My experiment consists of 2 groups (54 non-diabetic and 9 diabetic). I would like to use limma to find the differentially expressed genes for these microarray data set.
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38642+
I did Background correcting, Normalizing and Calculating Expression, unfortunately, I do not know how to create design matrix to use limfit() function
I am a beginner in R so any help is appreciated.
Could you explain this a little bit @Chirs86 please?
how can I create groups vector ?
the samples not in sequence it arranges like 2 disease and 12 healthy then 3 disease and 25 healthy.
as you see it comes like this
I did like this
strain <-c("ID1","ID2","ID3","ID4","ID5","ID6","ID7","ID8","ID9","ID10","ID11","ID12","ID13","ID14","ID15","ID16","ID17","ID18","ID19","ID20","ID21","ID22","ID23","ID24","ID25","ID26","ID27","ID28","ID29","ID30","ID31","ID32","ID33","ID34","ID35","ID36","ID37","ID38","ID39","ID40","ID41","ID42","ID43","ID44","ID45","ID46","ID47","ID48","ID49","ID50","ID51","ID52","ID53","ID54","ID55","ID56","ID57","ID58","ID59","ID60","ID61","ID62","ID63")
> design <- model.matrix(~factor(strain))
but I do not know how to create colnames(design)
I am a beginner in R and I will be greatful to you
I normally would have an annotation file, which is a data frame. In this there would be column 1 = your ids in the same order as microarray (google for how to make data frames and reorder columns of matrix and dfs) column 2 = disease status and then other columns like your covariates like gender and age. Then your model matrix is simply model.matrix(~factor(des$status)). Where des is the description file.
Thanks for your help I got what I want