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Bixverse implementation of the fgsea algorithm

calc_fgsea.Rd

Rust-based version of the fgsea simple and multi-level algorithm. Initially, the simple method is run. For low p-values, the multi-level method is used to estimate lower p-values than possible just based on the permutations, see Korotkevich, et al.

Usage

calc_fgsea(
  stats,
  pathways,
  nperm = 1000L,
  gsea_params = params_gsea(),
  seed = 123L
)

Arguments

stats

Named numeric vector. The gene level statistic.

pathways

List. A named list with each element containing the genes for this pathway.

nperm

Integer. Number of permutation tests. Defaults to 1000L.

gsea_params

List. The GSEA parameters, see params_gsea() wrapper function. This function generates a list containing:

  • min_size - Integer. Minimum size for the gene sets.

  • max_size - Integer. Maximum size for the gene sets.

  • gsea_param - Float. The GSEA parameter. Defaults to 1.0.

  • sample_size - Integer. Number of samples to iterate through for the multi-level implementation of fgsea.

  • eps - Float. Boundary for calculating the p-value. Used for the multi- level implementation of fgsea.

seed

Random seed for reproducibility.

Value

A data.table with the results from the GSEA with the following columns:

  • es - Float. The enrichment score for this pathway.

  • nes - Float. The normalised enrichment score for this pathway.

  • pvals - Float. The p-value for this pathway.

  • n_more_extreme - Integer. Number of permutation that had more extreme enrichment scores than the actual.

  • size - Integer. The size of the pathway.

  • pathway_name - Character. The name of the pathway.

  • leading_edge - List of character vectors with the leading edge genes.

  • fdr - Float. The adjusted pval.

References

Korotkevich, et al., bioRxiv

Examples

# fgsea with the multi-level refinement of low p-values
set.seed(42L)
stats <- stats::setNames(rnorm(500), sprintf("gene_%03i", 1:500))
stats[1:20] <- stats[1:20] + 2
pathways <- list(
  up_set = sprintf("gene_%03i", 1:20),
  random_set = sprintf("gene_%03i", 200:240)
)
res <- calc_fgsea(stats, pathways, nperm = 1000L)
res[, c("pathway_name", "es", "pvals")]
#>    pathway_name         es        pvals
#>          <char>      <num>        <num>
#> 1:       up_set  0.9045609 1.026119e-12
#> 2:   random_set -0.2135723 8.426667e-01