Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change-Point Approach

DOI10.1111/J.1541-0420.2009.01328.XzbMath1203.62202OpenAlexW2146918676WikidataQ42097541 ScholiaQ42097541MaRDI QIDQ3064251

Mark N. Kvale, John S. Witte, Yu Chuan Tai

Publication date: 21 December 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3766751

zbMATH Keywords

segmentationcopy number variantBayesian multiple change pointssignal attenuationSNP microarrays

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40) Estimation in survival analysis and censored data (62N02)

Uses Software

• BioHMM
• CGHclassify
• PennCNV

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Hidden Markov models approach to the analysis of array CGH data
• Detecting simultaneous changepoints in multiple sequences
• A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations
• Stochastic segmentation models for array-based comparative genomic hybridization data analysis
• Bayesian Hidden Markov Modeling of Array CGH Data
• Inferring Spatial Phylogenetic Variation Along Nucleotide Sequences
• Circular binary segmentation for the analysis of array-based DNA copy number data
• A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data

This page was built for publication: Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change-Point Approach