[BioC] outlier probes detection

[Djork-Arné Clevert]

Hi Andrea,

I suggest using the farms package to summarize your data. FARMS is a probabilistic latent variable model that 
decomposes the data variance into signal and noise variance. Thus, up to five probe outliers per probe-set will 
not impair the summarization as they will be explained as noise.  
Another nice feature is FARMS' informative/ non-informative (I/NI) call that allows you to filter out probe-sets that
are relevant for your experiment.
Check out:
farms:
http://bioinformatics.oxfordjournals.org/content/22/8/943.abstract
I/NI:
http://bioinformatics.oxfordjournals.org/content/23/21/2897.abstract

Cheers,
Okko


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e: okko at clevert.de
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Am 08.05.2012 um 12:15 schrieb andrea.grilli at ior.it:

> 
> Dear all,
> I'm performing an analysis on HGU133plus2 arrays with 40 samples;
> looking at their surface with "affyPLM" package, I've seen a couple of
> arrays with small scratches and one more with a small bubble. Because
> I don't want to exclude these arrays (according to Murphys' law 2 on 3
> belong to the class with less samples), I want to detect those probes
> and to exclude them.
> I was thinking in some outlier detection method, but because I'm new
> to this problem I don't know if this is the right method and which
> packages can be appropriate (did some research but I've no clear idea).
> Any help is really appreciated,
> andrea
> 
> 
> 
> 
> Dr. Andrea Grilli
> andrea.grilli at ior.it
> phone 051/63.66.756
> 
> Laboratory of Experimental Oncology,
> Development of  Biomolecular Therapies unit,
> Rizzoli Orthopaedic Institute
> Codivilla Putti Research Center
> via di Barbiano 1/10
> 40136 - Bologna - Italy
> 
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