FindGeneMarkers: Identification of gene markers for a cluster or two arbitrary...

In ILoReg: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

Description

FindGeneMarkers enables identifying gene markers for one cluster or two arbitrary combinations of clusters, e.g. 1_2 vs. 3_4_5. Gene and cell filters can be applied to accelerate the analysis, but this might lead to missing weak signals.

Usage

FindGeneMarkers.SingleCellExperiment(
  object,
  clusters.1,
  clusters.2,
  clustering.type,
  test,
  logfc.threshold,
  min.pct,
  min.diff.pct,
  min.cells.group,
  max.cells.per.cluster,
  pseudocount.use,
  return.thresh,
  only.pos
)

## S4 method for signature 'SingleCellExperiment'
FindGeneMarkers(
  object,
  clusters.1 = NULL,
  clusters.2 = NULL,
  clustering.type = "",
  test = "wilcox",
  logfc.threshold = 0.25,
  min.pct = 0.1,
  min.diff.pct = NULL,
  min.cells.group = 3,
  max.cells.per.cluster = NULL,
  pseudocount.use = 1,
  return.thresh = 0.01,
  only.pos = FALSE
)

Arguments

object	of SingleCellExperiment class
clusters.1	a character or numeric vector denoting which clusters to use in the first group (named group.1 in the results)
clusters.2	a character or numeric vector denoting which clusters to use in the second group (named group.2 in the results)
clustering.type	"manual" or "optimal". "manual" refers to the clustering formed using the "SelectKClusters" function and "optimal" to the clustering formed using the "CalcSilhInfo" function. Default is "manual".
test	Which test to use. Only "wilcoxon" (the Wilcoxon rank-sum test, AKA Mann-Whitney U test) is supported at the moment.
logfc.threshold	Filters out genes that have log2 fold-change of the averaged gene expression values (with the pseudo-count value added to the averaged values before division if pseudocount.use > 0) below this threshold. Default is 0.25.
min.pct	Filters out genes that have dropout rate (fraction of cells expressing a gene) below this threshold in both comparison groups Default is 0.1.
min.diff.pct	Filters out genes that do not have this minimum difference in the dropout rates (fraction of cells expressing a gene) between the two comparison groups. Default is NULL.
min.cells.group	The minimum number of cells in the two comparison groups to perform the DE analysis. If the number of cells is below the threshold, then the DE analysis is not performed. Default is 3.
max.cells.per.cluster	The maximun number of cells per cluster if downsampling is performed to speed up the DE analysis. Default is NULL, i.e. no downsampling.
pseudocount.use	A positive integer, which is added to the average gene expression values before calculating the fold-change. This makes sure that no divisions by zero occur. Default is 1.
return.thresh	If only.pos=TRUE, then return only genes that have the adjusted p-value (adjusted by the Bonferroni method) below or equal to this threshold. Default is 0.01.
only.pos	Whether to return only genes that have an adjusted p-value (adjusted by the Bonferroni method) below or equal to the threshold. Default is FALSE.

Value

a data frame of the results if positive results were found, else NULL

Examples

library(SingleCellExperiment)
sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500))
sce <- PrepareILoReg(sce)
## These settings are just to accelerate the example, use the defaults.
sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,k=5,r=1)
sce <- RunPCA(sce,p=5)
sce <- HierarchicalClustering(sce)
sce <- SelectKClusters(sce,K=5)
gene_markes_1 <- FindGeneMarkers(sce,clusters.1=1)
gene_markes_1_vs_2 <- FindGeneMarkers(sce,clusters.1=1,clusters.2=2)

ILoReg documentation built on Nov. 8, 2020, 8:20 p.m.