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:
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.