runScanpyFindMarkers

runScanpyFindMarkers

runScanpyFindMarkers(
  inSCE,
  nGenes = NULL,
  useAssay = "scanpyNormData",
  colDataName,
  group1 = "all",
  group2 = "rest",
  test = c("wilcoxon", "t-test", "t-test_overestim_var", "logreg"),
  corr_method = c("benjamini-hochberg", "bonferroni")
)

Arguments

inSCE

Input SingleCellExperiment object.

nGenes

The number of genes that appear in the returned tables. Defaults to all genes.

useAssay

Specify the name of the assay to use for computation of marker genes. It is recommended to use log normalized assay.

colDataName

colData to use as the key of the observations grouping to consider.

group1

Name of group1. Subset of groups, to which comparison shall be restricted, or 'all' (default), for all groups.

group2

Name of group2. If 'rest', compare each group to the union of the rest of the group. If a group identifier, compare with respect to this group. Default is 'rest'

test

Test to use for DE. Default "t-test".

corr_method

p-value correction method. Used only for 't-test', 't-test_overestim_var', and 'wilcoxon'.

Value

A SingleCellExperiment object that contains marker genes populated in a data.frame stored inside metadata slot.

Examples

data(scExample, package = "singleCellTK")
if (FALSE) {
sce <- runScanpyNormalizeData(sce, useAssay = "counts")
sce <- runScanpyFindHVG(sce, useAssay = "scanpyNormData", method = "seurat")
sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")
sce <- runScanpyPCA(sce, useAssay = "scanpyScaledData")
sce <- runScanpyFindClusters(sce, useReducedDim = "scanpyPCA")
sce <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )
}