Dear all,
I'm looking for a tool (better would be a Bioconductor package) able to perform differential expression analysis on a table of RPKMs. I've seen so far only solutions implementing raw read counts, like DEGSeq, edgeR and BaySeq. And yeah, in this particular case I cannot transform RPKMs back to read counts.
Do you have any idea? Furthermore, a great advantage would be the capability of calculating significance of "contrasts of contrasts" analysis, i.e. of tetrafactorial designs.
E.g. having 4 conditions: WT control, WT treated, mutant control, mutant treated.
DE1 = WT treated vs. WT control
DE2 = mutant treated vs. mutant control
DE1DE2 = (mutant trated vs. mutant control) vs. (WT treated vs. WT control)
Something like the great limma does for microarrays. But on RNASeq RPKMs.
Thanks a lot for any hint! :-)
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written
by
Federico Giorgi
1 answer
Some useful thoughts on my question:
http://seqanswers.com/forums/showthread.php?t=18622
Conclusion: use edgeR and raw read counts. No complex designs for RPKMs
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if you are more familiar with LIMMA, it now can handle RNAseq data. Check out the user guide: http://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf
Thanks Dave! Unfortunately, limma explicitly requires read counts, not RPKMs. I cite page 3 of the user guide: "The approach is to convert a table of sequence read counts into an expression object which can then be analysed as for microarray data."
Do you want to go ahead with cufflinks output (which compute FPKM) data by some bioconductor package? .I am not sure, but i think all R packages are based on read count data and are suitable for differential expressions for genes only not for differential isoforms expression.
I do use cummeRbund, but it's a mere wrapper of cuffdiff output (although very nice-looking), and it has no function of conversion to original raw read counts per isoform. AFAIK.