ArticleJournal of the American Statistical Association2010
Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data.
Article in Journal of the American Statistical Association, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.Cell reports methods · 2025Article
- BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.The annals of applied statistics · 2023Article
- Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer.Biometrics · 2023Article
- BaySyn: Bayesian Evidence Synthesis for Multi-system Multiomic Integration.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Innovative trial design in precision oncology.Seminars in cancer biology · 2022Review
- Exploring the relationship between white matter integrity, cocaine use and GAD polymorphisms using Bayesian Model Averaging.PloS one · 2021Article
- A nonparametric Bayesian basket trial design.Biometrical journal. Biometrische Zeitschrift · 2019Article
- Efficient Bayesian Regularization for Graphical Model Selection.Bayesian analysis · 2019Article
- Bayesian High-Dimensional Regression for Change Point Analysis.Statistics and its interface · 2019Article
- Functional interaction-based nonlinear models with application to multiplatform genomics data.Statistics in medicine · 2018Article
- Bayesian variable selection with graphical structure learning: Applications in integrative genomics.PloS one · 2018Article
- Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.Statistical modelling · 2017Article
- A semiparametric Bayesian model for comparing DNA copy numbers.Brazilian journal of probability and statistics · 2016Article
- Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials.Statistics in biosciences · 2016Article
- Assessing systematic effects of stroke on motorcontrol by using hierarchical function-on-scalar regression.Journal of the Royal Statistical Society. Series C, Applied statistics · 2016Article
- Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence.NeuroImage · 2016Article
- A Two-Sample Test for Equality of Means in High Dimension.Journal of the American Statistical Association · 2015Article
- Generalized species sampling priors with latent Beta reinforcements.Journal of the American Statistical Association · 2014Article
- Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer.Journal of the Royal Statistical Society. Series C, Applied statistics · 2014Article
- A bayesian integrative model for genetical genomics with spatially informed variable selection.Cancer informatics · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that utilizes information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, referred to as BDSAcgh (Bayesian Detection of Shared Aberrations in aCGH), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alteration that correspond to local false discovery rates. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set.
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Registered trials
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.