[BioC] ...another question about using weights on microarray analysis

[Jenny Drnevich]

Hi Erika,

Filtering spots on each array individually has been addressed several 
times on the list, and the general consensus is to only do it in very 
rare circumstances, such as when you have manually flagged spots that 
are scratches, dust spots, e.g., where the reported value has 
ABSOLUTELY NO RELATIONSHIP to whatever the real value might have 
been. Spots with low SNR, auto-flagged by GenePix as "missing", or 
saturated spots all have values that are approximations of what the 
real value is, even if they aren't as precise because they are 
outside the measurement abilities of the scan. As I tell my students 
- zeros are REAL data points - would you throw them out in other 
scientific measurements? NO. It is fine to throw out a spot that 
fails to meet your criteria on ALL arrays, like the control spots.

I'm not sure about the array quality weights... the example uses 10 
replicates per group, which is probably a fine number to use to 
determine which arrays aren't as much alike as the others, but I'm 
not sure if it's good to use when you only have 3 replicates. Anyone 
care to comment on this?

Cheers,
Jenny

At 05:49 AM 2/17/2009, Erika Melissari wrote:
>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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>
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Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

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