Limma for Fluidigm high throughput PCR

Limma for Fluidigm high throughput PCR

1

@3184ac53

Last seen 3 days ago

United States

Hello!

I recently did a biomark fluidigm high throughput qPCR experiment with a 96×96 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)


limma


Limma


Fluidigm

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