This function performs statistical testing for differential enrichment using plaid
Usage
dual_test(
X,
y,
G,
gsetX = NULL,
fc.test = "cor",
pv1 = NULL,
pv2 = NULL,
metap.method = "stouffer",
sort.by = "p.dual"
)Arguments
- X
Matrix of log expression value
- y
Vector of 0s and 1s indicating group
- G
Sparse matrix of gene sets. Non-zero entry indicates gene/feature is part of gene sets. Features on rows, gene sets on columns.
- gsetX
Gene set score matrix which is output of
plaid(). Can be NULL in that case it will be recomputed from X and G (default required).- fc.test
Method for fold change testing ("ztest", "rankcor", "cor"). Default "cor".
- pv1
Pre-computed p-values from fold change test. If NULL, will be computed based on fc.test.
- pv2
Pre-computed p-values from single sample test. If NULL, will be computed using gset_ttest.
- metap.method
Method for combining p-values ("stouffer" or "fisher"). Default "stouffer".
- sort.by
Column name to sort results by ("p.dual", "gsetFC", "p.fc", "p.ss"). Default "p.dual".
Value
Data frame with columns: gsetFC (gene set fold change), size (gene set size), p.fc (p-value from fold change test), p.ss (p-value from single sample test), p.dual (combined p-value), and q.dual (FDR-adjusted combined p-value).
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)
)
G <- gmt2mat(gmt)
# Perform dual test
results <- dual_test(X, y, G)
#> computing gsetX using plaid. please precompute for efficiency.
print(head(results))
#> 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
# Perform dual test with correlation test
results_cor <- dual_test(X, y, G, fc.test = "cor")
#> computing gsetX using plaid. please precompute for efficiency.
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