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Bioinformatics Advance Access published online on September 1, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti644
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received June 16, 2005
Revised August 1, 2005
Accepted August 23, 2005

Article

A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings

Aurora Torrente 1*, Misha Kapushesky 2, and Alvis Brazma 2

1 EMBL Outstation - Hinxton, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom; Universidad Carlos III de Madrid, Leganés, CP.28911, Madrid, Spain
2 EMBL Outstation - Hinxton, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom

* To whom correspondence should be addressed.
Aurora Torrente, E-mail: aurora{at}ebi.ac.uk


  Abstract

Motivation: Clustering is one of the most widely used methods in unsupervised gene expression data analysis. The use of different clustering algorithms or different parameters often produces rather different results on the same data. Biological interpretation of multiple clustering results requires understanding how different clusters relate to each other. It is particularly nontrivial to compare the results of a hierarchical and a flat, e.g., k-means, clustering.

Results: We present a new method for comparing and visualizing relationships between different clustering results, either flat vs. flat, or flat vs. hierarchical. When comparing a flat to a hierarchical clustering, the algorithm cuts different branches in the hierarchical tree at different levels to optimize the correspondence between the clusters. The optimization function is based on graph layout aesthetics or on mutual information. The clusters are displayed using a bipartite graph where the edges are weighted proportionally to the number of common elements in the respective clusters and the weighted number of crossings is minimized. The performance of the algorithm is tested using simulated and real gene expression data. The algorithm is implemented in the online gene expression data analysis tool Expression Profiler.

Availability: http://www.ebi.ac.uk/expressionprofiler.


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