Raw cel files were imported into the statistical programming environment R (R: A language and environment for statistical computing Ver 2.2.0; R Foundation for Statistical Computing, 2005) for further analysis with tools available from the Bioconductor Project (Gentleman et al., 2004). Expression data from the 48 arrays of each region were normalized within-region and converted to log(2) using the Robust Multi-chip Average (RMA) method (Irizarry et al., 2003) implemented in the Bioconductor package RMA. As a standardization step to facilitate later comparisons with other experiments, expression levels were scaled such that the mean expression of all arrays was log2(1000). As we were primarily concerned with identifying genes that could be subjected to further bioinformatic analysis, all probe sets currently annotated by Affymetrix as “expressed sequence tags” or whose gene names contain the words “riken”, “predicted”, or “similar to” were filtered out. We next filtered out probe sets that were not detectable above background in our samples,; this has been shown to reduce noise in microarray experiments (McClintick and Edenberg, 2003) Probe sets that did not have at least 25% of
