Package: presto 1.1.0

Kamil Slowikowski

presto: Fast Functions for Differential Expression Using Wilcoxon and AUC

Scalable implementation of the Wilcoxon rank sum test and the area under the receiver operating characteristic curve (also known as auROC or AUC) statistic. Interfaces to dense and sparse matrices, as well as the genomics analysis frameworks 'Seurat' and 'SingleCellExperiment'.

Authors:Ilya Korsunsky [aut], Aparna Nathan [aut], Nghia Millard [aut], Soumya Raychaudhuri [aut], Kamil Slowikowski [cre, ctb], Austin Hartman [ctb]

presto_1.1.0.tar.gz
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presto_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
presto/json (API)

# Install 'presto' in R:
install.packages('presto', repos = c('https://satijalab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/immunogenomics/presto/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

10.06 score 219 stars 1 packages 1.6k scripts 5.4k downloads 16 exports 25 dependencies

Last updated from:b5df6ee609. Checks:15 OK. Indexed: no.

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source / vignettesOK473
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macos-release-arm64OK177
macos-release-x86_64OK329
macos-oldrel-arm64OK175
macos-oldrel-x86_64OK357
windows-devel-arm64OK316
windows-devel-x86_64OK232
windows-release-arm64OK263
windows-release-x86_64OK405
windows-oldrel-x86_64OK134
wasm-releaseOK239

Exports:%>%collapse_countscompute_hashnnzeroGroupspseudobulk_deseq2pseudobulk_one_vs_allpseudobulk_pairwisepseudobulk_withinrank_matrixsumGroupssummarize_dge_pairstop_markerstop_markers_ddstoy_scetoy_seuratwilcoxauc

Dependencies:clicpp11data.tabledplyrgenericsgluelatticelifecyclemagrittrMatrixpillarpkgconfigpurrrR6RcppRcppArmadillorlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Getting started with presto
Demo data | Quick start | Result columns | Top markers per cluster | Restricting to a subset of groups | Seurat and SingleCellExperiment | Notes on input types | Session info
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-09-20
Started: 2019-05-07

Pseudobulk differential expression with DESeq2
Why pseudobulk? | Demo data | Step 1: collapse cells to pseudobulks | Step 2: one-vs-all DESeq2 | Step 3: top markers per cluster | Step 4: pairwise cluster contrasts | Step 5: within-cluster Case vs Control | Tips | Session info
Rendered frompseudobulk.Rmdusingknitr::rmarkdown

Last update: 2026-09-20
Started: 2026-08-20