supercell_FindMarkers: Differential expression analysis of supep-cell data. Most of...

In SuperCell: Simplification of scRNA-Seq Data by Merging Together Similar Cells

Differential expression analysis of supep-cell data. Most of the parameters are the same as in Seurat FindMarkers (for simplicity)

Description

Differential expression analysis of supep-cell data. Most of the parameters are the same as in Seurat FindMarkers (for simplicity)

Usage

supercell_FindMarkers(
  ge,
  supercell_size = NULL,
  clusters,
  ident.1,
  ident.2 = NULL,
  genes.use = NULL,
  logfc.threshold = 0.25,
  min.expr = 0,
  min.pct = 0.1,
  seed = 12345,
  only.pos = FALSE,
  return.extra.info = FALSE,
  do.bootstrapping = FALSE
)

Arguments

ge	gene expression matrix for super-cells (rows - genes, cols - super-cells)
supercell_size	a vector with supercell size (ordered the same way as in ge)
clusters	a vector with clustering information (ordered the same way as in ge)
ident.1	name(s) of cluster for which markers are computed
ident.2	name(s) of clusters for comparison. If NULL (defauld), then all the other clusters used
genes.use	set of genes to test. Defeult – all genes in ge
logfc.threshold	log fold change threshold for genes to be considered in the further analysis
min.expr	minimal expression (default 0)
min.pct	remove genes with lower percentage of detection from the set of genes which will be tested
seed	random seed to use
only.pos	whether to compute only positive (upregulated) markers
return.extra.info	whether to return extra information about test and its statistics. Default is FALSE.
do.bootstrapping	whether to perform bootstrapping when computing standard error and p-value in wtd.t.test

Value

a matrix with a test name (t-test), statisctics, adjusted p-values, logFC, percenrage of detection in eacg ident and mean expresiion

SuperCell documentation built on Oct. 25, 2024, 5:07 p.m.