Hi, I am new in this field (so basic questions will follow). I want to analyze methylation data published together with my own data. I have downloaded beta values from a 450K array (there was not IDAT data for it). They just uploaded the normalized beta values. Myself I have data from EPIC array which I normalized, etc. and I ended up with beta values normalized. I was able to merge and intersect probes between these two types of arrays. I generated a data.frame containing the CpG identificator and next to it the 18 columns (10 are normal + 8 pathological). I don't know what is the best way to analyze this matrix. I tried limma but got stuck making a contrast matrix, advices? bellow the code. But more importantly it is the best way to analyze my data? or should I take another approach. I'm worried that normalization is not the correct one or that interecting just this data.frame by CpG identificator is not the right one to do it. Thanks a lot!
normal_cols <- colnames(your_data)[2:11] patho_cols <- colnames(your_data)[12:19]
design <- model.matrix(~ 0 + factor(rep(c("Normal", "Patho"), each = length(normal_cols))), data = your_data)
colnames(design) <- make.names(colnames(design))
contrast.matrix <- makeContrasts(Patho - Normal, levels = design) +++Error in eval(ej, envir = levelsenv) : object 'Patho' not found+++