I used oligo and limma packages to process and analyze my Affymetrix Mouse Gene 2.0 ST Arrays.
The ExpressionSet object was created by:
eSet = rma(raw, target="core")
No additional filtering was applied.
eSet: ExpressionSet (storageMode: lockedEnvironment) assayData: 41345 features, 19 samples element names: exprs protocolData etc......
After RMA, the boxplot and array clustering demonstrated good quality. No outlier.
My design matrix:
design <- model.matrix(~0+Treatment) #4 level - treatment with subsequent contrast matrix.
Question 1 :
In the topTable, the top significant results were annotated as "reporter probe", for instance "17549014" (category: reporter).
I am wondering what could be the explanation. Could it be the consequence of poor array quality? If it is, is there any solution for additional correction besides RMA? What about array weights?
I understand I could filter my probes and keep "main" ones but still I would like to know the meaning of my result with respect to array quality without probe filtering.
In order to detect DE I could choose between 2 approaches. One approach is to set all the treatments (3) against the control group and overlap the results (venn diagram). The other one is simple to set all the possible contrasts. I am wondering if there is any difference between the 2 approaches. If it is , under what criteria should I prefer one to the other?
Thank you for your help in advance,