ROSeq {ROSeq} | R Documentation |
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
ROSeq(countData, condition, numCores = 1)
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 |
pValues and FDR adjusted p significance values
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