> c) if you can create an appropriate input matrix (read counts by exon
> or other contig for each sample eg), the Principal Component Analysis
> tool might be helpful (library size normalization is one devil that
> lies in the detail and it's not quite the same as MDS - see below)
I like starting with this approach because it can be done easily in Galaxy. You
can take the expression datasets produced by Cufflinks for each replicate and
join them on gene name to get a big table of replicate-expression values and
either eyeball it or use PCA. Note that since Cufflinks produces FPKM, library
size is already accounted for.
Another idea/approach: Cuffdiff already has an advanced model for dealing with
replicates:
http://cufflinks.cbcb.umd.edu/howitworks.html#reps
You may want to investigate how this model works and whether you can tune it
with parameter settings before giving up on using all your replicates.
One challenge with this approach is that the Galaxy Cuffdiff wrapper does not
yet include all parameters, so you might try enhancing the Cuffdiff wrapper
with additional, relevant parameters and using those as well as the existing
ones. If you do this, please consider submitting your enhancements back to me
and I can integrate them into our code base.
Best,
J.
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