Maximum significance clustering of oligonucleotide microarrays

D de Ridder, FJT Staal, JJM van Dongen, MJT Reinders

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)

Abstract

Motivation: Affymetrix high-density oligonucleotide microarrays measure the expression of DNA transcripts using probesets, 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 paper 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 criterion for clustering microarrays. Results: 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.
Original languageUndefined/Unknown
Pages (from-to)326-331
Number of pages6
JournalBioinformatics
Volume22
Issue number3
Publication statusPublished - 2006

Keywords

  • academic journal papers
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