Paired end -- fragments per million.  The trick which I don't yet know how
to do is the paired-end part.  I can adjust for tx length later if need be.

I usually work with RPM or log1p(RPM), since I tend to do my own alignments
in Subread, but reading the Cufflinks guys' paper, it does seem like FP[K]M
for paired-end data makes sense, more so with multiple isoforms.  Since
counting features in Subread is trivial, and with a splicegraph, I can go
back and count against whatever subjunc or RSEM finds, the only other thing
to fiddle with is fragment size.  The rest I already implemented as
coercions that take subread counts and return a SummarizedExperiment, which
is how I get my RPMs from FASTQs.
:-)

Thanks!

--t

On Fri, Sep 7, 2012 at 4:09 PM, Steve Lianoglou <
mailinglist.honeypot@gmail.com> wrote:

> Hi Tim,
>
> On Fri, Sep 7, 2012 at 5:45 PM, Tim Triche, Jr. <tim.triche@gmail.com>
> wrote:
> > Hi Simon,
> >
> > I have a distantly related question -- suppose we wanted to compute FPM,
> > i.e. fragments per million, for paired-end RNAseq data.  Like, say, if a
> > person (me) was really really lazy and was less concerned about
> > sensitivity, since they were just going to feed everything to voom() or
> fit
> > robust linear models of log(RPM/FPM) against predictive factors.
> >
> > Is this (fragment-level computation) possible in an existing Bioconductor
> > package at this point in time?  Or using HTseq, for that matter?
>
> Are you forgetting the K (eg. FP(K)M)?
>
> If not, I guess I'm missing something, because it's easy to compute
> from a count matrix you've jimmy'd together (cf. edgeR::cpm w/
> normalized.lib.size=TRUE).
>
> Or, are you asking how to create such a count matrix with PE data?
>
> Or?
>
> Apologies if I'm totally in the woods, here.
>
> -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
>



-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

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