[BioC] objective criterion for identification of outlying arrays by pca IN GENE ST1.0 CHIPS

[Benilton Carvalho]

oligo, as Richard already tried, does offer some affyPLM features
(NUSE+RLE) for (but not only) ST arrays. At the probeset level,
however, NUSE boxplots are very asymmetric and I believe this is
associated to the control probesets (I need to make some time to check
this and allow some filtering if this is the case).

b

On 4 November 2011 15:03, Vincent Carey <stvjc at channing.harvard.edu> wrote:
> On Fri, Nov 4, 2011 at 10:26 AM, Richard Friedman <
> friedman at cancercenter.columbia.edu> wrote:
>
>>
>> On Nov 2, 2011, at 10:16 AM, Vincent Carey wrote:
>>
>>  you can read about formally calibrated outlier assessment for microarrays
>>> in
>>>
>>> http://bioinformatics.**oxfordjournals.org/content/25/**1/48<http://bioinformatics.oxfordjournals.org/content/25/1/48>
>>>
>>
>> Dear Vincent and List,
>>
>>        I read your paper with great interest. i will implement it for
>> datasets for Affymetrix Chips with
>> mismatch probes, to which it is geared, along with the weighting method
>> suggested by Jim MacDonald, for arrays which pass the test. However a Gene
>> ST1.0 dataset has come up with this kind of rejection question. Has you
>> method be adapted to Gene ST1.0 arrays? If not, do you know of
>> a method analogous to yours which can be used for ST1.0s.
>>
>
> There is a fair amount of generality in arrayMvout but it has not all been
> exercised to the same degree.  The simplest uses work from affyBatch or
> lumiBatch instances and compute QA statistics tailored to the respective
> platforms.  If you have a data frame of array-specific QA statistics for
> some other platform, arrayOutliers() will perform calibrated multivariate
> outlier detection with these measures.  For the early affy arrays, NUSE and
> RLE statistics from affyPLM play a role; I don't know if these are readily
> computed for 1.0 ST arrays at this time.  arrayMvout uses
> Mahalanobis-distance based procedures, but the distance is robustified by
> inward peeling to deal with masking problems that can arise with multiple
> outliers.
>
> Let's not forget the  Bioconductor mdqc package, which also deals with a
> differently robustified Mahalanobis distance for this purpose.
>
>
>>
>> Thanks and best wishes,
>> Rich
>> ------------------------------**-----------------------------
>> Richard A. Friedman, PhD
>> Associate Research Scientist,
>> Biomedical Informatics Shared Resource
>> Herbert Irving Comprehensive Cancer Center (HICCC)
>> Lecturer,
>> Department of Biomedical Informatics (DBMI)
>> Educational Coordinator,
>> Center for Computational Biology and Bioinformatics (C2B2)/
>> National Center for Multiscale Analysis of Genomic Networks (MAGNet)
>> Room 824
>> Irving Cancer Research Center
>> Columbia University
>> 1130 St. Nicholas Ave
>> New York, NY 10032
>> (212)851-4765 (voice)
>> friedman at cancercenter.**columbia.edu <friedman at cancercenter.columbia.edu>
>> http://cancercenter.columbia.**edu/~friedman/<http://cancercenter.columbia.edu/%7Efriedman/>
>>
>> I am a Bayesian. When I see a multiple-choice question on a test and I
>> don't
>> know the answer I say "eeney-meaney-miney-moe".
>>
>> Rose Friedman, Age 14
>>
>>
>>
>>
>>
>>
>>> On Wed, Nov 2, 2011 at 10:04 AM, Richard Friedman <friedman at cancercenter.
>>> **columbia.edu <friedman at cancercenter.columbia.edu>> wrote:
>>> Dear Bioconductor List,
>>>
>>>       Does anyone know of an objective criterion for the identification
>>> of outlying arrays
>>> by pca?
>>>
>>>       I usually do this subjectively. However the experimental
>>> investigator whom I am helping
>>> has a different subjective sense than I do, so that I wonder if there is
>>> a hard-and-fast criterion.
>>>
>>>
>>
>>
>> -
>>
>>
>>
>>
>>
>>
>>
>
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>
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