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()
```
# Deprecation Warning
As of **GSVA** (>= 2.7.0), futurization is no longer supported. An
informative error message will produced, if attempted.
The reason for this is that **GSVA** has moved to using
`BiocParallel::bpiterate()` internally. That call is not supported by
the `BiocParallel::DoparParam()` backend, which is the only one that
currently bridges Futureverse and BiocParallel. See
for details and
status updates.
# 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()
```
## Rank-based functions
As of **GSVA** (>= 2.7.5), the two-step `gsvaRanks()` + `gsvaScores()`
pipeline is deprecated in favor of a three-step `gsvaRowNorm()` +
`gsvaColRanks()` + `gsvaColScores()` pipeline. Each of these steps can
be parallelized with `futurize()`:
```r
gsvarnorm <- gsvaRowNorm(param) |> futurize()
gsvaranks <- gsvaColRanks(gsvarnorm) |> futurize()
es <- gsvaColScores(gsvaranks, geneSets(param)) |> futurize()
```
# Supported Functions
The following **GSVA** functions are supported by `futurize()`:
* `gsva()`
* `gsvaRanks()`
* `gsvaScores()`
* `spatCor()`
[GSVA]: https://bioconductor.org/packages/GSVA/
[other parallel backends]: https://www.futureverse.org/backends.html
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