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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!
library(futurize)
plan(multisession)
library(fgsea)
res <- fgsea(pathways, stats) |> futurize()
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.
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)
The following fgsea functions are supported by futurize():
fgsea()fgseaMultilevel()fgseaSimple()fgseaLabel()geseca()gesecaSimple()collapsePathwaysGeseca()These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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