RSEM counts are indeed counts (not a normalized/scaled FPKM), but for the
relatively small subset of reads that are ambiguously mapped
("multi-mapped"), then the count for that read gets broken up across the
(probabilistically-weighted) possibilities.  So the final counts are
non-integer.  We (and others) round these values to integers to usually
good effect, since their magnitude remains consistent with the
philosophically "true" count (i.e. the mean/dispersion relationship remains
similar to what it would have been had we just ignored the multi-mapped
reads and just used true counts).

-Aaron

On Wed, Nov 7, 2012 at 9:58 AM, Steve Lianoglou <
mailinglist.honeypot@gmail.com> wrote:

> Hi,
>
> On Wed, Nov 7, 2012 at 9:42 AM, Sakshi Gulati
> <Sakshi.Gulati@cancer.org.uk> wrote:
> > Hi Mark,
> >
> > Thanks for answering. It makes more sense now. I have upper quartile
> normalised RSEM counts per gene. Is that ok as an input for edgeR and/or
> DESeq?
>
> My guess is that they are not fine.
>
> Not familiar with RSEM, but if these are actually *counts* (first hint
> that they are not counts if they are not integers) then you are ok. If
> these are something like (R|F)PKM, then you're not -- ditto if you are
> already inputting numbers that have been previously scaled to library
> size.
>
> -steve
>
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
>
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