Reimplementation of dualGSEA (Bull et al., 2024) but defaults with replaid backend. For the preranked test we still use fgsea. Should be much faster than original using fgsea + GSVA::ssGSEA.

Reimplementation of dualGSEA (Bull et al., 2024) but defaults with replaid backend. For the preranked test we still use fgsea. Should be much faster than original using fgsea + GSVA::ssGSEA.

Usage

dualGSEA(
  X,
  y,
  gmt,
  matG = NULL,
  fc.test = "fgsea",
  gsea.method = "replaid.ssgsea"
)

Arguments

X

Expression matrix with genes on rows and samples on columns

y

Binary vector (0/1) indicating group membership

gmt

List of gene sets in GMT format

matG

Optional sparse matrix of gene sets (will be computed from gmt if NULL)

fc.test

Method for fold change testing ("fgsea", "ztest", "rankcor", "cor")

gsea.method

Method for single-sample enrichment ("replaid.ssgsea", "replaid.gsva", "ssgsea", "gsva")

Value

Data frame with results from dual testing including fold changes, p-values, and combined statistical measures.

Examples

# Create example expression matrix
set.seed(123)
X <- matrix(rnorm(1000), nrow = 100, ncol = 20)
rownames(X) <- paste0("GENE", 1:100)
colnames(X) <- paste0("Sample", 1:20)

# Create binary group vector
y <- rep(c(0, 1), each = 10)

# Create example gene sets
gmt <- list(
  "Pathway1" = paste0("GENE", 1:20),
  "Pathway2" = paste0("GENE", 15:35),
  "Pathway3" = paste0("GENE", 30:50)
)

# Perform dualGSEA with correlation test (fast method)
results_cor <- dualGSEA(X, y, gmt, fc.test = "cor", gsea.method = "replaid.gsva")
#> fc.test using cor
#> computing matG
#> single-sample testing using replaid.gsva
print(head(results_cor))
#>          gsetFC size p.fc p.ss p.dual q.dual
#> Pathway2      0   21    1    1      1      1
#> Pathway3      0   21    1    1      1      1
#> Pathway1      0   20    1    1      1      1

# \donttest{
# Perform dualGSEA with fgsea (requires fgsea package)
if (requireNamespace("fgsea", quietly = TRUE)) {
  results <- dualGSEA(X, y, gmt, fc.test = "fgsea", gsea.method = "replaid.ssgsea")
  print(head(results))
}
#> fc.test using fgsea
#> Warning: For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
#> computing matG
#> single-sample testing using replaid.ssgsea
#>          gsetFC size        p.fc p.ss p.dual q.dual
#> Pathway2      0   21 0.005633866    1      1      1
#> Pathway3      0   21 0.005633866    1      1      1
#> Pathway1      0   20 0.005391312    1      1      1
# }