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(scuttle) sce <- logNormCounts(sce) |> futurize() qc <- perCellQCMetrics(sce) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize the **[scuttle]** functions. The **[scuttle]** Bioconductor package provides basic utility functions for single-cell RNA-seq data analysis, including quality control, normalization, and aggregation, which can be parallelized across cells. ## Example: Log-normalizing counts in parallel The `logNormCounts()` function computes log-normalized expression values for a `SingleCellExperiment` object: ```r 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::SingleCellExperiment( assays = list(counts = counts) ) sce <- logNormCounts(sce) ``` Here `logNormCounts()` runs sequentially, but we can easily make it run in parallel by piping to `futurize()`: ```r library(futurize) sce <- logNormCounts(sce) |> 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 **scuttle** functions are supported by `futurize()`: * `calculateAverage()` * `logNormCounts()` * `normalizeCounts()` * `perCellQCMetrics()` * `perFeatureQCMetrics()` * `addPerCellQCMetrics()` * `addPerFeatureQCMetrics()` * `addPerCellQC()` * `addPerFeatureQC()` * `numDetectedAcrossCells()` * `numDetectedAcrossFeatures()` * `sumCountsAcrossCells()` * `sumCountsAcrossFeatures()` * `summarizeAssayByGroup()` * `aggregateAcrossCells()` * `aggregateAcrossFeatures()` * `librarySizeFactors()` * `computeLibraryFactors()` * `geometricSizeFactors()` * `computeGeometricFactors()` * `medianSizeFactors()` * `computeMedianFactors()` * `pooledSizeFactors()` * `computePooledFactors()` * `fitLinearModel()` [scuttle]: https://bioconductor.org/packages/scuttle/ [other parallel backends]: https://www.futureverse.org/backends.html