title: Maximum significance clustering of oligonucleotide microarrays.
abstract: BACKGROUND
  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.
authors.affiliation: Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands. D.deRidder@ewi.tudelft.nl
authors.forename: Dick
authors.initials: D
authors.lastname: de Ridder
authors.forename: Frank J T
authors.initials: FJ
authors.lastname: Staal
authors.forename: Jacques J M
authors.initials: JJ
authors.lastname: van Dongen
authors.forename: Marcel J T
authors.initials: MJ
authors.lastname: Reinders
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software