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Bioinformatics Advance Access published online on May 24, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti517
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received March 24, 2005
Revised May 24, 2005
Accepted May 24, 2005

Review

Computational cluster validation in post-genomic data analysis

Julia Handl 1*, Joshua Knowles 1, and Douglas B. Kell 1

1 School of Chemistry, University of Manchester, Faraday Building, Sackville Street PO Box 88, Manchester M60 1QD, UK

* To whom correspondence should be addressed.
Julia Handl, E-mail: J.Handl{at}postgrad.manchester.ac.uk


  Abstract

Motivation: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore focused on the transfer of clustering methods introduced in other scientific fields [26, 67, 74], and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data [33, 51]. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge -- whether the clusters actually correspond to real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature [15, 31, 36, 56], but have been given only a fraction of the same attention in bioinformatics [8, 9].

Results: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application for post-genomic data analysis. Synthetic and real biological data sets are used to demonstrate the benefits, and also some of the perils, of analytical cluster validation.

Availability: Enlarged colour plots are provided in the supplementary material. The software used in the experiments is made available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.


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