Hi all,

I downloaded the data for an experiment from GEO. It contains information regarding 70 samples and I have a dataframe (count data) with the sample names as the columns and the gene IDs as the rows. I believe this data is signal intensities that have been log2 transformed and normalized using RMA. In this case, do I need to first tranform them to CPM?

I also have another dataframe that contains metadata and it contains the row names that correspond to the count data' s columns. 

The problem is that the samples contain 3 different types of treatments for 5 different time points. This information is contained under the time point column of the metadata df. I wish to carry out Limma anaysis for one time point at a time, and compare the control to each of the treatments. 

Should I alter this line of code to reflect that I want to look into the differentially expressed genes among each treatment group vs the control at different  timepoints?

# Create design matrix
design <- model.matrix(~ pData(bottomly.eset)$strain)

I'm really new to this and would appreciate any help I can get.

Thanks.