Maximum significance clustering of oligonucleotide microarrays

Abstract

Affymetrix high-density oligonucleotide microarrays measure expression of DNA transcripts using probe sets, i.e. multiple probes per transcript. Usually, these multiple measurements are transformed into a single probeset expression level before data analysis proceeds; any information on variability is lost. In this work we demonstrate how individual probe measurements can be used in a statistic for differential expression. Furthermore, we show how this statistic can serve as a clustering criterion. A novel clustering algorithm using this maximum significance criterion is demonstrated to be more efficient with the measured data than competing techniques for dealing with repeated measurements, especially when the sample size is small.