Question about the correct usage of the lme() function (from the nlme package).
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@alex-f-bokov-627
Last seen 9.6 years ago
*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 -------------- structure(list(), exprs = structure(c(8.22399398015545, 12.4132278440884, 7.68233016785921, 10.0298584503781, 11.2294400170450, 5.35283491446067, 10.6504347543323, 4.82963220633748, 9.63958376599006, 10.4197885462594, 7.81390487813497, 12.5039859724549, 7.16159069936732, 10.1478828492562, 10.9871734333764, 5.45886711193606, 10.7291076616034, 4.72040634408881, 9.97862416453462, 10.5025062066800, 7.62084688449234, 12.5275553915705, 7.21794241277868, 10.0837354364152, 11.1026267769378, 5.25530298838706, 10.7183348182704, 4.33385532481525, 9.90775114321463, 10.3341625595568, 7.67254359720526, 12.3907630441080, 7.027677047243, 10.1458995733497, 10.9923509182642, 5.39471617578218, 10.6914821740839, 4.60233056251545, 9.8720353190966, 10.3222036578613, 7.70239198952856, 12.4836420891106, 7.39405732897098, 9.8836048006648, 11.0014326130650, 5.41938351086007, 10.7482932421680, 4.25328518019665, 9.88439928674076, 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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n", url = "http://www.uthscsa.edu/", samples = structure(list( description = c("Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.", "Your ad here.")), .Names = "description"), hybridizations = list(), normControls = list(), preprocessing = structure(list( filenames = c("g:\\all users\\documents\\cat microarray\\12 2003\\All unlogged\\/chen1 (1R) wt_c.CEL", "g:\\all users\\documents\\cat microarray\\12 2003\\All unlogged\\/chen1 (2) cat_c.CEL", "g:\\all users\\documents\\cat microarray\\12 2003\\All unlogged\\/chen1 (3) wt_TNF.CEL", "g:\\all users\\documents\\cat microarray\\12 2003\\All unlogged\\/chen1 (4) 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Microarray Annotation Normalization Preprocessing Microarray Annotation Normalization • 1.3k views
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