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(sva) adjusted <- ComBat(dat = dat, batch = batch) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize the **[sva]** functions. The **[sva]** Bioconductor package provides functions for removing batch effects and other unwanted variation in high-throughput experiments. The `ComBat()` function is a widely used method for batch effect correction using an empirical Bayes framework. It supports parallelization via BiocParallel's BPPARAM argument. ## Example: Running ComBat() in parallel The `ComBat()` function adjusts for known batch effects in microarray or RNA-seq data: ```r library(sva) # Create example data with batch effect set.seed(42) n_genes <- 200L n_samples <- 40L dat <- matrix(rnorm(n_genes * n_samples), nrow = n_genes, ncol = n_samples) rownames(dat) <- paste0("gene", seq_len(n_genes)) colnames(dat) <- paste0("sample", seq_len(n_samples)) batch <- rep(c(1, 2), each = n_samples / 2L) dat[, batch == 2] <- dat[, batch == 2] + 2 adjusted <- ComBat(dat = dat, batch = batch) ``` Here `ComBat()` runs sequentially, but we can easily make it run in parallel by piping to `futurize()`: ```r library(futurize) adjusted <- ComBat(dat = dat, batch = batch) |> 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) ``` ## Using ComBat() with a model matrix You can also include a model matrix for biological covariates of interest, which will be protected during batch correction: ```r mod <- model.matrix(~ group) adjusted <- ComBat(dat = dat, batch = batch, mod = mod) |> futurize() ``` # Supported Functions The following **sva** functions are supported by `futurize()`: * `ComBat()` * `read.degradation.matrix()` [sva]: https://bioconductor.org/packages/sva/ [other parallel backends]: https://www.futureverse.org/backends.html