Question: Design Matrix Microarray data analysis
0
gravatar for jeevan92ultimate
5.1 years ago by
India
jeevan92ultimate20 wrote:

Hi,

I'm analyzing a microarray data and stuck at the design matrix command. 

I have few samples, in which I divided them into two groups i.e. "Controls" & "Diseased".

I assigned the samples information as a factor with two levels "C" & "D".

In what exact order should I assign the levels? If I assign levels as C, D then I get the logFC value as say X and if I do it as D, C I get the logFC value as -X.

Should I start with the diseased group (D) or control group (C)?

In default, in what exact order the design matrix take the levels?

 

My code is as follows: 

info$Group<-factor(info$Group, levels=c("C","D"))  

# ^^^ Which one should I consider first in the above command? The diseased group or control group? ^^^

levels(info$Group)

lev<-levels(info$Group)

design<-model.matrix(~0+info$Group)
colnames(design)<-lev
dim(design)
head(design)
fit<-lmFit(exp, design)
names(fit)
contr.str <- c()
len<-length(lev)
for(i in 1:(len-1))contr.str<-c(contr.str, paste(lev[(i+1):len], lev[i], sep="-"))
contr.str
contr.mat<-makeContrasts(contrasts=contr.str, levels=lev)
fit2<-contrasts.fit(fit, contr.mat)
fit2<-eBayes(fit2)
names(fit2)
top <- topTable(fit2, number=nrow(fit2), adjust.method="fdr")

Thanks in advance

ADD COMMENTlink modified 5.1 years ago • written 5.1 years ago by jeevan92ultimate20

It doesn't matter computationally, but it makes more sense to use the control sample as your referent, so levels = c('C', 'D') is a bit more pragmatic. 

ADD REPLYlink written 5.1 years ago by russhh4.6k

Thanks a lot :)

ADD REPLYlink written 5.1 years ago by jeevan92ultimate20
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