TMMnormalization: TMM Normalization.

In krishan57gupta/ROSeq: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data

Description

Trimmed Means of M values (TMM) normalization (on the basis of edgeR package)

Usage

TMMnormalization(countTable)

Arguments

countTable

The filtered, read count matrix, with row names as genes name/ID and column names as sample id/name

Value

countTableTMM

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$count[g_keep,]
countTableTMM<-ROSeq::TMMnormalization(countData$count)
countTableTMM

krishan57gupta/ROSeq documentation built on March 3, 2021, 11:12 a.m.