.330741 -9.249503 -9.326221 -9.331940
#> Pathway1 -9.249067 -9.308255 -9.298057 -9.317977 -9.280176