[BioC] ...another question about using weights on microarray analysis
Naomi Altman
naomi at stat.psu.edu
Thu Feb 19 16:13:40 CET 2009
I vote with Jenny and I feel strongly too.
Too much highly informative data are being discarded. The only time
to discard saturated or NotFound spots is when they are in that
condition on every array in the study.
--Naomi
At 09:56 AM 2/19/2009, Jenny Drnevich wrote:
>Hi Erika,
>
>Let me try to make my arguments more persuasive, as I do feel
>strongly about this:
>
>>About weighting array spot-to-spot, what is your opinion?
>>I am not sure I have to consider as good, and then usefull to fit
>>linear model, saturated spots. In all measurement process this kind
>>of values are discarded because are outside the range of
>>reliability of the instrument used to permorm the measurement.
>
>While these values are outside the range of reliability, you did
>know that they were _high_. If you discard them, it's as if you had
>no information at all on the expression values of those samples,
>which is not true. You shouldn't have too many saturated spots
>anyway, else you didn't set the settings on your scan properly. I'll
>give you an example of where discarding spots could cause a big
>problem with the next scenario...
>
>>Another observation...
>>If GenePix flags as NotFound a spot because the level of
>>hybridization is not enought to consider reliable the level of
>>hybridization, why should I use this signal to fit my model?
>
>Because if there had been enough transcript, you would have measured
>something! The numbers, while not completely accurate, are going to
>be relatively low when compared to other samples that did have a
>measurable hybridization. Here's a real scenario: say one of your
>transfected lines turns on a gene that was off in the reference and
>in the other lines. There shouldn't be any measurable hybridization
>in either channel for the other lines hybed with the ref, but if you
>throw away all these data points, then you cannot tell that the
>expression was higher in the first transfected line compared with
>the other transfected lines! While this scenario is likely rare,
>these are the genes in which you would probably be most interested.
>
>Jenny
>
>
>>And concerning weighting spots before or after they have been
>>normalized? The result change completely!
>>
>>I am not much persuader about using unreliable spots and a lot
>>confused about the workflow to be followed.
>>
>>Could you, or anyone else, help me?
>>
>>Thank you so much
>>
>>Erika
>>
>>----- Original Message ----- From: "Matt Ritchie" <mer36 at cam.ac.uk>
>>To: "Jenny Drnevich" <drnevich at illinois.edu>; "Erika Melissari"
>><erika.melissari at bioclinica.unipi.it>
>>Cc: <bioconductor at stat.math.ethz.ch>
>>Sent: Thursday, February 19, 2009 05:12 AM
>>Subject: Re: [BioC] ...another question about using weights on
>>microarray analysis
>>
>>
>>>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
>
>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
>
>330 ERML
>1201 W. Gregory Dr.
>Urbana, IL 61801
>USA
>
>ph: 217-244-7355
>fax: 217-265-5066
>e-mail: drnevich at illinois.edu
>
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Naomi S. Altman 814-865-3791 (voice)
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Dept. of Statistics 814-863-7114 (fax)
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