When DESeq is used for our RNA-Seq data analysis of two conditions, each of them has three biological replicates, we got a result better than Cuffdiff2 and CLCBio Genomics Workbench, verified by RT-qPCR. Based on the normalized counts and adj-p values, can we also tell with confidence which genes are highly expressed and which genes are rarely expressed within the same conditions. I knew that DESeq have library size factor in normalization, but do not have gene length factor in normalization like Cuffdiff2. Any comment will be highly appreciated. Chun [[alternative HTML version deleted]]

Hi, On Tue, Feb 11, 2014 at 6:25 AM, Liang, Chun <liangc at="" miamioh.edu=""> wrote: > When DESeq is used for our RNA-Seq data analysis of two conditions, each of > them has three biological replicates, we got a result better than Cuffdiff2 > and CLCBio Genomics Workbench, verified by RT-qPCR. > > Based on the normalized counts and adj-p values, can we also tell with > confidence which genes are highly expressed and which genes are rarely > expressed within the same conditions. I knew that DESeq have library size > factor in normalization, but do not have gene length factor in > normalization like Cuffdiff2. Any comment will be highly appreciated. You could always divide the normalized counts by the K in RPKM do get a similar number, no? For instance, edgeR has an `rpkm` function to do the same -- you can take inspiration from the code there to see how to do it. -steve -- Steve Lianoglou Computational Biologist Genentech