Dear Bioconductor experts,

We have data from a homemade one-channel microarray that I tried to apply
limma for differential expression analysis between matched paired Normal
(N) and Tumor (Tumor) samples - 8 biological replicates (one tech replicate
has been averaged after normalization). All samples are formatted in one
matrix (M).

Signals have been quantile normalized between each paired normal and tumor.
Signal values below 5 (log scale) have been replaced by "NA" since they are
potentially noises. So there are many NAs in M.

I followed the user manual and made the codes below.

I think the code is correct? My questions are (1) how to deal with NAs - as
I did a search but no clear idea (2) how do people do the statistics at the
gene level for one gene having multiple probes - averaging or taking median?

Thanks,
Zhengyu


 > head(M)
         N1       N2       N3       N4       N5       N6       N7
N8       T1        T2       T3
2  8.622724 7.423568       NA       NA 7.487174       NA 8.516293       NA
7.876259  7.856707       NA
         T4       T5       T6       T7       T8
2        NA 7.720018       NA 7.752550       NA

> eset<-as.matrix(M)
> Pair=factor(targets$Pair)
>     Treat=factor(targets$Treatment,levels=c("N","T")) # compared matched
normal to tumors
>               design<-model.matrix(~Pair+Treat)
> targets
   FilenName Pair Treatment
1         N1    1         N
2         N2    2         N
3         N3    3         N
4         N4    4         N
5         N5    5         N
6         N6    6         N
7         N7    7         N
8         N8    8         N
9         T1    1         T
10        T2    2         T
11        T3    3         T
12        T4    4         T
13        T5    5         T
14        T6    6         T
15        T7    7         T
16        T8    8         T
fit_pair<-lmFit(eset,design)
             fit_pair<-eBayes(fit_pair)

 R=topTable(fit_pair, coef="TreatT", adjust="BH",number=30) # display top 30

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