Hart, Christopher E. and Sharenbroich, Lucas and Bornstein, Benjamin J. and Trout, Diane and King, Brandon and Mjolsness, Eric and Wold, Barbara J. (2005) A mathematical and computational framework for quantitative comparison and integration of large-scale gene expression data. Nucleic Acids Research, 33 (8). pp. 2580-2594. ISSN 0305-1048. http://resolver.caltech.edu/CaltechAUTHORS:HARnar05
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Analysis of large-scale gene expression studies usually begins with gene clustering. A ubiquitous problem is that different algorithms applied to the same data inevitably give different results, and the differences are often substantial, involving a quarter or more of the genes analyzed. This raises a series of important but nettlesome questions: How are different clustering results related to each other and to the underlying data structure? Is one clustering objectively superior to another? Which differences, if any, are likely candidates to be biologically important? A systematic and quantitative way to address these questions is needed, together with an effective way to integrate and leverage expression results with other kinds of large-scale data and annotations. We developed a mathematical and computational framework to help quantify, compare, visualize and interactively mine clusterings. We show that by coupling confusion matrices with appropriate metrics (linear assignment and normalized mutual information scores), one can quantify and map differences between clusterings. A version of receiver operator characteristic analysis proved effective for quantifying and visualizing cluster quality and overlap. These methods, plus a flexible library of clustering algorithms, can be called from a new expandable set of software tools called CompClust 1.0 (http://woldlab.caltech.edu/compClust/). CompClust also makes it possible to relate expression clustering patterns to DNA sequence motif occurrences, protein–DNA interaction measurements and various kinds of functional annotations. Test analyses used yeast cell cycle data and revealed data structure not obvious under all algorithms. These results were then integrated with transcription motif and global protein–DNA interaction data to identify G1 regulatory modules.
|Additional Information:||© The Author 2005. Published by Oxford University Press. All rights reserved. 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 firstname.lastname@example.org. Received February 15, 2005; Revised March 25, 2005; Accepted April 6, 2005. Funding of this work and its open access publication was from the NCI, the NIH, NASA, the Department of Energy, and the LK Whittier Foundation. The authors thank Prof. Joe Hacia, Drs Jose Luis Riechmann and Brian Williams for helpful comments on the manuscript. Conflict of interest statement. None declared.|
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|Deposited On:||17 Jun 2005|
|Last Modified:||26 Dec 2012 08:40|
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