Hi, I am trying to determine the differentially expressed genes between two groups, the raw data in form of CEL files generated by the Clariom S array, The two groups of comparison, Case=6 arrays, Control= 7 arrays. (total 13 arrays).
My question is
- How I can filter the genes with low variance and QC probes.
- considering my experiment design, how to design a matrix?
Code should be placed in three backticks as shown below
library(oligo) library(affycoretools) library(limma) library(clariomshumantranscriptcluster.db) library(pd.clariom.s.human) list.celfiles() rawdata <- read.celfiles(list.celfiles()) probeset.eset=rma(rawdata, background=TRUE, normalize=TRUE, subset=NULL) probeset.eset <- annotateEset(probeset.eset,annotation(probeset.eset)) # I Stopped here cause I don't know how to formulate the design matrix
include your problematic code here with any corresponding output
please also include the results of running the following in an R session