ROSeq: Modeling expression ranks for noise-tolerant differential...

In ROSeq: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data

Description

Takes in the complete filtered and normalized read count matrix, the location of the two sub-populations and the number of cores to be used

Usage

ROSeq(countData, condition, numCores = 1)

Arguments

countData	The normalised and filtered, read count matrix, with row names as genes name/ID and column names as sample id/name
condition	Labels for the two sub-populations
numCores	The number of cores to be used

Value

pValues and FDR adjusted p significance values

Examples

countData<-list()
countData$count<-ROSeq::L_Tung_single$NA19098_NA19101_count
countData$group<-ROSeq::L_Tung_single$NA19098_NA19101_group
head(countData$count)
gene_names<-rownames(countData$count)
countData$count<-apply(countData$count,2,function(x) as.numeric(x))
rownames(countData$count)<-gene_names
countData$count<-countData$count[,colSums(countData$count> 0) > 2000]
g_keep <- apply(countData$count,1,function(x) sum(x>2)>=3)
countData$count<-countData$count[g_keep,]
countData$count<-limma::voom(ROSeq::TMMnormalization(countData$count))
output<-ROSeq(countData=countData$count$E, condition = countData$group)
output

ROSeq documentation built on Feb. 18, 2021, 2 a.m.