Hi Dave,
This is an interesting and non-trivial question that extends well
beyond Galaxy - and there's no simple solution AFAIK
Defining an 'outlier' tends to boil down to subjective judgement in
most real cases I've seen.
EG: see 
http://comments.gmane.org/gmane.science.biology.informatics.conductor/40927

My 2c worth:
a) confirm that all of your sample library sizes and quality score
distributions are comparable with the FastQC tool. A sample with
relatively low library size may indicate an upstream technical failure
with (eg) RNA extraction or a flowcell lane.
b) check that the number of unique alignments to the reference are
similar (eg picard alignment summary metrics or even the samtools
flagstat tool)
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)
d) If you're an R hacker, you might find
http://gettinggeneticsdone.blogspot.com.au/2012/09/deseq-vs-edger-comparison.html
useful - it shows how to get MDS plots which are probably the most
reliable way to identify samples that don't cluster well with the
other members of their tribe



On Fri, Nov 9, 2012 at 10:22 AM, Dave Corney <dcor...@princeton.edu> wrote:
> Hello list,
>
> I've been analyzing an experiment with two groups each with three
> replicates. My workflow was TopHat (paired end) -> Cufflinks -> CuffDiff.
> Unfortunately, there are not many significant differences identified by
> CuffDiff.
>
> I am wondering whether one of my replicates might be an outlier. Does
> anybody have a suggestion on how to search for an outlier? The quality
> statistics of the unprocessed data looked equally good for all samples, so I
> don't think that this is a problem.
>
> Thanks,
> Dave
>
>
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-- 
Ross Lazarus MBBS MPH;
Head, Medical Bioinformatics, BakerIDI; Tel: +61 385321444
http://scholar.google.com/citations?hl=en&user=UCUuEM4AAAAJ
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