[BioC] Agilent single color array

[Kasper Daniel Hansen]

Hi

I have gotten my hands on data from the single color Agilent platform  
using a custom array design and I would like to hear what people are  
usually doing when it comes to preprocessing.

I have previously analyzed some two color arrays from Agilent and  
found that the data I had was pretty standard when it comes to  
normalization. Even though I preferred doing my own preprocessing the  
Agilent supplied gProcessedSignal and rProcessedSignal columns were  
decent (this was from a much earlier version of their software -  
Feature Extractor).

But for the one color arrays I find that gProcessedSignal performs  
horrible - flat out horrible, the raw data looks much better.  
Furthermore, when I normalize between I arrays I see relatively little  
effect of normalization, sometimes the normalization even increases  
the spread on MA plots where I would not expect it to do anything. Of  
course this may be related to the hybridizations done or the array  
design I have in hand, but I still find it somewhat surprising.

I have tried vsn2 from vsn, quantile normalization and quantile  
normalization following normexp (offset 25 and 50) background  
correction from Limma. All 3 (4 if you count the 2 offsets)  
combinations have also been done with and without subtracting the  
local background estimate from Feature Extractor (the gBGMeanSignal  
column).

Anyway, I am curious as to what other people's experience using this  
platform are.

Kasper