[BioC] remove microarray batch effects using Limma

[Xie, Zhi (NIH/NHLBI) [E]]

Hi everyone,

I have some microarray data files containing two sets of samples in
normal and disease condition. I have tested that the data also contain
significant batch effects with hybridization time. However, the
positive hits I obtained using the following approaches are very
different (using the same cutoff value in decideTests function). I
think I am supposed to use the first approach but I am surprised to
see a big difference between the two approaches. could anyone help
figure out the reasons?

Thanks,

Zhi Xie

NIH/NHLBI


Here eset is the expression dataset after RMA function.

___________________
Approach 1:
# Consider batch effects in the model matrix
design<-model.matrix(~0+condition.factor+batch.factor)

# fit the linear model
fit<-lmFit(eset,design)

Then I create contrast matrix and compute coefficients and errors
using contrast.fit function
___________________
Approach 2:
# remove batch effects first
exp.eset.rm.batch<-removeBatchEffect(exprs(eset),batch.factor)

# only consider normal and disease conditions in the model matrix
design<-model.matrix(~0+condition.factor)

# fit the linear model
fit<-lmFit(eset.rm.batch,design)
where eset.rm.batch is the expression dataset containing expression
values from exp.eset.rm.batch table