[altirriba@hotmail.com: [BioC] Design in factDesign] (fwd)

[Jordi Altirriba Gutiérrez]

Thank you very much for your prompt answer Denise!
Yes, you are right, the command FUN=mainES is wrong, I did this mistake when 
I copied the commands to the mail (sorry). Therefore, the other commands are 
correct.
But there is something that I don't understand (sorry if it is a very basic 
question), because when I want to get the genes that are differentially 
expressed due to diabetes I "fit" my data to the functions: lm(y ~ DIABETES 
+ TREATMENT + DIABETES * TREATMENT) and lm(y ~ TREATMENT). Therefore the 
genes that "fit" better to the first function are differentially expressed 
due to diabetes, but why don't I fit my data to the functions: lm(y ~ 
DIABETES + TREATMENT + DIABETES * TREATMENT) and lm(y ~ TREATMENT + DIABETES 
* TREATMENT)? I know that the parameter DIABETES * TREATMENT is the 
intersection of the other two parameters, but it should be independent of 
these parameters.
Thank you very much for your comments and help! (and your patience)

Jordi Altirriba
IDIBAPS - Hospital Clinic (Barcelona, Spain)
>
>Hello Jordi,
>
>My comments to your questions are below.  I hope this helps.  -Denise
>
>__________________________________________________________________________
>Denise Scholtens
>Department of Biostatistics
>Harvard School of Public Health
>dscholte at hsph.harvard.edu
>
>Hi all!
>I’ve been using RMA and LIMMA to analyse my data and I am currently trying
>to analyse it with the package factDesign. My design is a 2x2 factorial
>design with 4 groups: diabetic treated, diabetic untreated, health treated
>and health untreated with 3 biological replicates in each group. I want to
>know what genes are differentially expressed due to diabetes,  to the
>treatment and to the combination of both (diabetes + treatment).
>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
>
>Are these commands correct to get the results listed below? Where are the
>errors?
> >lm.full<-function(y) lm(y ~ DIABETES + TREATMENT + DIABETES * TREATMENT)
> >lm.diabetes<-function(y) lm(y ~ DIABETES)
> >lm.treatment<-function(y) lm(y ~ TREATMENT)
> >lm.diabetestreatment<-function(y) lm(y ~ DIABETES + TREATMENT)
> >lm.f<-esApply(eset, 1, lm.full)
> >lm.d<-esApply(eset, 1, lm.diabetes)
> >lm.t<-esApply(eset, 1, lm.treatment)
> >lm.dt<-esApply(eset, 1, lm.diabetestreatment)
>
>#####
># Yes, these commands look correct for making the linear models and
># running them for the exprSet called eset.
>######
>
>## To get the genes characteristics of the treatment:
> >Fpvals<-rep(0, length(lm.f))
> >for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.d[[i]], lm.f[[i]])$P[2]}
> >Fsub<-which(Fpvals<0.01)
> >eset.Fsub<-eset[Fsub]
> >lm.f.Fsub<-lm.f[Fsub]
> >betaNames<-names(lm.f[[1]] [["coefficients"]])
> >lambda<-par2lambda(betaNames, c("TREATMENTTRUE"), c(1)) ## I get the same
> >genes if I write : > lambda2 <- par2lambda (betaNames,
> >list(c("TREATMENTTRUE" , "DIABETESTRUE:TREATMENTTRUE")),list( c(1,1)))
> >mainTR<-function(x) contrastTest(x,lambda,p=0.1) [[1]]
> >mainTRgenes<-sapply(lm.f.Fsub, FUN=mainES)
>
>#####
># I think the problem is the use of mainES rather than mainTR in the last
># sapply. mainES is a function that is defined in the factDesign vignette
># - your own function should be used here instead.  If you define the
># function differently for different contrasts, my guess is you will see
># different gene lists for the lambda and lambda2 defined above.
>#####
>
>
>## To get the genes characteristics of the diabetes:
> >for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.t[[i]], lm.f[[i]])$P[2]}
> >Fsub<-which(Fpvals<0.01)
> >eset.Fsub<-eset[Fsub]
> >lm.f.Fsub<-lm.f[Fsub]
> >betaNames<-names(lm.f[[1]] [["coefficients"]])
> >lambda<-par2lambda(betaNames, c("DIABETESTRUE"), c(1)) ## I get also the
> >same genes if I consider the intersection DIABETESTRUE:TREATMENTTRUE.
> >mainDI<-function(x) contrastTest(x,lambda,p=0.1) [[1]]
> >mainDIgenes<-sapply(lm.f.Fsub, FUN=mainES)
>
>#####
># See above comments.
>#####
>
>## To get the genes characteristics of the diabetes+treatment:
> >for (i in 1:length(lm.f)) {Fpvals[i]<-anova(lm.dt[[i]], lm.f[[i]])$P[2]}
> >Fsub<-which(Fpvals<0.01)
> >eset.Fsub<-eset[Fsub]
> >lm.f.Fsub<-lm.f[Fsub]
> >  betaNames<-names(lm.f[[1]] [["coefficients"]])
> >lambda<-par2lambda(betaNames, c("DIABETESTRUE:TREATMENTTRUE"), c(1))
> >mainDT<-function(x) contrastTest(x,lambda,p=0.1) [[1]]
> >mainDTgenes<-sapply(lm.f.Fsub, FUN=mainES) ## I don’t get any “fail to
> >reject” gene.
>
>#####
># Again, I think changing mainES to mainDT will do the trick.
>#####
>
>
>When I get the “rejected” and the “failed to reject” genes, can I classify
>them by their Fvalues? How?
>
>#####
># Currently, the contrastTest function only returns the contrast estimate
># (cEst), the pvalue from the F-test (pvalue), and a statement of either
># "REJECT" or "FAIL TO REJECT" based on the p-value cutoff you specify.
># This can be changed to return the F-value as well, and I'm happy to put
># this change into the package.  Then you can use the Fvalues for whatever
># you would like.
>#
># One thing to consider if you are going to use p-values from the F tests
># to select genes - you will want to corrent for multiple testing.  The
># multtest package is very useful for this.
>######
>
>
>Thank you very much for your comments and help.
>Yours sincerely,
>
>Jordi Altirriba
>IDIBAPS-Hospital Clinic (Barcelona, Spain)

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