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Peter Lee
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@peter-lee-875
Last seen 8.1 years ago

What eventually was the correct design matrix for this dataset?
Peter
On Mar 17, 2004, at 10:48 AM, 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?
> 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@wehi.edu.au>
>> To: Jordi Altirriba Guti?rrez <altirriba@hotmail.com>
>> CC: bioconductor@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 Guti?rrez 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 Cl?nic ? 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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