An algorithm for clustering cDNA fingerprints

Erez Hartuv, Armin O. Schmitt, Jörg Lange, Sebastian Meier-Ewert, Hans Lehrach, Ron Shamir*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Clustering large data sets is a central challenge in gene expression analysis. The hybridization of synthetic oligonucleotides to arrayed cDNAs yields a fingerprint for each cDNA clone. Cluster analysis of these fingerprints can identify clones corresponding to the same gene. We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. Unlike other methods, it does not assume that the clusters are hierarchically structured and does not require prior knowledge on the number of clusters. In tests with simulated libraries the algorithm outperformed the Greedy method and demonstrated high speed and robustness to high error rate. Good solution quality was also obtained in a blind test on real cDNA fingerprints. (C) 2000 Academic Press.

Original languageEnglish
Pages (from-to)249-256
Number of pages8
JournalGenomics
Volume66
Issue number3
DOIs
StatePublished - 15 Jun 2000

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