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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(GSVA)
param <- gsvaParam(expr, geneSets)
es <- gsva(param) |> futurize()
This vignette demonstrates how to use this approach to parallelize the GSVA functions.
The GSVA Bioconductor package implements gene set variation
analysis, a non-parametric, unsupervised method for estimating
variation of gene set enrichment through the samples of an expression
data set. The main function gsva() computes enrichment scores for
each gene set and sample, which can be parallelized across gene sets.
The gsva() function computes gene set enrichment scores using
different methods depending on the parameter object passed to it:
library(GSVA)
# Create example data
set.seed(42)
n_genes <- 200L
n_samples <- 120L
expr <- matrix(rnorm(n_genes * n_samples), nrow = n_genes, ncol = n_samples)
rownames(expr) <- paste0("gene", seq_len(n_genes))
colnames(expr) <- paste0("sample", seq_len(n_samples))
geneSets <- list(
geneSet1 = paste0("gene", sample(n_genes, 30L)),
geneSet2 = paste0("gene", sample(n_genes, 50L)),
geneSet3 = paste0("gene", sample(n_genes, 40L))
)
param <- gsvaParam(expr, geneSets)
es <- gsva(param)
Here gsva() runs sequentially, but we can easily make it run in
parallel by piping to futurize():
library(futurize)
es <- gsva(param) |> 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)
GSVA supports multiple enrichment methods through different parameter
objects. All of them can be parallelized with futurize():
## ssGSEA method
es <- gsva(ssgseaParam(expr, geneSets)) |> futurize()
## PLAGE method
es <- gsva(plageParam(expr, geneSets)) |> futurize()
## Combined z-score method
es <- gsva(zscoreParam(expr, geneSets)) |> futurize()
The following GSVA functions are supported by futurize():
gsva() - requires GSVA (>= 2.4.2 or >= 2.5.7)gsvaRanks()gsvaScores()spatCor()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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