Oligonucleotide arrays are a powerful technology for measuring the expression of thousands of genes simultaneously. Improvements in the sensitivity and precision of the measurements, which often pose a challenge to users, would assist the practical application of the technology. Here, we describe a new analysis algorithm for assessing changes in gene expression using oligonucleotide arrays. Changes in expression are detected in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. We show that the S-score is sensitive and reliable, enabling us to obtain more consistent results than with existing methods. Cluster analysis of S-score data of four brain regions exhibits patterns that are more distinctive because of improved data quality. In our case study of two mouse brain regions, over 200 genes were identified to have detectable changes between the ventral tegmental area and the prefrontal cortex. The genes with the most distinctive changes are found to be related to myelin or neurofilament synthesis, calcium signaling, and transcription factors. Many of these findings are in agreement with previous studies, using other techniques, such as in situ hybridization. Overall, our findings suggest that this new algorithm may have broad applicability for improving the analysis of oligonucleotide array data.
Copyright 2002 Elsevier Science Ltd.