Skip Navigation


Bioinformatics Advance Access originally published online on September 1, 2005
Bioinformatics 2005 21(21):3993-3999; doi:10.1093/bioinformatics/bti644
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrowOA All Versions of this Article:
21/21/3993    most recent
bti644v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (2)
Google Scholar
Right arrow Articles by Torrente, A.
Right arrow Articles by Brazma, A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Torrente, A.
Right arrow Articles by Brazma, A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

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

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

1EMBL Outstation—Hinxton, European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
2Universidad Carlos III de Madrid Leganés, CP 28911, Madrid, Spain

*To whom correspondence should be addressed.

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 non-trivial 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 versus flat, or flat versus hierarchical. When comparing a flat clustering 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

Contact: aurora{at}ebi.ac.uk

Supplementary information: http://www.ebi.ac.uk/microarray/General/Publications/publications.html

Received on June 16, 2005; revised on August 1, 2005; accepted on August 23, 2005

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.