Last seen 7 hours ago
WEHI, Melbourne, Australia
RT-PCR data are almost always delta CT values, and the GEO entry tells you that this is so. You can analyse delta CT values using the limma package.
Just read the values into a matrix:
> x <- read.delim("GSE92776_Non-normalized_data.txt",row.names="ID_REF")
 347 337
Now CT values are inversely correlated with expression, so you need to reverse them to represent log2-expression values:
> y <- max(x) - x
X100.B0100V1.BASELINE X101.B0101V1.BASELINE X102.B0102V1.BASELINE
ABCA1-Hs01059118_m1 18.23124 17.36424 16.88424
ACP1-Hs00962877_m1 16.58378 17.52378 18.24978
ADAR-Hs00241666_m1 19.92366 19.17866 19.89866
ADM-Hs00181605_m1 16.34361 15.83161 15.63761
Then you can enter the expression matrix into a limma analysis. You will need to create a design matrix, which will require quite a bit of care as this is a complex dataset.
I would suggest that you also filter genes with very low expression, for example you might do this:
> keep <- rowMeans(y) > 7
> y <- y[keep,]
Then you do a standard limma analysis, starting with:
> fit <- lmFit(y, design)
and so on.
PS. PCR never produces "gene counts", so I don't know where you might have got that idea from. You certainly can't analyse this data using a RNA-seq package like DESeq or edgeR.