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Gene set-level transcriptional perturbation analysis. Converts differential expression p-values into per-gene z-scores and tests whether each gene set shows stronger perturbation than expected under a size-matched permutation null. Uses GPD tail extrapolation for extreme-value p-values.
# install.packages("devtools")
devtools::install_github("LHJLab/DSGE")library(DSGE)
library(org.Hs.eg.db)
# Build pathway-gene map from Bioconductor OrgDb
pw <- get_pathway_genes_db(org.Hs.eg.db, min_size = 10)
# Read DE results (any tool — DESeq2, edgeR, limma, Seurat, etc.)
res <- read.csv("your_de_results.csv")
# Required columns: pvalue, gene symbol
# Optional: baseMean/AveExpr (for expression filtering), log2FoldChange (for direction)
# Run pathway analysis
result <- pathway_dsge(pw, pvalue = res$pvalue, base_mean = res$AveExpr,
gene_names = res$gene, gene_id_col = "db_object_symbol",
n_perm = 100000, n_cores = 4,
directional = TRUE, direction_vec = res$log2FoldChange,
return_null = TRUE)
# Significant pathways
head(result$table[result$table$p_adj < 0.05, c("go_id", "go_name", "dsge_std", "p_adj")])
# Plot null distribution for selected pathways
plot_dsge(result, go_ids = c("GO:0007264", "GO:0018108"))Key parameters: min_size, max_size,
n_perm, use_gpd, directional
(with direction_vec), nds_top_frac ,
n_cores.
For detailed documentation with worked examples, parameter references, and diagnostic plots, see the DSGE Wiki.
MIT
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