5.2 years ago by
Zentrum für Molekularbiologie, Universität Heidelberg
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
> gene?s FPKM depends on the expression of other genes due to the
> library size.
> Now if my purpose is for visualization or analysis other than
> DEGs, I wonder if it?s better to replace FPKM by DESeq normalized
> count divided by gene length?
Look at it this way: To account for sequencing depth, you divide the
counts by a number which quantifies this depth. Simply using the total
number of reads (divided by 1 million) is an obvious but very
choice, and the various other scaling normalization schemes (our
median-of-ratios approach from DESeq, but also other similar
such as TMM, etc.) are simply meant to suggest a more clever way to
a number to divide by.
In case of DESeq, we try to get this numbers to be close to one. If
want to have the same scale as typical FPKM values (and so have better
comparability across experiments), you could then divide everything by
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)
we offer in DESeq2, and which, we feel, offers a better input for