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The futurize package allows you to easily turn sequential code
into parallel code by piping the sequential code to the futurize()
function. Easy!
library(futurize)
plan(multisession)
library(scuttle)
qc <- perFeatureQCMetrics(sce) |> futurize()
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 or features.
The perFeatureQCMetrics() function computes quality control metrics
for each feature (gene) in a SingleCellExperiment object:
library(scuttle)
# Simulate data
sce <- mockSCE()
qc <- perFeatureQCMetrics(sce)
Here perFeatureQCMetrics() runs sequentially, but we can easily make
it run in parallel by piping to futurize():
library(futurize)
qc <- perFeatureQCMetrics(sce) |> futurize()
This will distribute the work across the available parallel workers, given that we have set up parallel workers, e.g.
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.
plan(future.mirai::mirai_multisession)
and
plan(future.batchtools::batchtools_slurm)
The following scuttle functions are supported by futurize():
calculateAverage()perFeatureQCMetrics()numDetectedAcrossFeatures()summarizeAssayByGroup()medianSizeFactors()computeMedianFactors()pooledSizeFactors()computePooledFactors()fitLinearModel()The following scuttle functions are deprecated in scuttle (>=
1.22) in favor of counter-part functions in Bioconductor package
scrapper. Support for futurize() of the these deprecated
functions remains, but will be phased out;
logNormCounts()normalizeCounts()perCellQCMetrics()addPerCellQCMetrics()addPerFeatureQCMetrics()addPerCellQC()addPerFeatureQC()numDetectedAcrossCells()sumCountsAcrossCells()sumCountsAcrossFeatures()aggregateAcrossCells()aggregateAcrossFeatures()librarySizeFactors()computeLibraryFactors()geometricSizeFactors()computeGeometricFactors()These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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