Nucleic Acids Research, 2003, Vol. 31, No. 19 5582-5589
© 2003 Oxford University Press
EXCAVATOR: a computer program for efficiently mining gene expression data
1 Protein Informatics Group, Life Sciences Division and 2 Computer Sciences and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6480, USA
*To whom correspondence should be addressed at Computer Science Department, 201 Engineering Building West, University of Missouri-Columbia, Columbia, MO 65211-2060, USA. Tel: +1 573 882 7064; Fax: +1 573 882 8318; Email: dong{at}cecs.missouri.edu
Present addresses:
Victor Olman and Ying Xu, Department of Biochemistry and Molecular Biology, B122 Life Sciences Building, University of Georgia, Athens, GA 30602, USA
Massive amounts of gene expression data are generated using microarrays for functional studies of genes and gene expression data clustering is a useful tool for studying the functional relationship among genes in a biological process. We have developed a computer package EXCAVATOR for clustering gene expression profiles based on our new framework for representing gene expression data as a minimum spanning tree. EXCAVATOR uses a number of rigorous and efficient clustering algorithms. This program has a number of unique features, including capabilities for: (i) data- constrained clustering; (ii) identification of genes with similar expression profiles to pre-specified seed genes; (iii) cluster identification from a noisy background; (iv) computational comparison between different clustering results of the same data set. EXCAVATOR can be run from a Unix/Linux/DOS shell, from a Java interface or from a Web server. The clustering results can be visualized as colored figures and 2-dimensional plots. Moreover, EXCAVATOR provides a wide range of options for data formats, distance measures, objective functions, clustering algorithms, methods to choose number of clusters, etc. The effectiveness of EXCAVATOR has been demonstrated on several experimental data sets. Its performance compares favorably against the popular K-means clustering method in terms of clustering quality and computing time.