Parallelize 'fgsea' functions

Henrik Bengtsson

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The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the futurize() function. Easy!

TL;DR

library(futurize)
plan(multisession)
library(fgsea)

res <- fgsea(pathways, stats) |> futurize()

Introduction

This vignette demonstrates how to use this approach to parallelize the fgsea functions.

The fgsea Bioconductor package implements fast preranked gene set enrichment analysis (GSEA). The main functions fgsea(), fgseaMultilevel(), and fgseaSimple() perform permutation-based enrichment testing, which can be parallelized across gene sets.

Example: Running fgseaSimple() in parallel

The fgseaSimple() function performs permutation-based gene set enrichment analysis:

library(fgsea)

# Create example data
set.seed(42)
n_genes <- 1000L
stats <- rnorm(n_genes)
names(stats) <- paste0("gene", seq_len(n_genes))

pathways <- list(
  pathway1 = paste0("gene", sample(n_genes, 50L)),
  pathway2 = paste0("gene", sample(n_genes, 100L)),
  pathway3 = paste0("gene", sample(n_genes, 150L))
)

res <- fgseaSimple(pathways, stats, nperm = 10000)

Here fgseaSimple() runs sequentially, but we can easily make it run in parallel by piping to futurize():

library(futurize)

res <- fgseaSimple(pathways, stats, nperm = 10000) |> futurize()

This will distribute the work across the available parallel workers, given that we have set up parallel workers, e.g.

plan(multisession)

The built-in multisession backend parallelizes on your local computer and works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

plan(future.mirai::mirai_multisession)

and

plan(future.batchtools::batchtools_slurm)

Supported Functions

The following fgsea functions are supported by futurize():

• fgsea()
• fgseaMultilevel()
• fgseaSimple()
• fgseaLabel()
• geseca()
• gesecaSimple()
• collapsePathwaysGeseca()