[BioC] Question about the correct usage of the lme() function (from the nlme package).

Sat Feb 7 21:46:48 MET 2004

*Summary:*
I'm trying to analyze data from an Affymetrix experiment, and I'm trying 
to figure out the right way to invoke lme() for a mixed model with two 
main effects where in one effect each subject is its own control and in 
the other the subjects are independent.

*Intro:*
I apologize for the length of this message... as a former computer 
consultant I know how annoying it is when users ask you for advice and 
then expect you to read their minds. Therefore, I'm erring in the 
opposite direction and including all information that could possibly be 
relevant to this problem.

I'm new to R, so I've tried to get as far as I could on my own without 
bothering this list with naive questions. Unfortunately, I've finally 
reached the limit of reading the documentation, trial, and error. My 
script no longer aborts with syntax errors, but it may still be giving 
incorrect results and I'd never know the difference.

I've got the problem narrowed down to two lines of code, which I've 
marked in the script included below with "#Probably wrong?".

*Experimental Design:*
The first main effect is genotype. Cultured cells from five wild-type 
subjects are used, along with cultured cells from five transgenics.

The second main effect is treatment of these cells with a signalling 
molecule. mRNA for the arrays was collected from each of the 10 cell 
cultures both before and after treatment with this molecule. Therefore, 
each cell culture was its own control.

to recap:

        untreated   treated

wild-type   n=5     n=5

transgenic  n=5     n=5

Total number of microarray experiments: 20
Total number of different individuals: 10

I assume that this is a mixed-model ANOVA problem, and therefore I wrote 
an R script that invokes the lme() function from the nlme package. My 
question is whether I'm getting the syntax right for this model. The way 
I'm doing it is (by inferring from the examples used in the man pages 
and the nlme manual)...

*Code:*
    # I've included a small sample exprSet in the proper
    # format, and if it's placed in the working directory,
    # the following will read it in. Really, any exprSet
    # with the same pData will do.
    eset<-dget("example_data.R")

    # This just lets the script accomodate any number of
    # chips instead of hardcoding it in
    myCols=dim(exprs(eset))[2]
    lmePvals<-function(y){
        myMatrix<-cbind(y[1:myCols],pData(eset)[1:3])
        names(myMatrix)=c("Value","Treatment","Genotype","Subject")
        attach(myMatrix)

    # Probably wrong?
        myMatrixGD<-groupedData(Value~Genotype*Treatment|Subject,myMatrix)

        Treatment<-factor(Treatment)
        Genotype<-factor(Genotype)
        Subject<-factor(Subject)

    # Probably wrong?
        myMatrixRes<-lme(fixed=Value~Genotype*Treatment,
                            random=~Treatment|Subject,
                            data=myMatrixGD,na.action=na.exclude)

        myAnova<-anova(myMatrixRes, test="F")
        cbind(as.numeric(myAnova[,3][2:4]),as.numeric(myAnova[,4][2:4]))
    }

    ta<-function(){esApply(eset,1,lmePvals)}

    lmeOutput<-t(ta())
    dimnames(lmeOutput)[[2]]<-c("F Treatment", "F Genotype", "F 
Interaction",
                                "p Treatment", "p Genotype", "p 
Interaction")

    lmeOutput

-- 
                               --Sincerely, Alex F. Bokov
                                 Department of Physiology
                                 UTHSCSA
------------------------------------------------------------
"Dealing with failure is easy: Work hard to improve. Success 
is also easy to handle: You've solved the wrong problem. 
Work hard to improve."         - Alan Perlis 

-------------- next part --------------
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