I think that the GSE68744 authors have made things rather difficult for you with their nonstandard design and data entry. I am also not a fan of Universal Reference -- direct comparison between epithelium and stroma on the same arrays would have been far more precise and economical. Anywhere, here is how I would read and analyze this data.
First, create a two-color targets frame as in Section 4.3 of the limma User's Guide. You need to create columns Cy3 and Cy5 recording the samples labelled green and red for each microarray:
> head(targets)
geo tissue tumor dye title
1 GSM1680263 epithelium 12 Cy5 012E_rep1
2 GSM1680264 epithelium 12 Cy3 012E_rep2
3 GSM1680265 stroma 12 Cy5 012S_rep1
4 GSM1680266 stroma 12 Cy3 012S_rep2
5 GSM1680267 epithelium 22 Cy3 022E_rep1
6 GSM1680268 epithelium 22 Cy5 022E_rep2
> targets$Cy3 <- "reference"
> targets$Cy5 <- "reference"
> i <- targets$dye=="Cy3"
> targets$Cy3[i] <- targets$tissue[i]
> i <- targets$dye=="Cy5"
> targets$Cy5[i] <- targets$tissue[i]
> head(targets)
geo tissue tumor dye title Cy3 Cy5
1 GSM1680263 epithelium 12 Cy5 012E_rep1 reference epithelium
2 GSM1680264 epithelium 12 Cy3 012E_rep2 epithelium reference
3 GSM1680265 stroma 12 Cy5 012S_rep1 reference stroma
4 GSM1680266 stroma 12 Cy3 012S_rep2 stroma reference
5 GSM1680267 epithelium 22 Cy3 022E_rep1 epithelium reference
6 GSM1680268 epithelium 22 Cy5 022E_rep2 reference epithelium
Create the design matrix:
> design <- modelMatrix(targets,ref="stroma")
> design <- cbind(Dye=1,design)
Read and normalize the data:
> f <- dir(pattern="GSM*")
> f[1:4]
[1] "GSM1680263_US22502628_251239119555_S01_A01.txt.gz"
[2] "GSM1680264_US22502628_251239119556_S01_A01.txt.gz"
[3] "GSM1680265_US22502628_251239112288_S01_A01.txt.gz"
[4] "GSM1680266_US22502628_251239112309_S01_A01.txt.gz"
> RG <- read.maimages(f,source="agilent")
> RGb <- RG[RG$genes$ControlType==0,]
> RGb$genes$ControlType <- NULL
> RGb <- backgroundCorrect(RGb,method="normexp")
> MA <- normalizeWithinArrays(RGb,method="loess")
Now find genes DE between epithelium and stroma:
> fit <- lmFit(MA,design)
> fit <- eBayes(fit)
> options(digits=3)
> topTable(fit,coef="epithelium",n=30)[,c(5,8:12)]
GeneName logFC AveExpr t P.Value adj.P.Val
14880 LAMA4 -2.49 9.80 -18.0 4.39e-39 1.83e-34
16953 NNMT -2.59 11.20 -17.6 3.90e-38 5.88e-34
26775 HOM-TES-103 -1.29 10.18 -17.6 4.23e-38 5.88e-34
36196 TGFBI -2.25 11.92 -17.3 3.00e-37 3.13e-33
28357 FYN -1.21 9.24 -17.2 4.99e-37 4.16e-33
38249 SERPINF1 -1.91 10.54 -17.0 1.21e-36 7.79e-33
22500 ITM2C -2.36 11.80 -17.0 1.31e-36 7.79e-33
29003 AEBP1 -2.41 9.14 -16.9 1.88e-36 9.80e-33
19700 TPM2 -2.07 10.61 -16.8 3.53e-36 1.63e-32
11159 MGC4083 -2.34 11.93 -16.6 1.60e-35 6.65e-32
37562 COTL1 -1.85 11.97 -16.0 5.04e-34 1.91e-30
30614 CHES1 -1.95 10.71 -15.6 6.17e-33 2.14e-29
27502 TPM2 -2.00 9.63 -15.4 1.93e-32 6.18e-29
17826 BC004295 -2.56 11.93 -15.3 3.23e-32 9.62e-29
22819 THC1496865 -2.76 7.24 -15.2 3.98e-32 1.09e-28
20585 COTL1 -1.71 12.18 -15.2 4.18e-32 1.09e-28
21173 GNG11 -2.36 11.08 -15.2 5.48e-32 1.34e-28
13897 ANTXR2 -1.62 9.86 -15.0 1.85e-31 4.28e-28
37720 BC032783 -2.60 11.27 -14.9 2.37e-31 5.20e-28
24240 IFI16 -2.27 9.42 -14.9 2.59e-31 5.40e-28
30020 T1A-2 -2.52 9.45 -14.9 3.06e-31 6.08e-28
21054 AF131762 -2.18 9.16 -14.8 4.70e-31 8.91e-28
42630 AK021980 -2.83 7.96 -14.6 2.41e-30 4.37e-27
5412 AIF1 -2.26 9.60 -14.5 2.96e-30 5.14e-27
4287 I_1152228 -2.05 10.20 -14.5 3.39e-30 5.66e-27
29726 HEPH -2.29 8.71 -14.5 4.39e-30 7.03e-27
25520 DKFZP566K1924 -2.16 8.88 -14.4 5.23e-30 8.07e-27
981 AU121101 -1.71 9.83 -14.3 1.23e-29 1.83e-26
27696 M36501 -2.27 12.34 -14.3 1.31e-29 1.88e-26
27158 INHBA -1.97 8.61 -14.2 1.59e-29 2.20e-26
There are 6000 up and 4582 down DE genes at FDR < 0.05:
> summary(decideTests(fit))
Dye epithelium reference
Down 3390 4582 21351
NotSig 30846 31093 3797
Up 7439 6000 16527
Note that the top 30 DE genes are all down-regulated in epithelium relative to stroma but, overall, there are more genes up in epithelium. It would seem that the genes with largest fold changes are down-regulated.
You can see the assymetry between up and down genes using suitable plots:
> volcanoplot(fit,coef="epithelium")
> plotMD(fit,coef="epithelium")
Can you copy/paste the actual output of
summary(decideTests(fit))
as well as the output from the following command, please?