Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/satijalab/seurat-wrappers/llms.txt

Use this file to discover all available pages before exploring further.

Presto reimplements the Wilcoxon rank-sum test and auROC analysis in highly optimized C++, enabling differential expression analysis on datasets with millions of cells in seconds rather than hours. SeuratWrappers integrates Presto as a drop-in replacement for Seurat’s built-in Wilcoxon test via RunPresto() and RunPrestoAll().
Citation: Korsunsky et al. (2019) Presto scales Wilcoxon and auROC analyses to millions of observations. bioRxiv. PreprintSource: immunogenomics/presto (GitHub)

​
Installation

remotes::install_github('immunogenomics/presto')

​
Key Functions

  • RunPresto() — Presto-accelerated equivalent of FindMarkers(). Finds markers distinguishing one identity class from another (or from all others).
  • RunPrestoAll() — Presto-accelerated equivalent of FindAllMarkers(). Runs marker detection across all identity classes simultaneously.
Both functions share the same interface as their Seurat counterparts and accept the same parameters. They work by temporarily replacing Seurat’s internal WilcoxDETest function with the Presto implementation, then restoring the original on completion.
RunPresto() and RunPrestoAll() only support test.use = "wilcox". For other statistical tests (negbinom, poisson, DESeq2, etc.), use Seurat’s native FindMarkers() and FindAllMarkers() directly.

​
RunPresto Parameters

​
object
Seurat object
required
The Seurat object to test.
​
ident.1
character
default:"NULL"
Identity class to find markers for. Pass a phylo object or 'clustertree' to find markers for a tree node.
​
ident.2
character
default:"NULL"
Identity class to compare against. If NULL, uses all remaining cells as the comparison group.
​
assay
character
default:"NULL"
Assay to use for differential expression testing.
​
slot
character
default:"data"
Data slot to pull expression values from.
​
features
character vector
default:"NULL"
Subset of features to test. If NULL, tests all features.
​
logfc.threshold
numeric
default:"0.25"
Minimum log-fold change required for a feature to be tested.
​
min.pct
numeric
default:"0.1"
Minimum fraction of cells in either group that must express a feature for it to be tested.
​
min.diff.pct
numeric
default:"-Inf"
Minimum difference in expression fraction between groups.
​
only.pos
logical
default:"FALSE"
If TRUE, return only positive markers (upregulated in ident.1).
​
max.cells.per.ident
numeric
default:"Inf"
Downsample each identity class to this many cells before testing.
​
group.by
character
default:"NULL"
Regroup cells by a different metadata field before testing.
​
subset.ident
character
default:"NULL"
Subset to a specific identity class before regrouping. Only relevant when group.by is set.
​
reduction
character
default:"NULL"
Run DE on cell embeddings from a dimensionality reduction instead of gene expression.
​
base
numeric
default:"2"
Logarithm base for fold change calculation.
​
random.seed
numeric
default:"1"
Random seed for reproducible downsampling.
​
min.cells.feature
numeric
default:"3"
Minimum number of cells expressing a feature for it to be tested.
​
min.cells.group
numeric
default:"3"
Minimum number of cells per group required to run the test.

​
RunPrestoAll Additional Parameters

​
node
integer
default:"NULL"
Find markers for a specific node in the cluster tree. Requires BuildClusterTree() to have been run.
​
return.thresh
numeric
default:"0.01"
Only return markers with adjusted p-value below this threshold.

​
Usage

​
Setup

library(presto)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)

InstallData("pbmc3k")
data("pbmc3k")
pbmc3k <- NormalizeData(pbmc3k)
Idents(pbmc3k) <- "seurat_annotations"

​
Pairwise marker detection with RunPresto

Find markers distinguishing CD14+ Monocytes from B cells: 
diffexp.B.Mono <- RunPresto(pbmc3k, "CD14+ Mono", "B")
head(diffexp.B.Mono, 10)

               p_val avg_logFC pct.1 pct.2     p_val_adj
CD79A  1.660326e-143 -2.989854 0.042 0.936 2.276972e-139
TYROBP 3.516407e-138  3.512505 0.994 0.102 4.822401e-134
S100A9 7.003189e-137  4.293303 0.996 0.134 9.604174e-133
CST3   1.498348e-135  3.344758 0.992 0.174 2.054834e-131
S100A4 8.872946e-135  2.854897 1.000 0.360 1.216836e-130
LYZ    2.720838e-134  3.788514 1.000 0.422 3.731357e-130

​
Marker detection across all clusters with RunPrestoAll

diffexp.all <- RunPrestoAll(pbmc3k)
head(diffexp.all[diffexp.all$cluster == "B", ], 10)

                    p_val avg_logFC pct.1 pct.2     p_val_adj cluster      gene
CD79A.3      0.000000e+00  2.933865 0.936 0.044  0.000000e+00       B     CD79A
MS4A1.3      0.000000e+00  2.290577 0.855 0.055  0.000000e+00       B     MS4A1
LINC00926.1 2.998236e-274  1.956493 0.564 0.010 4.111781e-270       B LINC00926
CD79B.3     1.126919e-273  2.381160 0.916 0.144 1.545457e-269       B     CD79B
TCL1A.3     1.962618e-272  2.463556 0.622 0.023 2.691534e-268       B     TCL1A

​
Only positive markers, per cluster

markers <- RunPrestoAll(
  pbmc3k,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.5
)

​
Compare against all other cells

When ident.2 is NULL, the comparison group is all cells not in ident.1: 
cd4_markers <- RunPresto(
  pbmc3k,
  ident.1 = "CD4 T cells",
  ident.2 = NULL,
  only.pos = TRUE
)

Build docs developers (and LLMs) love

Get started for freeTalk to us