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September 2009 Doubly stochastic continuous-time hidden Markov approach for analyzing genome tiling arrays
W. Evan Johnson, X. Shirley Liu, Jun S. Liu
Ann. Appl. Stat. 3(3): 1183-1203 (September 2009). DOI: 10.1214/09-AOAS248

Abstract

Microarrays have been developed that tile the entire nonrepetitive genomes of many different organisms, allowing for the unbiased mapping of active transcription regions or protein binding sites across the entire genome. These tiling array experiments produce massive correlated data sets that have many experimental artifacts, presenting many challenges to researchers that require innovative analysis methods and efficient computational algorithms. This paper presents a doubly stochastic latent variable analysis method for transcript discovery and protein binding region localization using tiling array data. This model is unique in that it considers actual genomic distance between probes. Additionally, the model is designed to be robust to cross-hybridized and nonresponsive probes, which can often lead to false-positive results in microarray experiments. We apply our model to a transcript finding data set to illustrate the consistency of our method. Additionally, we apply our method to a spike-in experiment that can be used as a benchmark data set for researchers interested in developing and comparing future tiling array methods. The results indicate that our method is very powerful, accurate and can be used on a single sample and without control experiments, thus defraying some of the overhead cost of conducting experiments on tiling arrays.

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W. Evan Johnson. X. Shirley Liu. Jun S. Liu. "Doubly stochastic continuous-time hidden Markov approach for analyzing genome tiling arrays." Ann. Appl. Stat. 3 (3) 1183 - 1203, September 2009. https://doi.org/10.1214/09-AOAS248

Information

Published: September 2009
First available in Project Euclid: 5 October 2009

zbMATH: 1196.62141
MathSciNet: MR2750392
Digital Object Identifier: 10.1214/09-AOAS248

Keywords: Bayesian hierarchical model , continuous-space Markov chain , Expectation Conditional Maximization , forward–backward algorithm , Hidden Markov model , Markov chain Monte Carlo , Tiling microarray

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.3 • No. 3 • September 2009
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