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(SingleCellExperiment) library(scuttle) result <- applySCE(sce, perCellQCMetrics) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize the **[SingleCellExperiment]** functions. The **[SingleCellExperiment]** Bioconductor package defines the `SingleCellExperiment` class for storing single-cell genomics data, including alternative experiments (e.g. spike-in transcripts, antibody tags). The `applySCE()` function applies a given function to the main experiment and each alternative experiment, passing additional arguments such as `BPPARAM` via `...` to enable parallelization of the applied function. ## Example: Computing per-cell QC metrics in parallel The `applySCE()` function applies a function across the main experiment and its alternative experiments: ```r library(SingleCellExperiment) library(scuttle) # Simulate data set.seed(42) n_genes <- 200L n_cells <- 100L counts <- matrix( rpois(n_genes * n_cells, lambda = 10), nrow = n_genes, ncol = n_cells, dimnames = list( paste0("gene", seq_len(n_genes)), paste0("cell", seq_len(n_cells)) ) ) sce <- SingleCellExperiment( assays = list(counts = counts) ) # Add an alternative experiment (e.g. spike-ins) spike_counts <- matrix( rpois(10L * n_cells, lambda = 5), nrow = 10L, ncol = n_cells ) rownames(spike_counts) <- paste0("spike", seq_len(10L)) colnames(spike_counts) <- paste0("cell", seq_len(n_cells)) altExp(sce, "spikes") <- SingleCellExperiment( assays = list(counts = spike_counts) ) result <- applySCE(sce, perCellQCMetrics) ``` Here `applySCE()` runs `perCellQCMetrics()` sequentially on each experiment, but we can easily make it run in parallel by piping to `futurize()`: ```r library(futurize) result <- applySCE(sce, perCellQCMetrics) |> 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) ``` # Supported Functions The following **SingleCellExperiment** functions are supported by `futurize()`: * `applySCE()` [SingleCellExperiment]: https://bioconductor.org/packages/SingleCellExperiment/ [other parallel backends]: https://www.futureverse.org/backends.html