Converting FPKM from Cufflinks to raw counts for DESeq

[Simon Anders]

Furthermore, even for gene level differential expression, DESeq can be inaccurate when genes overlap (or when reads cannot be assigned uniquely to a gene due to duplicates, etc.)

This is incorrect. If I have several samples, the probability that a read gets discarded due to it falling on overlapping genes is the same in all samples. Hence, under the null hypothesis of no differential expression, the count in each sample gets reduced by the same proportion, so that the effect cancels out. One might lose power because of the overlap, but the result of the test is correct.

This requires, of course, that one properly discard reads mapping to overlaps. This is what htseq-count ensures. Not that htseq-count would be more than a simple script but it is important to note that it does this. On the Bioconductor website, you will find (in the workshop materials section) several explanations on how to do the counting in R, and unfortunately, all these do not properly take care of overlaps.

[Balat]

Yes this issue was discussed a number of times. One way to convert FPKM values is to multiply the FPKM values with transcript length and the number of reads mapped in million. Trascript length can be obtained using HTSeq.
I can't understand why it is not valid to convert FPKM values into counts and use edgeR or DESeq to test for differential expression.Especially when you want to use biological replicates for testing differential expression. Get the FPKM values by comparing expression between different samples by cufflinks. Then convert FPKM values into read counts and use any of the 'R' programs to test for differential expression.

[lpachter]

@Chrisbala:

We've discussed this issue on a number of other threads before. It doesn't make sense to convert FPKM values to read count estimates for the purposes of running DESeq.

If you want to test gene level differential expression you can just use HT-Seq as you noted. However you should know that differential expression by count data will not allow you to compare expression values of different transcripts (or genes) within a single experiment nor will you be able to test for isoform level differential expression. Unfortunately the DESeq approach is simply not suited to that. Furthermore, even for gene level differential expression, DESeq can be inaccurate when genes overlap (or when reads cannot be assigned uniquely to a gene due to duplicates, etc.)

[chrisbala]

counting reads

hi,

i stuggled to figure this out myself for a while.

I've wound up using Simon Anders' HT-Seq

http://http://www-huber.embl.de/users/anders/HTSeq/doc/count.html#count

The starting point for this, however, is the sam file, not FPKM.

Chris

[jebe]

How to I convert the FPKM values output by Cufflinks in genes.expr to raw counts to import into DESeq?