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... 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 <dcorney@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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