[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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This archive was generated by hypermail 2.0.0 : Fri Jul 16 2004 - 13:13:33
EDT
>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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