hi, A few papers have concluded that DESeq is more accurate for DE genes discovery than methods using FPKM, and that the bias in FPKM is that a gene’s FPKM depends on the expression of other genes due to the division by library size. Now if my purpose is for visualization or analysis other than looking for DEGs, I wonder if it’s better to replace FPKM by DESeq normalized gene count divided by gene length? Thanks for your comment! Jack [[alternative HTML version deleted]]

Hi Jack On 23/07/13 17:29, Jike Cui wrote: > A few papers have concluded that DESeq is more accurate for DE genes > discovery than methods using FPKM, and that the bias in FPKM is that a > gene?s FPKM depends on the expression of other genes due to the division by > library size. > > Now if my purpose is for visualization or analysis other than looking for > DEGs, I wonder if it?s better to replace FPKM by DESeq normalized gene > count divided by gene length? Yes, definitely. Look at it this way: To account for sequencing depth, you divide the raw counts by a number which quantifies this depth. Simply using the total number of reads (divided by 1 million) is an obvious but very simplistic choice, and the various other scaling normalization schemes (our median-of-ratios approach from DESeq, but also other similar suggestions such as TMM, etc.) are simply meant to suggest a more clever way to find a number to divide by. In case of DESeq, we try to get this numbers to be close to one. If you want to have the same scale as typical FPKM values (and so have better comparability across experiments), you could then divide everything by something like geometric mean of the total read counts of all samples / 1 million You may want to look, though, also at the variance-stabilizing transformation (VST) and the regularized log transformation (rlog) that we offer in DESeq2, and which, we feel, offers a better input for downstream visualization. Simon