Hi,

I am analysing single-channel miRNA microarray data: I have 4 treatments at 4 time points, mostly in triplicate, spread approximately evenly across 2 batches.  Previously I have used ComBat to reduce batch effects and then used Limma and AgiMicroRna to get DE miRNAs.  However, I read on the list somewhere that it is better to add Batch as a factor to the model in Limma rather than try and remove it.

### So this time I added Batch to my targets file...###

FileName Treatment GErep Subject Time Compound Batch
A1.txt  A  1   1  hr4  v  1
A2.txt  A  1   2  hr4  v  2
A3.txt  A  1  3  hr4  v  2
B1.txt  B  2  1  hr4  m  2
B2.txt  B  2  2  hr4  m  1
B3.txt  B  2  3  hr4  m  1
...  (I also have hr8, hr24, hr120 and Compound b and d)

###And did the following...###
TS<-paste(phenoData$Time,phenoData$Compound,phenoData$Batch,sep=".")
TS<-factor(TS,levels=c("hr4.v.1","hr4.m.1","hr4.b.1","hr4.d.1", hr4.v.2","hr4.m.2","hr4.b.2","hr4.d.2",
"hr8.v.1","hr8.m.1","hr8.b.1","hr8.d.1",
"hr8.v.2","hr8.m.2","hr8.b.2","hr8.d.2",
"hr24.v.1","hr24.m.1","hr24.b.1","hr24.d.1",
"hr24.v.2","hr24.m.2","hr24.b.2","hr24.d.2",
"hr120.v.1","hr120.m.1","hr120.b.1","hr120.d.1", "hr120.v.2","hr120.m.2","hr120.b.2","hr120.d.2"))
design<-model.matrix(~0+TS)
fit<-lmFit(minimalSet,design)
### I now get an error message saying that coefficients are not estimable (partial NA coefficients for 260 probes). Last year I ran the same process but without batch and it worked fine.  I would like to find miRNAs that have changed compared to the control at each time point, having taken into account the effect of batch.  Any suggestions as to how I can get it to work?
Thanks very much
Corrinne
(Additionally, I am doing the analysis in R development version as I would like to be able to get CI for log fc)

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