[BioC] Multifactorial analysis with RMA and LIMMA of Affymetrix microarrays

Jordi Altirriba Gutiérrez altirriba at hotmail.com
Tue Aug 10 09:12:31 CEST 2004


Here is the answer

HTH

Jordi


BioC] Multifactorial analysis with RMA and LIMMA of Affymetrix microarrays
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From: Gordon Smyth (smyth at landfield.com)
Date: Thu Mar 18 2004 - 02:14:43 EST

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At 02:48 AM 18/03/2004, Jordi Altirriba Guti?rrez wrote:
>Thank you very much Gordon for your quick answer! My phenoData is:
>>pData(eset)
>DIABETES TREATMENT DNT1 TRUE FALSE DNT2 TRUE FALSE DNT3 TRUE FALSE DT1 TRUE 
>TRUE DT2 TRUE TRUE DT3 TRUE TRUE SNT1 FALSE FALSE SNT2 FALSE FALSE SNT3 
>FALSE FALSE ST1 FALSE TRUE ST2 FALSE TRUE ST3 FALSE TRUE
>
>(DNT=Diabetic untreated, DT=Diabetic treated, SNT=Health treated, ST=Health 
>untreated)
>
>I want to know the genes characteristics of the diabetes, the treatment and 
>the treatment + diabetes. Moreover when I analyse my data with SAM and I 
>compare Health treated vs the Health untreated I don't see many 
>differences, but when I compare the Diabetic treated vs the Diabetic 
>treated I see a lot of differences, so is correct to apply a 2 x 2 
>factorial design?


You simply need to fit a model which contains four coefficient which
distinguish your four groups. The classical 2x2 model is just one
particular parametrization you can use:


design <- model.matrix( ~ DIABETES*TREATMENT, data=pData(eset))
fit <- lmFit(eset, design)


>Is LIMMA the correct tool to answer my questions? If it is the correct 
>tool, how can I do a factorial design matrix (if to do a factorial design 
>is correct)? (Robert Gentleman has suggested me to use the factDesign).


You're just fitting a linear model, so the above calculation is exactly
equivalent to what factDesign does, although probably a bit faster. I would
use limma myself because it allows you go on to do empirical Bayes
moderation of the residual standard deviations etc, which I think it
important, but Robert may be able to make a further case for factDesign.


Cheers
Gordon


>Thank you very much for your time, patience and your suggestions. Yours 
>sincerely,




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>From: Peter Lee <peter.d.lee at mcgill.ca>
>To: Jordi Altirriba Gutiérrez <altirriba at hotmail.com>
>CC: bioconductor at stat.math.ethz.ch
>Subject: Re: [BioC] Multifactorial analysis with RMA and LIMMA of 
>Affymetrix microarrays
>Date: Thu, 5 Aug 2004 14:42:12 -0400
>
>What eventually was the correct design matrix for this dataset?
>
>Peter
>
>On Mar 17, 2004, at 10:48 AM, Jordi Altirriba Gutiérrez wrote:
>
>>Thank you very much Gordon for your quick answer!
>>My phenoData is:
>>>pData(eset)
>>     DIABETES TREATMENT
>>DNT1     TRUE       FALSE
>>DNT2     TRUE       FALSE
>>DNT3     TRUE       FALSE
>>DT1      TRUE        TRUE
>>DT2      TRUE        TRUE
>>DT3      TRUE        TRUE
>>SNT1    FALSE       FALSE
>>SNT2    FALSE       FALSE
>>SNT3    FALSE       FALSE
>>ST1     FALSE        TRUE
>>ST2     FALSE        TRUE
>>ST3     FALSE        TRUE
>>
>>(DNT=Diabetic untreated, DT=Diabetic treated, SNT=Health treated,  
>>ST=Health untreated)
>>
>>I want to know the genes characteristics of the diabetes, the  treatment 
>>and the treatment + diabetes. Moreover when I analyse my  data with SAM 
>>and I compare Health treated vs the Health untreated I  don't see many 
>>differences, but when I compare the Diabetic treated vs  the Diabetic 
>>treated I see a lot of differences, so is correct to  apply a 2 x 2 
>>factorial design?
>>Is LIMMA the correct tool to answer my questions? If it is the correct  
>>tool, how can I do a factorial design matrix (if to do a factorial  design 
>>is correct)? (Robert Gentleman has suggested me to use the  factDesign).
>>Thank you very much for your time, patience and your suggestions.
>>Yours sincerely,
>>
>>
>>>From: Gordon Smyth <smyth at wehi.edu.au>
>>>To: Jordi Altirriba Gutiérrez <altirriba at hotmail.com>
>>>CC: bioconductor at stat.math.ethz.ch
>>>Subject: Re: [BioC] Multifactorial analysis with RMA and LIMMA of   
>>>Affymetrix microarrays
>>>Date: Wed, 17 Mar 2004 11:32:16 +1100
>>>
>>>At 07:55 AM 17/03/2004, Jordi Altirriba Gutiérrez wrote:
>>>>(Sorry, but I've had some problems with the HTML)
>>>>Hello all!
>>>>I am a beginner user of R and Bioconductor, sorry if my questions  have 
>>>>already been discussed previously.
>>>>I am studying the effects of a new hypoglycaemic drug for the  treatment 
>>>>of diabetes and I have done this classical study:
>>>>4 different groups:
>>>>1.- Healthy untreated
>>>>2.- Healthy treated
>>>>3.- Diabetic untreated
>>>>4.- Diabetic treated
>>>>With 3 biological replicates of each group, therefore I have done 12  
>>>>arrays (Affymetrix).
>>>>I have treated the raw data with the package RMA of Bioconductor  
>>>>according to the article ”Exploration, normalization and summaries  of 
>>>>high density oligonucleotide array probe level data”  (Background=RMA, 
>>>>Normalization=quantiles, PM=PMonly,  Summarization=medianpolish).
>>>>I am currently trying to analyse the object eset with the package  LIMMA 
>>>>of Bioconductor. I want to know what genes are differentially  expressed 
>>>>due to diabetes,  to the treatment and to the combination  of both 
>>>>(diabetes + treatment), being therefore an statistic  analysis similar 
>>>>to a two-ways ANOVA).
>>>>So, my questions are:
>>>>1.- I have created a PhenoData in RMA, will the covariates of the  
>>>>PhenoData have any influence in the analysis of LIMMA?
>>>
>>>Not automatically.
>>>
>>>>2.- Are these commands correct to get these results? (see below) In  the 
>>>>command TopTable, the output of coef=1 are the genes  characteristics of 
>>>>diabetes?
>>>
>>>No.
>>>
>>>>3.- If I do not see any effect of the treatment in the healthy  
>>>>untreated rats should I design the matrix differently? Something  
>>>>similar to a one-way-ANOVA, considering differently the four groups:
>>>>( > design<-model.matrix(~ -1+factor(c(1,1,1,2,2,2,3,3,3,4,4,4)))  ).
>>>
>>>This design matrix would be very much better, i.e., it would be a  
>>>correct matrix. You could then use contrasts to test for differences  and 
>>>interaction terms between your four groups, and that would do the  job.
>>>
>>>If you tell us what's in your phenoData slot, i.e., type pData(eset),  
>>>then we might be able to suggest another approach analogous to the  
>>>classical two-way anova approach.
>>>
>>>Gordon
>>>
>>>>5.- Any other idea?
>>>>Thank you very much for your time and your suggestions.
>>>>Yours sincerely,
>>>>
>>>>Jordi Altirriba (PhD student, Hospital Clínic ­ IDIBAPS, Barcelona,  
>>>>Spain)
>>>>
>>>>>design<- 
>>>>>cbind("disease"=c(1,1,1,1,1,1,0,0,0,0,0,0),"treatment"=c(0,0,0,1,1,1 
>>>>>,0,0,0,1,1,1))
>>>>>fit<-lmFit(eset,design)
>>>>>contrast.matrix<-cbind("diabetes"=c(1,0),"drug"=c(0,1),"diabetes- 
>>>>>drug"=c(1,1))
>>>>>rownames(contrast.matrix)<-colnames(design)
>>>>>design
>>>>      disease treatment
>>>>[1,]        1           0
>>>>[2,]        1           0
>>>>[3,]        1           0
>>>>[4,]        1           1
>>>>[5,]        1           1
>>>>[6,]        1           1
>>>>[7,]        0           0
>>>>[8,]        0           0
>>>>[9,]        0           0
>>>>[10,]        0           1
>>>>[11,]        0           1
>>>>[12,]        0           1
>>>>>contrast.matrix
>>>>            diabetes drug diabetes-drug
>>>>diabetes      1         0             1
>>>>tratamiento   0         1             1
>>>>>fit2<-contrasts.fit(fit,contrast.matrix)
>>>>>fit2<-eBayes(fit2)
>>>>>clas<-classifyTests(fit2)
>>>>>vennDiagram(clas)
>>>>>topTable(fit2,number=100,genelist=geneNames(eset),coef=1,adjust="fdr ")
>>>>                     Name        M        t      P.Value        B
>>>>11590          1387471_at 19.32575 9.442926 2.743122e-17 29.95299
>>>>2500           1369951_at 19.24748 9.404683 2.743122e-17 29.66737
>>>>10652          1384778_at 19.22968 9.395981 2.743122e-17 29.60254
>>>>

>>>>>sink("limma-diabetes.txt")
>>>>>topTable(fit2,number=1000,genelist=geneNames(eset),coef=1,adjust="fd 
>>>>>r")
>>>>>sink()
>>>
>>
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>>
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>>
>


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