[BioC] Please advice

[Naomi Altman]

Dear Mali,
The reasons that we advise people to keep their questions on the 
mailing list are that there are more people available to assist you 
with your questions and everyone can benefit from the answers.

I do not have time to go through every contrast that you set up.  The 
treatment matrix including the batch is correct.  The first few 
contrasts are correct. The batch effect removes the mean value for each batch.

I did not check the second analysis.

Regards,
Naomi


At 07:05 AM 3/7/2011, mali salmon wrote:
>Dear Naomi
>First I would like to apologize for writing you directly.
>My name is Mali Salmon, I am a researcher in Tel Hashomer hospital in Israel.
>I am currently analyzing microarray data using limma package, and I 
>would like to have some advice from a statistician who has an 
>experience with microarray analysis using Limma and bioconductor.
>I was searching the mailing list, and saw your name as one who 
>assist many people with their analysis.
>I have a simple factorial design which I build the design and 
>contrast matrix for, and I just want to be sure that what I did is 
>correct, I would be very grateful is you could give me your opinion.
>
>I have two factors, one is strain with three levels (C,D,S), and a 
>treatment factor with two levels: treated (DSR) and untreated (NO), 
>I also have batch effect which I included in the matrix
>
> > targets
>
>    FileName Treatment Strain batch
>1     NO2_C        NO      C     1
>2    DSR2_C       DSR      C     1
>3     NO2_D        NO      D     1
>4    DSR2_D       DSR      D     1
>5     NO2_S        NO      S     1
>6    DSR2_S       DSR      S     1
>7     NO3_C        NO      C     2
>8    DSR3_C       DSR      C     2
>9     NO3_D        NO      D     2
>10   DSR3_D       DSR      D     2
>11    NO3_S        NO      S     2
>12   DSR3_S       DSR      S     2
>
>I am trying to follow example 8.7 in the user guide (factorial design)
>In order to build the design matrix this is what I did:
>
>TS <- paste(targets$Treatment, targets$Strain, sep=".")
>TS <- factor(TS, unique(TS))
>design<-model.matrix(~0+TS)
>colnames(design) <- levels(TS)
>batch<-c(0,0,0,0,0,0,1,1,1,1,1,1)
>design<-cbind(design,batch)
>
> > design
>    NO.C DSR.C NO.D DSR.D NO.S DSR.S batch
>1     1     0    0     0    0     0     0
>2     0     1    0     0    0     0     0
>3     0     0    1     0    0     0     0
>4     0     0    0     1    0     0     0
>5     0     0    0     0    1     0     0
>6     0     0    0     0    0     1     0
>7     1     0    0     0    0     0     1
>8     0     1    0     0    0     0     1
>9     0     0    1     0    0     0     1
>10    0     0    0     1    0     0     1
>11    0     0    0     0    1     0     1
>12    0     0    0     0    0     1     1
>
>
>Now the questions I would like to answer are:
>1. what is the main effect of the strain (here I want to ignore the 
>treatment, just to find the difference between different strains)
>2. what is the main effect of the treatment (genes that change their 
>expression because of treatment, again I want to ignore the strain here).
>3. genes that are respond to treatment in each strain + interaction
>
>Below is how I created the contrast.matrix, the first three 
>contrasts are to answer the first question, contrast number 4 is to 
>answer question 2, and the last 6 contrasts to answer question 3
>
>cont.matrix <- makeContrasts(
>      DvsC=(DSR.D+NO.D)-(DSR.C+NO.C),
>
>      SvsC=(DSR.S+NO.S)-(DSR.C+NO.C),
>      DvsS=(DSR.D+NO.D)-(DSR.S+NO.S),
>      DSRvNO=(DSR.C+DSR.D+DSR.S)-(NO.C+NO.D+NO.S),
>      C.DSRvsNO=DSR.C-NO.C,
>      D.DSRvsNO=DSR.D-NO.D,
>      S.DSRvsNO=DSR.S-NO.S,
>      DiffDvsC=(DSR.D-NO.D)-(DSR.C-NO.C),
>      DiffSvsC=(DSR.S-NO.S)-(DSR.C-NO.C),
>      DiffSvsD=(DSR.S-NO.S)-(DSR.D-NO.D),
>      levels=design)
>fit2 <- contrasts.fit(fit, cont.matrix)
>fit2 <- eBayes(fit2)
>
>Is this matrix correct?
>The batch is included in the design matrix, is that mean that the 
>batch will be removed?
>
>One of the comparisons I am interested with is the different between 
>strains (first question). Would you suggest to do a separate 
>"several group" analysis (example 8.6 in limma user guide) instead 
>of the one indicated above?
>The analysis for answer the first question would be:
>
>design.Strain <- model.matrix(~ 0+factor(c(1,1,2,2,3,3,1,1,2,2,3,3)))
>colnames(design.Strain) <- c("C", "D", "S")
>#correct for batch effect
>batch<-c(0,0,0,0,0,0,1,1,1,1,1,1)
>design.Strain<-cbind(design.Strain,batch)
>fit.Strain <- lmFit(selDataMatrix, design.Strain)
>contrast.matrix.Strain <- makeContrasts(D-C, S-D, S-C, levels=design.Strain)
>fit2.Strain <- contrasts.fit(fit.Strain, contrast.matrix.Strain)
>fit2.Strain <- eBayes(fit2.Strain)
>
>I have tried the two approaches, and I got different list of 
>differential expressed genes, so which one is preferred?
>
>Looking forward to your reply, and apologising for bothering you
>Thank you
>Mali
>
>