> 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 


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.

The Galaxy User list should be used for the discussion of
Galaxy analysis and other features on the public server
at usegalaxy.org.  Please keep all replies on the list by
using "reply all" in your mail client.  For discussion of
local Galaxy instances and the Galaxy source code, please
use the Galaxy Development list:


To manage your subscriptions to this and other Galaxy lists,
please use the interface at:


Reply via email to