[BioC] Agilent 2-color Microarray data analysis tutorial

[Martin Morgan]

Yong Li wrote:
> Dear Martin,
> 
> thanks for the useful links, especially the Bioconductor case studies
> book. I am also interested in the Agilent 2-color data analysis.
> 
> In the Bioconductor case studies book source code, the Chapter 4 Two
> Color Arrays is about the analysis of Agilent 2 color data processed
> with genepix image analysis program. I just have a question about
> filtering of such data. Normally filtering is done before finding
> differentially expressed genes (in this case with limma) and after
> normalization. Filtering out control probes is straightforward. What
> about other filtering criteria, such as IQR? How can filtering be
> applied to 2-color data in the form of RGlist or MAlist?

after normalization one has an MAlist. These can be subset, so for
instance one could use Biobase::rowQ on maList$M to calculate
interquartile range

  n = nrow(maList)
  iqr = with(maList, rowQ(M, ceiling(.75*n)) - rowQ(M, floor(.25*n)))
  filtered = maList[iqr > .5,]

(untested)

Martin

> 
> Thanks,
> Yong
> 
> Martin Morgan wrote:
>> Martin Morgan wrote:
>>> Hi Chuming --
>>>
>>> Chuming Chen wrote:
>>>> Dear All,
>>>>
>>>> Can anybody point a tutorial on Agilent 2-color Microarray data
>>>> analysis
>>>> including pre-processing, statistical analyses, Gene set enrichment
>>>> analysis, Clustering and visualization etc.?
>>> The limma vignette is a very useful general purpose starting point,
>>>
>>>   http://bioconductor.org/packages/2.5/bioc/html/limma.html
>>>
>>> as are the workshop resources related to microarray analysis
>>>
>>>   http://bioconductor.org/workshops/
>>>
>>> and common workflows
>>>
>>>   http://bioconductor.org/docs/workflows/index.html
>>
>> also the Bioconductor case studies book provides a very useful extended
>> tutorial
>>
>>   http://bioconductor.org/pub/biocases/
>>
>>> Martin
>>>
>>>> Thanks,
>>>>
>>>> Chuming
>>>>
>>>> _______________________________________________
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>>>
>>
> 
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-- 
Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
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