Getting started with presto
In presto: Fast Functions for Differential Expression Using Wilcoxon and AUC

presto runs the Wilcoxon rank-sum test and computes the auROC statistic quickly enough to be used as a routine marker-finding step on whole-genome, hundred-thousand-cell single-cell experiments.

Demo data

This vignette uses CD8 T cells from human colon tissue published by Thomas et al., Nature Medicine 2024 (GSE206299). The data is fetched with load_ircolitis_cd8(), a small build-time helper (see vignettes/ircolitis.R in the package sources) that downloads the 121 MB .h5ad.gz from GEO and parses it with rhdf5. It is not part of presto -- presto works on any counts matrix, Seurat, or SingleCellExperiment; this just supplies a realistic example.

library(presto)
library(Matrix)
library(dplyr)

## load_ircolitis_cd8() is defined in vignettes/ircolitis.R (build-time
## only), not exported by presto.
d <- load_ircolitis_cd8(verbose = FALSE)
dim(d$counts)
#> [1] 28165 25341

d$counts is a 28,165 × 25,341 sparse matrix of UMI counts (genes × cells). d$obs carries cell metadata, including a cluster column we will use to identify markers.

table(d$obs$cluster)
#> 
#>    1    2    3    4    5    6    7    8    9 
#> 8380 5115 2604 2246 2235 2049 1860  655  197

Quick start

The simplest call passes a feature-by-cell matrix and a vector of group labels:

res <- wilcoxauc(d$counts, d$obs$cluster)
head(res)
#>                       feature group  avgExpr     logFC statistic   auc     pval    padj pct_in
#> 1 ENSG00000243485|MIR1302-2HG     1 0.000000  0.000000  71066590 0.500 1.000000 1.00000 0.0000
#> 2  ENSG00000238009|AL627309.1     1 0.000239 -0.000233  71050031 0.500 0.379630 0.67361 0.0239
#> 3  ENSG00000241599|AL627309.4     1 0.000000  0.000000  71066590 0.500 1.000000 1.00000 0.0000
#> 4  ENSG00000235146|AC114498.1     1 0.000000  0.000000  71066590 0.500 1.000000 1.00000 0.0000
#> 5  ENSG00000237491|AL669831.5     1 0.053938  0.011016  71757724 0.505 0.000418 0.00187 5.1074
#> 6      ENSG00000177757|FAM87B     1 0.000000  0.000000  71066590 0.500 1.000000 1.00000 0.0000
#>   pct_out
#> 1  0.0000
#> 2  0.0472
#> 3  0.0000
#> 4  0.0000
#> 5  4.1389
#> 6  0.0000

Each row reports one (gene, group) test. Rows are returned for every gene × cluster combination, comparing observations in that cluster against all other observations.

Result columns

| column | description | |------------|----------------------------------------------------------------| | feature | name of the feature (gene) | | group | name of the group / cluster | | avgExpr | mean expression of the feature in the group | | logFC | log fold change between in-group vs out-of-group observations | | statistic| Wilcoxon rank-sum U statistic | | auc | area under the ROC curve | | pval | nominal p-value, two-sided Gaussian approximation of U | | padj | Benjamini-Hochberg adjusted p-value | | pct_in | percent of in-group cells with non-zero expression | | pct_out | percent of out-of-group cells with non-zero expression |

Top markers per cluster

top_markers() extracts the most distinguishing features per group. Use auc_min, padj_max, pct_in_min, and pct_out_max to filter.

top_markers(res, n = 5, auc_min = 0.6, padj_max = 1e-3)
#> # A tibble: 5 × 10
#>    rank `1`                    `2`                      `3`      `4`   `5`   `6`   `7`   `8`   `9`  
#>   <int> <chr>                  <chr>                    <chr>    <chr> <chr> <chr> <chr> <chr> <chr>
#> 1     1 ENSG00000198727|MT-CYB ENSG00000153563|CD8A     ENSG000… ENSG… ENSG… ENSG… ENSG… ENSG… ENSG…
#> 2     2 ENSG00000117632|STMN1  ENSG00000142669|SH3BGRL3 ENSG000… ENSG… ENSG… ENSG… ENSG… ENSG… ENSG…
#> 3     3 ENSG00000128951|DUT    ENSG00000196154|S100A4   ENSG000… ENSG… ENSG… ENSG… ENSG… ENSG… ENSG…
#> 4     4 ENSG00000198804|MT-CO1 ENSG00000116824|CD2      ENSG000… ENSG… ENSG… ENSG… ENSG… ENSG… ENSG…
#> 5     5 ENSG00000198938|MT-CO3 ENSG00000167286|CD3D     ENSG000… ENSG… ENSG… ENSG… ENSG… ENSG… ENSG…

The gene identifiers are the verbatim row names from the input matrix (ENSG…|SYMBOL). To plot just the symbol portion, split at |:

top5 <- top_markers(res, n = 5, auc_min = 0.6, padj_max = 1e-3)
top5 %>%
    dplyr::mutate(across(-rank, ~ sub(".*\\|", "", .x)))
#> # A tibble: 5 × 10
#>    rank `1`    `2`      `3`   `4`   `5`    `6`      `7`      `8`     `9`   
#>   <int> <chr>  <chr>    <chr> <chr> <chr>  <chr>    <chr>    <chr>   <chr> 
#> 1     1 MT-CYB CD8A     GZMK  GAPDH IL7R   ACTG1    TYROBP   TNFRSF4 JCHAIN
#> 2     2 STMN1  SH3BGRL3 CD27  GZMA  RPS12  CCL5     FCER1G   SRGN    IGHA1 
#> 3     3 DUT    S100A4   CST7  CD7   S100A4 TMSB4X   TRDC     ITGB2   IGKC  
#> 4     4 MT-CO1 CD2      ITGB2 TPI1  LTB    CD52     TNFRSF18 NFKBIA  IGLC2 
#> 5     5 MT-CO3 CD3D     GZMH  GZMB  RPS14  SH3BGRL3 GSTP1    CTLA4   IGLC3

Restricting to a subset of groups

Use groups_use to compare only a subset of groups against each other:

res_12 <- wilcoxauc(d$counts, d$obs$cluster, groups_use = c("1", "2"))
top_markers(res_12, n = 10, auc_min = 0.6, padj_max = 1e-3)
#> # A tibble: 10 × 3
#>     rank `1`                     `2`                     
#>    <int> <chr>                   <chr>                   
#>  1     1 ENSG00000198727|MT-CYB  ENSG00000153563|CD8A    
#>  2     2 ENSG00000198938|MT-CO3  ENSG00000116824|CD2     
#>  3     3 ENSG00000198886|MT-ND4  ENSG00000196154|S100A4  
#>  4     4 ENSG00000198712|MT-CO2  ENSG00000111796|KLRB1   
#>  5     5 ENSG00000198804|MT-CO1  ENSG00000197956|S100A6  
#>  6     6 ENSG00000117632|STMN1   ENSG00000142669|SH3BGRL3
#>  7     7 ENSG00000212907|MT-ND4L ENSG00000137078|SIT1    
#>  8     8 ENSG00000198888|MT-ND1  ENSG00000166710|B2M     
#>  9     9 ENSG00000128951|DUT     ENSG00000213658|LAT     
#> 10    10 ENSG00000164104|HMGB2   ENSG00000197540|GZMM

Seurat and SingleCellExperiment

wilcoxauc() dispatches on the input class. For Seurat or SingleCellExperiment objects, pass the object and the name of the metadata column that defines the groups:

# Seurat
wilcoxauc(seurat_object, group_by = "cluster")

# SingleCellExperiment
wilcoxauc(sce_object, group_by = "cluster")

For the full set of arguments (assay selection, layer, etc.), see ?wilcoxauc.

Notes on input types

wilcoxauc() accepts dense matrices, sparse dgCMatrix inputs, and data.frames. Sparse input is faster and uses far less memory on single-cell data. The matrix should be features × observations (rows are genes, columns are cells).

Session info

sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Sequoia 15.6.1
#> 
#> Matrix products: default
#> BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
#> 
#> locale:
#> [1] en_US/en_US/en_US/C/en_US/en_US
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] dplyr_1.2.1   Matrix_1.7-4  presto_1.1.0  knitr_1.51    ggplot2_4.0.2
#> 
#> loaded via a namespace (and not attached):
#>  [1] vctrs_0.7.2         cli_3.6.5           rlang_1.1.7         xfun_0.56          
#>  [5] otel_0.2.0          purrr_1.2.1         generics_0.1.4      S7_0.2.1           
#>  [9] data.table_1.18.2.1 glue_1.8.0          scales_1.4.0        grid_4.5.2         
#> [13] evaluate_1.0.5      tibble_3.3.1        Rhdf5lib_1.32.0     lifecycle_1.0.5    
#> [17] compiler_4.5.2      RColorBrewer_1.1-3  rhdf5filters_1.22.0 Rcpp_1.1.1         
#> [21] pkgconfig_2.0.3     tidyr_1.3.2         rhdf5_2.54.1        lattice_0.22-7     
#> [25] farver_2.1.2        R6_2.6.1            utf8_1.2.6          dichromat_2.0-0.1  
#> [29] tidyselect_1.2.1    pillar_1.11.1       magrittr_2.0.5      tools_4.5.2        
#> [33] withr_3.0.2         gtable_0.3.6