presto

Fast differential expression and marker discovery for single-cell data.

presto is built around two function families:

• wilcoxauc() — Wilcoxon rank-sum test and area-under-the-ROC across groups, fast enough to run on whole-genome × hundred-thousand- cell matrices in seconds.
• pseudobulk_deseq2() — collapse cells into per-donor pseudobulks with collapse_counts() and run a count-based regression with DESeq2 in one-vs-all, pairwise, or within-cluster designs.

Both work directly with sparse dgCMatrix input and have dispatchers for Seurat and SingleCellExperiment objects.

Installation

# install.packages("devtools")
devtools::install_github("immunogenomics/presto")

pseudobulk_deseq2() additionally requires DESeq2, and the load_ircolitis_cd8() demo loader requires rhdf5. Both live in Suggests, so you only need them if you use those features.

Quick example

library(presto)

## Generate a tiny toy dataset deterministically.
set.seed(42)
exprs <- matrix(rpois(25 * 150, lambda = 2), nrow = 25,
                dimnames = list(paste0("G", 1:25), NULL))
y <- rep(c("A", "B", "C"), each = 50)

res <- wilcoxauc(exprs, y)
head(res)
#>   feature group avgExpr  logFC statistic   auc   pval  padj pct_in pct_out
#> 1      G1     A    2.10  0.200      2740 0.548 0.3264 0.510     86      89
#> 2      G2     A    1.58 -0.560      1896 0.379 0.0137 0.172     84      86
#> 3      G3     A    1.86  0.020      2432 0.486 0.7826 0.893     84      89
#> 4      G4     A    1.96 -0.210      2384 0.477 0.6362 0.884     90      90
#> 5      G5     A    2.00 -0.280      2214 0.443 0.2442 0.505     82      88
#> 6      G6     A    2.26  0.240      2775 0.555 0.2627 0.505     90      83

top_markers() summarises the most distinguishing features per group:

top_markers(res, n = 5, auc_min = 0.5)
#> # A tibble: 5 × 4
#>    rank A     B     C
#>   <int> <chr> <chr> <chr>
#> 1     1 G10   G15   G25
#> 2     2 G24   G13   G5
#> 3     3 G16   G22   G18
#> 4     4 G6    G2    G14
#> 5     5 G7    G20   G2

The same call also works on Seurat and SingleCellExperiment objects:

wilcoxauc(seurat_object, group_by = "cluster")
wilcoxauc(sce_object,    group_by = "cluster")

Walkthroughs

For full examples on a real 25,000-cell single-cell RNA-seq dataset, see:

• Getting started — wilcoxauc() and top_markers() for marker discovery, including a description of every output column and how to restrict the comparison to a subset of groups.
• Pseudobulk differential expression with DESeq2 — collapse_counts() and the three pseudobulk_deseq2() modes (one-vs-all, pairwise, within), plus top_markers_dds() and summarize_dge_pairs().

Performance

A benchmark on 1,000,000 observations × 1,000 features × 10 groups runs in 16 seconds on sparse input and 85 seconds on dense input.