Dear List,
  It was a lot of e-mails from me recently for which I apologise and hope that my questions were of interest to you. I would be very much interested in your views on the following.
  It is accepted that microarray measurements contain technical and biological variations and that for single channel (e.g. Affy) normalised log2 data biological variation is ~2-4 times higher than technical. Now, let's say we have N arrays representing N separate individuals (or animals) of a certain type (wild type or under the same treatment condition). Thus, for a given gene, variations in its expression level represent variations across individuals. Suppose that for each gene we calculate its mean expression level across all arrays (across population) and then in each array classify genes as expressed above/below their average across population levels. Now for each gene we have a binary profile (above/below) across population of N individuals. We can further perform say simple correlation analysis and find those genes which concurrently expressed above/below their population means within the given set of N arrays. This may provide some information on their
 connectivity, etc.
  What would you say about this? Is it a valid suggestion? Or the technical component of variation will not allow doing this? If it were relevant, what would be the sample size sufficient for finding accurate correlations? Do you know of any Bioconductor tools that may be applied here for sample size calculations?
  Looking forward to hearing your opinions,
Lev.
       
---------------------------------

	[[alternative HTML version deleted]]

