Bioinformatics Advance Access originally published online on February 24, 2006
Bioinformatics 2006 22(9):1122-1129; doi:10.1093/bioinformatics/btl060
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A systematic comparison and evaluation of biclustering methods for gene expression data
1
1 Computer Engineering and Networks Laboratory ETH Zurich, 8092 Zurich, Switzerland
2 Institute for Plant Sciences and Functional Genomics Center Zurich ETH Zurich, 8092 Zurich, Switzerland
3 Colab ETH Zurich, 8092 Zurich, Switzerland
4 Seminar for Statistics ETH Zurich, 8092 Zurich, Switzerland
*To whom correspondence should be addressed.
Motivation: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available.
Results: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings.
Availability: The datasets used, the outcomes of the biclustering algorithms and the Bimax implementation for the reference model are available at http://www.tik.ee.ethz.ch/sop/bimax
Contact: bleuler{at}tik.ee.ethz.ch
Supplementary information: Supplementary data are available at http://www.tik.ee.ethz.ch/sop/bimax
Received on July 27, 2005; revised on January 4, 2006; accepted on February 15, 2006
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