Brazilian Journal of Probability and Statistics

A semiparametric Bayesian model for comparing DNA copy numbers

Luis Nieto-Barajas, Yuan Ji, and Veerabhadran Baladandayuthapani

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Abstract

We propose a two-step method for the analysis of copy number data. We first define the partitions of genome aberrations and conditional on the partitions we introduce a semiparametric Bayesian model for the analysis of multiple samples from patients with different subtypes of a disease. While the biological interest is to identify regions of differential copy numbers across disease subtypes, our model also includes sample-specific random effects that account for copy number alterations between different samples in the same disease subtype. We model the subtype and sample-specific effects using a random effects mixture model. The subtype’s main effects are characterized by a mixture distribution whose components are assigned Dirichlet process priors. The performance of the proposed model is examined using simulated data as well as a breast cancer genomic data set.

Article information

Source
Braz. J. Probab. Stat. Volume 30, Number 3 (2016), 345-365.

Dates
Received: January 2014
Accepted: February 2015
First available in Project Euclid: 29 July 2016

Permanent link to this document
http://projecteuclid.org/euclid.bjps/1469807216

Digital Object Identifier
doi:10.1214/15-BJPS283

Mathematical Reviews number (MathSciNet)
MR3531688

Zentralblatt MATH identifier
06633263

Keywords
Bayesian nonparametrics bivariate spike and slab prior circular binary segmentation comparative genomic hybridization Dirichlet process mixture model random effects

Citation

Nieto-Barajas, Luis; Ji, Yuan; Baladandayuthapani, Veerabhadran. A semiparametric Bayesian model for comparing DNA copy numbers. Braz. J. Probab. Stat. 30 (2016), no. 3, 345--365. doi:10.1214/15-BJPS283. http://projecteuclid.org/euclid.bjps/1469807216.


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