Pathway enrichment of meta-analysis results

Runs pre-ranked gene set enrichment analysis (GSEA, via fgsea) or over-representation analysis (ORA, hypergeometric test) on the gene-level meta-analysis.

Usage

mx_pathway_meta(
  meta_result,
  db = c("Hallmarks", "Reactome", "KEGG", "WikiPathways", "GO_BP"),
  method = c("GSEA", "ORA"),
  gene_sets = NULL,
  padj_threshold = 0.05,
  lfc_threshold = 0,
  min_size = 15,
  max_size = 500,
  seed = 1L,
  ...
)

Arguments

meta_result: A metaXpressResult from mx_meta.
db: Gene set collection passed to mx_gene_sets. Ignored when gene_sets is supplied.
method: "GSEA" (default) or "ORA".
gene_sets: Optional named list of gene sets (e.g. from mx_gene_sets or a GMT file) whose IDs match meta_table$gene_id.
padj_threshold, lfc_threshold: ORA only: thresholds on meta_padj and |meta_log2FC| defining the gene lists. Defaults: 0.05 and 0.
min_size, max_size: Gene set size limits after restricting to genes in the analysis. Defaults: 15 and 500.
seed: Integer seed for the fgsea permutation sampling, for reproducibility. The global RNG state is restored afterwards.
...: Further arguments passed to fgsea::fgsea().

Value

The meta_result with pathway_result filled:

GSEA: pathway_id, set_size, NES, pvalue, padj, leading_edge.
ORA: pathway_id, direction (up/down), set_size, n_overlap, n_list, fold_enrichment, pvalue, padj, overlap_genes.

Details

GSEA ranks genes by the signed meta-analysis z-score $\mathrm{sign}(\beta)\,\Phi^{-1}(1 - p/2)$, which uses both effect size and its precision; this avoids arbitrary significance cut-offs and is the recommended default. ORA tests up- and down-regulated genes separately against the universe of all genes in the meta-analysis (not the whole genome), as recommended to avoid inflated enrichment.

References

Subramanian, A. et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS, 102(43), 15545–15550. doi:10.1073/pnas.0506580102

Korotkevich, G. et al. (2016) Fast gene set enrichment analysis. bioRxiv. doi:10.1101/060012

Timmons, J.A., Szkop, K.J. & Gallagher, I.J. (2015) Multiple sources of bias confound functional enrichment analysis of global -omics data. Genome Biology, 16, 186. doi:10.1186/s13059-015-0761-7

Examples

if (requireNamespace("fgsea", quietly = TRUE)) {
  set.seed(1)
  genes <- paste0("G", 1:1000)
  sets  <- list(UP_SET = genes[1:40], NULL_SET = genes[501:560])
  de <- lapply(1:3, function(s) {
    lfc <- rnorm(1000, c(rep(1, 40), rep(0, 960)), 0.3)
    p <- 2 * pnorm(-abs(lfc / 0.3))
    data.frame(gene_id = genes, log2FC = lfc, lfcSE = 0.3, pvalue = p,
               padj = p.adjust(p, "BH"))
  })
  res <- mx_meta(de)
  res <- mx_pathway_meta(res, gene_sets = sets, min_size = 10)
  res@pathway_result
}
#> Running meta-analysis (random_effects) on 3 studies...
#>   pathway_id set_size       NES       pvalue         padj
#> 2     UP_SET       40  2.985097 1.000000e-50 2.000000e-50
#> 1   NULL_SET       60 -1.050589 3.446328e-01 3.446328e-01
#>                                                                                                                                             leading_edge
#> 2 G20;G4;G15;G25;G40;G30;G7;G36;G39;G16;G22;G10;G5;G23;G19;G34;G11;G33;G12;G32;G17;G1;G31;G37;G27;G26;G13;G18;G29;G35;G3;G6;G21;G38;G24;G2;G14;G28;G8;G9
#> 1                                                                        G511;G526;G513;G521;G559;G517;G502;G549;G510;G519;G533;G536;G507;G512;G546;G531