Skip to main content
SeuratWrappers provides two Presto-based differential expression functions:
  • RunPresto() — Drop-in replacement for FindMarkers() using Presto’s fast Wilcoxon test
  • RunPrestoAll() — Drop-in replacement for FindAllMarkers() using Presto
Both functions temporarily swap Seurat’s internal WilcoxDETest with Presto’s implementation, which is ~1000x faster for large datasets.

​
RunPresto()

​
Syntax

RunPresto(
  object,
  ident.1 = NULL,
  ident.2 = NULL,
  group.by = NULL,
  subset.ident = NULL,
  assay = NULL,
  slot = "data",
  reduction = NULL,
  features = NULL,
  logfc.threshold = 0.25,
  test.use = "wilcox",
  min.pct = 0.1,
  min.diff.pct = -Inf,
  verbose = TRUE,
  only.pos = FALSE,
  max.cells.per.ident = Inf,
  random.seed = 1,
  latent.vars = NULL,
  min.cells.feature = 3,
  min.cells.group = 3,
  mean.fxn = NULL,
  fc.name = NULL,
  base = 2,
  ...
)

​
Parameters

​
object
Seurat
required
A Seurat object.
​
ident.1
character
default:"NULL"
Identity class to define as positive. If NULL, uses all identities.
​
ident.2
character
default:"NULL"
Identity class to compare against. If NULL, uses all other cells.
​
group.by
character
default:"NULL"
Metadata column to use for grouping cells instead of active identities.
​
assay
character
default:"NULL"
Assay to use. Defaults to the active default assay.
​
slot
character
default:"data"
Slot within the assay to use.
​
logfc.threshold
numeric
default:"0.25"
Minimum log fold-change threshold for reporting genes.
​
min.pct
numeric
default:"0.1"
Minimum fraction of cells expressing the gene in either group.
​
only.pos
logical
default:"FALSE"
Return only positive markers.
​
max.cells.per.ident
numeric
default:"Inf"
Maximum number of cells per identity class (downsamples for speed).

​
RunPrestoAll()

​
Syntax

RunPrestoAll(
  object,
  assay = NULL,
  features = NULL,
  logfc.threshold = 0.25,
  test.use = "wilcox",
  slot = "data",
  min.pct = 0.1,
  min.diff.pct = -Inf,
  node = NULL,
  verbose = TRUE,
  only.pos = FALSE,
  max.cells.per.ident = Inf,
  random.seed = 1,
  latent.vars = NULL,
  min.cells.feature = 3,
  min.cells.group = 3,
  mean.fxn = NULL,
  fc.name = NULL,
  base = 2,
  return.thresh = 0.01,
  ...
)

​
Additional Parameters

​
return.thresh
numeric
default:"0.01"
Only return markers with adjusted p-value below this threshold.

​
Returns

A data frame identical to the output of FindMarkers() / FindAllMarkers(), containing columns: p_val, avg_log2FC, pct.1, pct.2, p_val_adj.

​
Examples

library(SeuratWrappers)
remotes::install_github("immunogenomics/presto")

# Find markers for one cluster
markers_14 <- RunPresto(object, ident.1 = "14", ident.2 = NULL)

# Find all markers
all_markers <- RunPrestoAll(object, only.pos = TRUE, logfc.threshold = 0.5)
top5 <- all_markers %>% group_by(cluster) %>% top_n(5, avg_log2FC)

​
See Also