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(GSVA) param <- gsvaParam(expr, geneSets) es <- gsva(param) |> futurize() ``` # Introduction 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. ## Example: Running gsva() in parallel The `gsva()` function computes gene set enrichment scores using different methods depending on the parameter object passed to it: ```r 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()`: ```r 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. ```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) ``` ## Other enrichment methods GSVA supports multiple enrichment methods through different parameter objects. All of them can be parallelized with `futurize()`: ```r ## 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() ``` # Supported Functions The following **GSVA** functions are supported by `futurize()`: * `gsva()` - requires **GSVA** (>= 2.4.2 or >= 2.5.7) * `gsvaRanks()` * `gsvaScores()` * `spatCor()` [GSVA]: https://bioconductor.org/packages/GSVA/ [other parallel backends]: https://www.futureverse.org/backends.html