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Bioinformatics Advance Access originally published online on August 12, 2008
Bioinformatics 2008 24(19):2193-2199; doi:10.1093/bioinformatics/btn372
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Cancer outlier detection based on likelihood ratio test

Jianhua Hu

Department of Biostatistics, Division of Quantitative Science, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA


  Abstract

Motivation: Microarray experiments can be used to help study the role of chromosomal translocation in cancer development through cancer outlier detection. The aim is to identify genes that are up- or down-regulated in a subset of cancer samples in comparison to normal samples.

Results: We propose a likelihood-based approach which targets detecting the change of point in mean expression intensity in the group of cancer samples. A desirable property of the proposed approach is the availability of theoretical significance-level results. Simulation studies showed that the performance of the proposed approach is appealing in terms of both detection power and false discovery rate. And the real data example also favored the likelihood-based approach in terms of the biological relevance of the results.

Availability: R code to implement the proposed method in the statistical package R is available at: http://odin.mdacc.tmc.edu/~jhhu/cod-analysis/.

Contact: jhu{at}mdanderson.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

Received on October 31, 2007; revised on June 19, 2008; accepted on July 15, 2008

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