ROSeq {ROSeq} | R Documentation

## 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

[Package ROSeq version 1.6.0 Index]