ROSeq {ROSeq}R Documentation

A rank based approach to modeling gene expression with filtered and normalized read count matrix

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 A vector containing 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$count)
countData$count<-apply(countData$count,2,function(x) as.numeric(x))
g_keep <- apply(countData$count,1,function(x) sum(x>0)>5)
countData$count<-countData$count[g_keep,]
countData$count<-limma::voom(ROSeq::TMMnormalization(countData$count))
output<-ROSeq(countData=countData$count, condition = countData$group)
output

[Package ROSeq version 1.0.0 Index]