Hi João,

I have been curious about this issue too. Even if one is doing differential
expression analysis using RNA-seq data, there is more power in a test of
longer transcripts than shorter ones (from Oshlack and Wakefield, 2009).
There appears to be no way to remedy this.

Looking at correlation between genes across samples is difficult too because
of differing transcript lengths, as you mention. Another concern in looking
at correlations of different genes using RNA-seq data is the issue of
multireads (reads that map do not map uniquely). Suppose there's a
paralogous region in three genes (or maybe a common exon between
transcripts, if one is mapping to a transcript set). An increase in the
expression of any of these genes would increase the coverage across the
paralogous region. Mapping programs like BWA deal with multireads by
distributing them randomly to all regions of equal mapping quality.
Consequently, the increase in expression of one of these three genes
increases the mapped transcripts for these other two genes as well. Thus, I
suspect there to be rampant artifactual correlation between genes with
paralogous regions or transcripts with common exons. I haven't seen mention
of this gene correlation and RNA-seq data issue yet.

Running programs that try to handle multireads becomes much more important
if one is looking at correlation across different genes. RSEM is such a
program (http://deweylab.biostat.wisc.edu/rsem/), and there are others out
there. Also, as a diagnostic, one could cluster transcript sets sequences,
and then see if highly correlated transcripts are overwhelmingly the same
with similar sequences. There could be biological reasons for this though
too.

Vince

On Thu, Apr 28, 2011 at 2:36 AM, João Moura <palerma@gmail.com> wrote:

> Dear all,
>
>
> Until now I was doing RNAseq DE analysis and to do that I understand that
> normalization issues only matter inside samples, because one can assume the
> length/content biases will cancel out when comparing same genes in
> different
> samples.
> Although, I'm now trying to compare correlation of different genes and so,
> this biases should be taken into account - for this is there any better
> method than RPKM?
>
> My main doubt is if I should also take into acount the biases inside
> samples
> and to do that is there any better approach then TMM by Robinson and
> Oshlack
> [2010]?
>
> Thank you all,
> --
> João Moura
>
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>
>
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-- 
Vince Buffalo
Statistical Programmer
Bioinformatics Core
UC Davis Genome Center
University of California, Davis

"There's real poetry in the real world. Science is the poetry of reality."
-Richard Dawkins

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