A semiparametric Bayesian model for comparing DNA copy numbers.

Luis Nieto-Barajas, Yuan Ji, Veerabhadran Baladandayuthapani

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Article in Brazilian journal of probability and statistics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Luis Nieto-BarajasDepartment of Statistics, ITAM, Rio Hondo 1, Progreso Tizapan, 01080 Mexico, D.F. Mexico.

Yuan JiBiomedical Informatics, NorthShore University HealthSystem and University of Chicago, 1001 University Place, Evanston, Illinois 60201, USA.

Veerabhadran BaladandayuthapaniDepartment of Biostatistics, University of Texas MDACC, 1515 Holcombe Boulevard, Houston, Texas 77030, USA.

Funding

Bayesian models for cancer prognosis by integrating diverse types of dataR01CA132897 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JI, YUAN, MUELLER, PETER · 2008 to 2019

$2.1M

Integrative methods for high-dimensional genomics dataR01CA160736 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BALADANDAYUTHAPANI, VEERABHADRAN · 2011 to 2014

$1.4M

NCI NIH HHS R01 CA132897NCI NIH HHS R01 CA160736

6 · The paper itself

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.

Indexed as

Bayesian nonparametricsbivariate spike and slab priorcircular binary segmentationcomparative genomic hybridizationDirichlet process mixture modelrandom effects

Identifiers

PMID37799327

DOI10.1214/15-bjps283

PMCPMC10552905

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.