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

[Matt Ritchie]

Dear Jenny and Erika,

Regarding the question on array weights, so long as you have enough arrays 
to fit the linear model to the means, you will be able to estimate array 
variance factors using arrayWeights(). The example given on the 
arrayWeights help page uses the methodology on a set of 6 arrays with 3 
replicates per group.

And yes, spot and array weights can be combined in the analysis by 
multiplying them together. Make sure that what you are multiplying are 
matrices of the same dimension though - the output of arrayWeights is a 
vector, so you will want to run asMatrixWeights() on this vector before 
multiplying with the spot weights.

Best wishes,

Matt

>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