My apologies if this question's title is vague: I do now know how to label what I am trying to do, which has made my efforts at finding relevant litterature frustrating and unsuccessful.
I am analyzing the results of a two-color microarray hybridization experiment using the limma package. To validate the normalization methods I've been applying to the data, I am generating hierarchical clusters of individual channels of the microarrays to see if control and treatment samples cluster together. They do not; rather the green and red channels for each array cluster together, indicating that the array effect is more important than any other. My attempts at changing the normalization algorithms have proven unfruitful in correcting this problem.
My hypothesis for the moment is that the microarray I am using (which is of custom design) contains two classes of probes: one that is informative vis-à-vis the biological factor of interest, and another which contains nothing but noise due to a probe-design defect. What I'm looking for is some kind of algorithm/program which would take my microarray data and an a priori expected tree of samples and partitions the probes into those would support such a tree and those who would not. Is there such a thing, or at least something similar to it which II could use as a starting point for more research?