[BioC] a question about LIMMA

Thu Jun 12 17:57:33 CEST 2008

Dear Francois,

I followed your suggestion about cheking the behavior of replicate probes, 
checking in an old experiment performed by using Agilent 44k rat whole 
genome arrays .
I have found a "strange" result.
I copy for you only one of these strange results.

       F635 Median - B635 F532 Median - B532 F635 Mean - B635 F532 Mean - 
B532 norm ratio
      Prkce NM_017171 A_42_P757370 3207 3264 3013 2967 1.210832876
     NM_017171 A_44_P481629 2566 2461 2605 2544 0.756496281
     NM_017171 A_44_P311955 404 457 427 454 0.002685538

This concerns Prkce gene and you can find GenePix 6.0 background subtracted 
signal intensities and normalized ratio by LOESS.
As you can see, the third probe have not actually the same signal as the 
others. I have checked the spot image, but there are not any problem and, 
sincerely,  there are not any problem on all the array.
At this point, I have thought to be a good idea to carry out an alignment of 
these probes and the Prkce rat transcriptome by using BLAST. I have found 
that the first two probes are placed near 3' end of this gene, whereas the 
third is far away this end.
To be more precise these are the results:

Prkce RNA length 1-2701 (5'->3')
A_42_P757370    2305-2364
A_44_P481629    2400-2459
A_44_P311955    425-484

The third probe is very close to 5' end!
I have found a similar situation for other replicate probes.
Then, I am thinking there is a problem in cDNA synthesis, that is perhaps 
the retrotranscription enzyme is not able to copy all the transcript and it 
is for this reason that the third probe have a signal so much low and 
different from the other two probes.
In your opinion is this a good explanation?
For this old experiment we did not use all the protocol by Agilent. 
Particularly, we did not use Quick Amp Labeling Protocol of Agilent, but we 
preferred Amino Allyl MessageAmp aRNA Amplification Kit by Ambion.
Do you use all the Agilent system, included the Agilent Kit?
Have you noticed any problem similar to that showed by our data?

Thank you for your attention and for your kind help.

Best Regards,

Erika

----- Original Message ----- 
From: "Francois Pepin" <fpepin at cs.mcgill.ca>
To: "Erika Melissari" <erika.melissari at bioclinica.unipi.it>
Cc: <bioconductor at stat.math.ethz.ch>
Sent: Thursday, June 05, 2008 18:30 PM
Subject: Re: [BioC] a question about LIMMA

> 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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