Dear Dr. Anders, I am using DESeq package for doing the differentially expressed analysis of miRNA in two conditions (case and control) without replicate. When I run estimateSizeFactors() function, the result of sizeFactors(cds) is 1 and 1 for both conditions. 1- Is it normal to have same sizeFactors for both conditions? 2- This function use for normalization of raw data, right? Or I should normalize the data before using the package? Actually I run the following code: library(DESeq) countsTable=read.table(file="Con12Raw.txt", sep="\t", header=T);head(countsTable) rownames(countsTable)=countsTable$SeqName countsTable=countsTable[,-1] head(countsTable) conds<-c("N","T") cds=newCountDataSet(countsTable,conds) cds=estimateSizeFactors(cds);sizeFactors(cds) cds <- estimateVarianceFunctions( cds , method="blind") res <- nbinomTest( cds, "N", "T" ) head(res) plot(res$baseMean, res$log2FoldChange, col=ifelse(res$pval<0.05,"red","black"), log="x",pch=16,cex=1) abline(h=0, col="green") write.table(res, file="DesCon12Raw.txt", sep="\t",row.names = FALSE) Many thanks in advance for your reply and help -- Kind regards, Sara [[alternative HTML version deleted]]

Dear Sara On 09/20/2011 03:39 PM, Sara Nadaf wrote: > I am using DESeq package for doing the differentially expressed analysis of > miRNA in two conditions (case and control) without replicate. > > When I run estimateSizeFactors() function, the result of sizeFactors(cds) is > 1 and 1 for both conditions. > > 1- Is it normal to have same sizeFactors for both conditions? Getting exactly 1 tends to happen if your count values are all very low. To check whether the size factors are appropriate, make an MvA plot comparing the two samples plot( ( counts(cds)[,1] + counts(cds)[,2] )/2, counts(cds)[,2] / counts(cds)[,1], log="xy", pch="." ) and add a horizontal line to show the scaling constant abline( h = sizeFactors(cds)[1] / sizeFactors(cds)[2] ) Now check whether the horizontal line dissects the bulk of points nicely in the middle, especially at the larger count values. > 2- This function use for normalization of raw data, right? Or I should > normalize the data before using the package? No, raw values are correct. Simon