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Bioinformatics Advance Access published online on July 29, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn404
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A fast Bayesian change point analysis for the segmentation of microarray data

Chandra Erdman 1,* and John W. Emerson 1

1Department of Statistics, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511

*To whom correspondence should be addressed. Chandra Erdman, E-mail: chandra.erdman{at}yale.edu


  Abstract

Motivation: The ability to detect regions of genetic alteration is of great importance in cancer research. These alterations can take the form of large chromosomal gains and losses as well as smaller amplifications and deletions. The detection of such regions allows researchers to identify genes involved in cancer progression, and to fully understand differences between cancer and non-cancer tissue. The Bayesian method proposed by Barry and Hartigan (1993) is wellsuited for the analysis of such change point problems. In Erdman and Emerson (2007) we introduce the R package bcp (Bayesian change point), an MCMC implementation of Barry and Hartigan's method. In a simulation study and real data examples, bcp is shown to both accurately detect change points and estimate segment means. Earlier versions of bcp (prior to 2.0) are O(n2) in speed and O(n) in memory (where n is the number of observations), and run in approximately 45 minutes for a sequence of length 10,000. With the high resolution of newer microarrays, the number of computations in the O(n2) algorithm is prohibitively time-intensive.

Results: We present a new implementation of the Bayesian change point method that is O(n) in both speed and memory; bcp 2.1 runs in approximately 45 seconds on a single processor with a sequence of length 10,000 – a tremendous speed gain. Further speed improvements are possible using parallel computing, supported in bcp via NetWorkSpaces (REvolution Computing). In simulated and real microarray data from the literature, bcp is shown to quickly and accurately detect abberations of varying width and magnitude.

Availability: The R package bcp is available on CRAN (R Development Core Team, 2008). The O(n) version is available in version 2.0 or higher, with support for NetWorkSpaces in versions 2.1 and higher.

Contact: chandra.erdman{at}yale.edu

Associate Editor: Prof. Martin Bishop

Received on June 16, 2008; revised on July 27, 2008; accepted on July 28, 2007

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