Package: presto 1.1.0

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://slowkow.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

3.48 score 1 packages 238 downloads 16 exports 25 dependencies

Last updated from:5e323fb82d. Checks:15 OK. Indexed: yes.

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source / vignettesOK415
linux-release-arm64OK206
linux-release-x86_64OK287
macos-release-arm64OK209
macos-release-x86_64OK528
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macos-oldrel-x86_64OK337
windows-devel-arm64OK426
windows-devel-x86_64OK266
windows-release-arm64OK413
windows-release-x86_64OK205
windows-oldrel-x86_64OK219
wasm-releaseOK195

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

Last update: 2026-09-30
Started: 2026-09-30

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

Last update: 2026-09-30
Started: 2026-09-30