[BioC] a question about LIMMA

[Francois Pepin]

Dear Erika,

please include the bioconductor list in your replies. That way other 
people can chime in and people with the same question in the future can 
find the posts in the archives.

You might want to try the arrayQualityMetrics package for your QC also.

It depends if you mean by "handle". Differential expression is only one 
of the operation that is generally done with microarrays, after all. I 
generally use limma for differential expression, but other packages are 
available.

I do not do any kind of sorting with the RG object. As I said, I check 
the duplicate probes to make sure they're the same, but I otherwise 
ignore them. Gordon's new function is probably what I would use now to 
deal with them.

You might want to read up on what the Loess normalization does. This 
kind of repositioning would have no effect at all, as the neighborhood 
is defined by the relative intensities of the spots. 
?normalizeWithinArrays suggests papers that describes those methods in 
more details. In general, you never want to try to "help" those methods 
along unless you really understand what they do. You risk invalidating 
your results if you do so.

Francois

Erika Melissari wrote:
> Dear Francois,
> 
> thank you very much for your help.
> About arrays, I mean 4x44k Agilent arrays, but we have already used 44k 
> whole rat Agilent arrays.
> Agilent's Feature Extraction software performs a quality control 
> procedure based on replicate spots to produce a measure of 
> reproducibility (%CV) on the array...but It is not free of charge.
> Please, I have another question.
> What package do you use to handle microarray data?
> If you use LIMMA package, do you sort the RG file to put replicate 
> probes close and then you normalize?
> When LOESS normalization method is used, maybe the M value depends on 
> "neighbors" in the smoothing window. Then putting the replicate probes 
> close can ensure about a normalization "bad" effect.
> 
> Thank you
> 
> Erika
> 
> 
> ----- Original Message ----- From: "Francois Pepin" <fpepin at cs.mcgill.ca>
> To: "Gordon K Smyth" <smyth at wehi.EDU.AU>
> Cc: "Erika Melissari" <erika.melissari at bioclinica.unipi.it>; 
> <bioconductor at stat.math.ethz.ch>
> Sent: Wednesday, June 04, 2008 17:39 PM
> Subject: Re: [BioC] a question about LIMMA
> 
> 
>> Dear Erika,
>>
>> Are you talking about the whole genome 44k (or 4x44k) arrays?
>>
>> In our situation (with the arrays mentioned above), we have found those
>> replicate probes to behave in a virtually identical manner, to the point
>> where we arbitrarily select one of the probes and simply ignore the rest.
>>
>> As Gordon was saying, you can simply average the values. We have found
>> this not to be necessary, but it would definitely not hurt.
>>
>> I do not know of any package to use them for quality control. If you see
>> one replicate that is really different from the others, you would likely
>> worry about the array.
>>
>> Francois
>>
>> Gordon K Smyth wrote:
>>> Dear Erika,
>>>
>>> limma doesn't explicitly handle irregular replicates.  (In my lab, we
>>> haven't had to work with any of the new generation of Agilent arrays
>>> yet, so haven't had to solve the issues with them.)
>>>
>>> Your best bet may be to simply average over the replicates for each
>>> probe, after normalisation, and before using lmFit().  This is not hard,
>>> but requires some programming in R.
>>>
>>> Best wishes
>>> Gordon
>>>
>>> On Tue, 3 Jun 2008, Erika Melissari wrote:
>>>
>>>> Dear Dr Smyth,
>>>>
>>>> I am a PhD student at University of Pisa. I frequently use LIMMA
>>>> package to handle gene expression microarray data. I have a question
>>>> about spot copies management by LIMMA. I know that LIMMA needs all
>>>> spots on the array are in the same number of copy ( e.g. each spot in
>>>> double ). In my research group It is just starting a project in wich
>>>> we use Agilent microarrays (so high density microarrays) and on these
>>>> arrays there is only a block of probes, positioned in a random
>>>> fashion, in more than one spot for each probe. Moreover there is not
>>>> the same number of copies for each probe in this block. Then we have
>>>> not regularly spaced replicate spots on the same array. Please, check
>>>> the gal file by human Agilent microarrays sent as Email attachment, in
>>>> which I highlighted in red some spots (but not all...) to better
>>>> explain to you this situation. Is LIMMA able to manage this situation?
>>>> That is, is LIMMA able to use this kind of random replicated spots to
>>>> perform a quality control procedure, to fit the linear model and to
>>>> produce a unique fold change value for this probe? Can I use any kind
>>>> of strategy to solve this problem? Does It exists a free package that
>>>> does this?
>>>>
>>>> Thank you very much for any information about this topic.
>>>>
>>>> Best Regards
>>>>
>>>> Erika Melissari
>>>>
>>>
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