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 ```r library(futurize) plan(multisession) library(DESeq2) dds <- DESeqDataSetFromMatrix(countData, colData, design = ~ condition) dds <- DESeq(dds) |> futurize() ``` # Introduction 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. ## Example: Running DESeq() in parallel The `DESeq()` function performs the full differential expression analysis: ```r 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()`: ```r 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. ```r 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. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` # Supported Functions The following **DESeq2** functions are supported by `futurize()`: * `DESeq()` * `lfcShrink()` * `results()` [DESeq2]: https://bioconductor.org/packages/DESeq2/ [other parallel backends]: https://www.futureverse.org/backends.html