I'm using limma package to count differential gene expression. I need to make a table of logFC of genes specific for my cells of interest (there are 62 of them). What should I write to make such a table (where should I place the names of the genes)? My code at the moment: Code should be placed in three backticks as shown below

gset <- getGEO("GSE24742", GSEMatrix =TRUE, AnnotGPL=TRUE)
if (length(gset) > 1) idx <- grep("GPL570", attr(gset, "names")) else idx <- 1
gset <- gset[[idx]]

fvarLabels(gset) <- make.names(fvarLabels(gset))
make.names(fvarLabels(gset))

gsms <- "010101010101010101010101"
sml <- strsplit(gsms, split="")[[1]]
sml

ex <- exprs(gset)
qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))
LogC <- (qx[5] > 100) ||
  (qx[6]-qx[1] > 50 && qx[2] > 0)
if (LogC) { ex[which(ex <= 0)] <- NaN
exprs(gset) <- log2(ex) }

gs <- factor(sml)
gs
groups <- make.names(c("control","test"))
levels(gs) <- groups
gset$group <- gs
design <- model.matrix(~group + 0, gset)
colnames(design) <- levels(gs)
View(design)
fit <- lmFit(gset, design)  

cts <- paste(groups[1], groups[2], sep="-")
cont.matrix <- makeContrasts(test-control, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
cont.matrix

fit2 <- eBayes(fit2, 0.01)
tT1 <- topTable(fit2, adjust="fdr", sort.by="B", number=50000)
tT1 <- subset(tT1, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
write.table(tT1, file=stdout(), row.names=F, sep="\t")
View(tT1)

I guess the function "topTable" must be modified somehow or replaced with something else. I will be very grateful for any help.

Making a table for specified genes

It's easy to subset the toptable for any desired set of genes:

i <- tT1$Gene.symbol %in% MyGeneList
tT1.MyGenes <- tT1[i, ]

The samples are paired

However your current analysis is incorrect because it ignores the pairing of the samples. There is also a strong mean-variance trend. A correct analysis would be:

gset <- getGEO("GSE24742", GSEMatrix =TRUE, AnnotGPL=TRUE)[[1]]
exprs(gset) <- logs(exprs(gset))
Patient <- gl(12,2,24)
Treatment <- gl(2,1,24)
plotMDS(gset,label=paste(Patient,Treatment,sep="."))
design <- model.matrix(~Treatment+Patient)
fit <- lmFit(gset,design)
fit <- eBayes(fit,trend=TRUE)
tab <- topTable(fit,coef="Treatment2",n=Inf)

Beware that this dataset shows no significant results

You should be aware that this dataset shows no statistically significant responses to treatment. The original publication used ordinary t-tests and failed to adjust the p-values for multiple testing. In my opinion, claiming any results from this dataset would be scientifically unsound.