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jms2520 • 0@3184ac53
Last seen 10 weeks ago
I recently did a biomark fluidigm high throughput qPCR experiment with a 96x96 format (i.e. 96 samples by 96 genes). My experimental design is rather complex with both control and experimental groups as well as timecourse data. I was hoping to use limma to analyze this data.
#Since my design is rather complex, I start by organizing my targets targets<-readTargets("Targets.txt") #Then I read in a table with my deltaCt values normalized to my housekeeing gene B2m deltaCt<-read.table("B2m_limma.txt",header=T,sep="\t",row.names=1) #Next I transform to log2 expression y<- max(deltaCt) - deltaCt #I set the different types of groups I would like to compare in different levels flevels<-unique(targets$Group) flevels f<-factor(targets$Group,levels=flevels) des<-model.matrix(~0+f) colnames(des)<-flevels fit<-lmFit(y,des) contrast.matrix <- makeContrasts( Female7=FKO7-FWT7 Male7= MKO7-MWT7 Female15= FKO15-FWT15 Male15= MKO15-MWT15 Female30= FKO30-FWT30 Male30= MKO30-MWT30 ,levels=des) fit<-contrasts.fit(fit,contrast.matrix) fit<-eBayes(fit) options(digits=3)
My questions then become 1) Is this the correct way to read in this data and perform the analysis? 2) There are a few wells that did not work and/or there was no expression. How do I deal with these values in the data? (Their ct values are recorded as "999" as a default from the biomark software)