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@jordi-altirriba-gutierrez-682

Last seen 3.5 years ago

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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?
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?
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))) ).
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="fdr
")
>sink()
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