Hi all,

    I am new to micro-array processing and to Bioconductor.  The  
documentation provided by the authors of packages like limma and  
marray have made it very easy to get a toehold on how to pre-process,  
normalize, and analyze microarrays.

The Agilent Feature Extraction Software Manuals, alas, are not as  
helpful, and I would be very appreciative of some guidance on two  
issues.

First, The Data: I have inherited an Agilent two-color microarray  
dataset.  This dataset consists of eight microarrays.  To each array  
was hybridized both a common reference sample and a tissue specific  
sample.  In 6 of the arrays, the reference is Cy3.  In the other 2,  
the reference is Cy5 labeled.  Included in our probeset are a set of  
Agilent designed spike-in and negative controls, along with ~600  
probes we have designed that we hope act as negative controls for  
this species (sea urchin).

The questions:

1) Loess normalization when many genes are expected to be  
differentially expressed: The first pre-processing step I plan to  
take is a within array global normalization using loess.  However, I  
am concerned that because the data consist of comparisons between  
different tissues, even with a common reference, there may be a large  
number of genes that really are differentially expressed.  Can anyone  
provide insight as to the limits of loess normalization in the face  
of an expectation that many genes will be differentially regulated?   
Can anyone suggest alternatives for array normalization based on our  
experiment?

2) Spatial Detrending of the Background Signals: Agilent's Feature  
Extraction manual discusses something called spatial de-trending.   
The goal of the algorithm is to apply an offset to the spot  
intensities that reflect spatial specific variation on the array.   
Unlike other normalization modules I've seen in bioconductor, this  
algorithm is applied only to the negative controls or low intensity  
spots and is applied to each channel separately (though they still  
refer to the process as using 2D loess normalization).  I have not  
seen similar steps references in the literature.  Can anyone speak to  
the importance of this step or bioconductor packages that facilitate  
doing this.  I'm not against programming my own, if this step is  
needed, by why re-invent the wheel.

3) The best ways of incorporating our dye-swaps:  The most commonly  
referenced between array normalization strategy I have seen is  
quantile-normalization.  However, given our spoke-and-hub sort of  
design (with dye-swaps), it seems that this method would lose some  
potential information from the common reference and the dye-swaps.   
Can one suggest a (ideally already implemented) normalization  
strategy in this case?

I've asked many questions.  If you could provide any insight into any  
of them, that would be appreciated.  Hopefully I will be able to  
contribute information from this experience to the list at a future  
date.

Cheers,

David



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