On Mon, Dec 9, 2013 at 10:38 AM, Cook, Malcolm <MEC@stowers.org> wrote:

> Have you done either on ALL (not just DE) genes?  If so, do your
> replicates cluster?  Further, if so, do the distances between replicate
> clusters scale in any interesting way with condition (i.e. higher dose or
> better knockdown or longer exposure -> further away from untreated).   I
> think this can be taken as "evidence" for condition effects that you and
> your colleague should expect.  Do you agree with this?


In my experience, I do occassionally see "global" (all genes) clustering in
(*scaled* and centered) PCA that corresponds to experimental conditions;
and in such cases I will also find a vast multitude of DEGs (and also
brings up the spectre of whether the usual between-sample normalization
assumptions are being violated, and whether there may be unequal variances
between groups).  Or to consider the situation a different way, when a
small number of DEGs exhibit a very large magnitude of variance, then an
*unscaled* global PCA may also show experimental clustering (again, just
driven by the variance of those DEGs).  FYI, there are methods (such as
implemented in the superpc package) that use the PCA loadings of PCs
correlated to experimental design to select DEGs.  It's all quite circular.

Either way, the presence/absence of sample clustering in PCA does not
provide any more/less independent evidence of treatment effects not already
captured by the DEGs themselves, and so I usually argue that such
"DEG-focused" PCA representations are not particularly informative (or at
least no more informative than some representation of the DEGs themselves).
 We use the global PCA for QC discovery/confirmation of sample outliers,
non-experimental batch effects, etc., but not for evaluation of the
experimental axes of interest.

-Aaron

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