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Cancer Informatics

Synopsis: An open access, peer reviewed electronic journal that covers the role of computational biology and bioinformatics in cancer treatment.


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Journal: 257368
Most read article: 4283
Editor in chief:
James Willey
ISSN: 1176-9351


 
 
 


The Bimodality Index: A Criterion for Discovering  and Ranking Bimodal Signatures from Cancer Gene Expression Profiling Data

Authors: Jing Wang, Sijin Wen, W. Fraser Symmans, Lajos Pusztai and Kevin R. Coombes
Publication Date: 05 Aug 2009
Cancer Informatics 2009:7 199-216

Jing Wang1, Sijin Wen2, W. Fraser Symmans3, Lajos Pusztai4 and Kevin R. Coombes1

1Department of Bioinformatics and Computational Biology, 2Department of Biostatistics, 3Department of Pathology, 4Department of Breast Medical Oncology, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030-4009.

Abstract

Motivation: Identifying genes with bimodal expression patterns from large-scale expression profiling data is an important analytical task. Model-based clustering is popular for this purpose. That technique commonly uses the Bayesian information criterion (BIC) for model selection. In practice, however, BIC appears to be overly sensitive and may lead to the identification of bimodally expressed genes that are unreliable or not clinically useful. We propose using a novel criterion, the bimodality index, not only to identify but also to rank meaningful and reliable bimodal patterns. The bimodality index can be computed using either a mixture model-based algorithm or Markov chain Monte Carlo techniques.

Results: We carried out simulation studies and applied the method to real data from a cancer gene expression profiling study. Our findings suggest that BIC behaves like a lax cutoff based on the bimodality index, and that the bimodality index provides an objective measure to identify and rank meaningful and reliable bimodal patterns from large-scale gene expression datasets. R code to compute the bimodality index is included in the ClassDiscovery package of the Object-Oriented Microarray and Proteomic Analysis (OOMPA) suite available at the web site http://bioinformatics.mdanderson.org/Software/OOMPA.



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