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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(DESeq2)
dds <- DESeqDataSetFromMatrix(countData, colData, design = ~ condition)
dds <- DESeq(dds) |> futurize()
This vignette demonstrates how to use this approach to parallelize
the DESeq2 DESeq() function.
The DESeq2 Bioconductor package provides methods to test for
differential expression in RNA-seq data. The main function DESeq()
runs a pipeline of gene-wise dispersion estimation, fitting, and
statistical testing, which can be parallelized across genes.
The DESeq() function performs the full differential expression
analysis:
library(DESeq2)
# Simulate data
n_genes <- 100L
n_samples <- 8L
counts <- matrix(
as.integer(runif(n_genes * n_samples, min = 0, max = 1000)),
nrow = n_genes,
ncol = n_samples,
dimnames = list(
paste0("gene", seq_len(n_genes)),
paste0("sample", seq_len(n_samples))
)
)
col_data <- data.frame(
condition = factor(rep(c("control", "treated"), each = n_samples / 2L)),
row.names = colnames(counts)
)
dds <- DESeqDataSetFromMatrix(
countData = counts,
colData = col_data,
design = ~ condition
)
dds <- DESeq(dds)
res <- results(dds)
Here DESeq() runs sequentially, but we can easily make it run in
parallel by piping to futurize():
library(futurize)
dds <- DESeq(dds) |> futurize()
res <- results(dds)
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 DESeq2 functions are supported by futurize():
DESeq()lfcShrink()results()