I am looking for a R/BioC tutorial / workflow to analyze hgu133plus2 (Affymetrix platform, case-control data) .cel files and find differentially expressed genes in cases when compared to controls.

I have seen multiple tutorials over the web[for example 1, but some of the key aspects on QC, experimental design, issues on dealing with case-control data etc. seems to be not explained well or rather incomplete.

Please point to a resource or share your thoughts on a R/BioC tutorial that discuss about QC, Normalization, Experimental Design, Differential expression etc. in detail.

Thanks !

I found the documentation which comes with the simpleaffy package to be clear and helpful. It covers normalization, QC, experimental design (to some degree) and various ways to filter data. A second document covers QC in more detail.

• simpleaffy primer (PDF)
• simpleaffy QC (PDF)

I found this presentation to be very helpful since it guides you step by step, hope you find it useful too.

This one talks about processing Affymetrix data.

http://www.nd.edu/~steve/Rcourse/Lecture10v1.pdf