Hello all,

I have found discordant opinions among Bioconductor email regarding the use of quality weights on microarray analysis and I woul like to understand with clarity what to do before starting the statistical analysis of my last experiment.
I use LIMMA to perform statistical analysis of microarray experiments.
Usually, I assign a weight to all the spots of my experiment by using in read.maimages() this wt.fun:

function(x, threshold=3){

#to exclude spots with SNR<3 on both channels
snrok <- !(x[,"SNR 635"] < threshold & x[,"SNR 532"] < threshold );

#to include only genes and not control spots (I use Agilent microarrays)
spotok <- (x[,"ControlType"] == "false");

#to exclude spots with flag "bad" by GenePix Pro 6
flagok <- (x[,"Flags"] >= 0);

#to exclude spot saturated
satok <- !(x[,"F635 % Sat."] > 10 | x[,"F532 % Sat."] > 10 );

spot <- (snrok & spotok & flagok & satok);
as.numeric (spot);
}

In my opinion it is right to exclude spot saturated (because its intensity value is not reliable). Is it wrong?
I have a doubt about excluding spot with low SNR, because in my last experiment I should exclude for low SNR about 60% of 45000 spots and I am worried about the robustness of statistical analysis evalued only on 40% of the genes.
Should I exclude this spot?
Before or after normalization? 
Should I normalize all the spots and then, on the normalized value, apply the SNR quality filter to exclude normalized spots with low SNR from subsequent statistical analysis?
I would like to use arrayWeights() from LIMMA and combine spot quality weights and array quality weights. Is it right to multiply the spot weight matrix by array quality vector?

thank you very much for any help on this complicate question.

Erika


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