[BioC] Limma lmFit function and spot quality weights

[Gordon K Smyth]

Dear Benoit,

What makes you think you need to set quality weights at all?  You say that 
you're not confident about the weights you're setting, so they are best 
just left alone.  With sensible background correction and normalization, 
you'll will almost always get reasonable results without needing to set 
individual quality weights.  Spot weights are frankly overused. 
Frequently they make things worse, as it seems in your case.

Your intensity filtering sounds ok.  Should be done after normalization 
but before lmFit.

BTW, these are very general questions, best addressed to everyone not just 
to me.  They've been answered a number of times before on this list.

Best wishes
Gordon

> Date: Fri, 31 Jul 2009 10:03:45 +0200
> From: Benoit <benoit.loup at jouy.inra.fr>
> Subject: Re: [BioC] Limma lmFit function and spot quality weights
> To: Bioconductor mailing list <bioconductor at stat.math.ethz.ch>
>
> Dear Gordon,
>
> Thank you for your answer.
> I understand the interest to make comparisons even with one sample per
> group, it could be informative but in my analysis I try to identify
> robust differential expressed genes.
> Concerning the weights, I am a very new user in R and Limma and it's not
> easy to generate appropriate weights. Curently, I'm using 0 and 1 flags.
> I know that it's possible to use intermediate weight values but I don't
> know on which probe allocate these values.
> Another question is, is it judicious to filter before or after fitting
> linear model ?
> On affy data, I apply filtering on the PMA call before analysing with
> limma and keep probe with only a minimal number of "P" calls per group.
> Do you think it is pertinent or is it a bad method ?
>
> Thanks,
>
> Benoit
>
> Gordon K Smyth a ?crit :
>> Dear Benoit,
>>
>> It doesn't seem to me to be desirable to place restrictions on the
>> weights that people can specify to lmFit. In some cases it is
>> desirable to be able to make comparisons for probes with only one
>> available sample per group.
>>
>> On the other hand, this does means that you are responsible for the
>> weights you create, and you may get poor results if you input weights
>> that are innappropriate for the data.
>>
>> Best wishes
>> Gordon
>>
>>> Date: Mon, 27 Jul 2009 13:41:06 +0200
>>> From: Benoit <benoit.loup at jouy.inra.fr>
>>> Subject: [BioC] Limma lmFit function and spot quality weights
>>> To: bioconductor at stat.math.ethz.ch
>>>
>>> Hello,
>>> I'm using Limma to assess differential expression on double colour
>>> microarray data and have a question about the lmFit function.
>>> When I fit linear model using lmFit, as I understood, the function uses
>>> the weights extracted from the MA object when present and/or specified.
>>> Thus, I tried fitting with and without the spot quality weights and I
>>> found different results (not very surprising in fact).
>>> In fact, when I used weights, zero weighted spots seemed to be removed
>>> from the analysis and it's here that I have a problem.
>>>
>>> For my experiment, I compare two groups (control vs treated) in a
>>> classical design experiment "Two Groups: Common Reference" as describe
>>> in the Limma documentation.
>>>
>>> design=modelMatrix(targets,ref="ref")
>>> design
>>> fit=lmFit(MA,design,weights=MA$weights)
>>> /alternative without weights : fit=lmFit(MA,design,weights=NULL)/
>>> cont.matrix=makeContrasts(pollutedVScontrol=polluted-control,polluted,control,levels=design)
>>>
>>> cont.matrix
>>> fit2=contrasts.fit(fit,cont.matrix)
>>> fit2=eBayes(fit2)
>>> res=toptable(coef=1,number=15744,fit=fit2,genelist=fit2$genes,adjust.method="BH",A=fit2$Amean,eb=fit2,p.value=0.01)
>>>
>>>
>>> The difference between the analysis with and without weights is that
>>> when I use weights new genes highly differentially expressed appeared.
>>> When I control these genes, in fact they correspond to spots that are
>>> flagged (0) on the majority of the arrays (i.e. only one weight at 1 for
>>> the control and one weight at 1 for the treated). Thus for these genes
>>> the comparison is performed only one "control array" versus one "treated
>>> array".
>>> So is it possible to specify to lmFit that there must be a minimum of
>>> "1" weights or a maximum "0" weights per groups of array ?
>>>
>>> Thank you for any help you can bring me.
>>>
>>> Benoit
>>>
>>> --
>>> Benoit Loup, PhD
>>> UMR Biologie du D?veloppement et Reproduction
>>> Diff?renciation des Gonades et Perturbations
>>> INRA ? Domaine de Vilvert
>>> B?timent Jacques Poly
>>> 78350 Jouy en Josas
>>> France
>>>
>>> Tel: 33 1 34 65 25 38
>>> Fax: 33 1 34 65 22 41
>>> E-mail: benoit.loup at jouy.inra.fr
>>
>>
>
> -- 
> Benoit Loup, PhD
> UMR Biologie du D?veloppement et Reproduction
> Diff?renciation des Gonades et Perturbations
> INRA ? Domaine de Vilvert
> B?timent Jacques Poly
> 78350 Jouy en Josas
> France
>
> Tel: 33 1 34 65 25 38
> Fax: 33 1 34 65 22 41
> E-mail: benoit.loup at jouy.inra.fr