ArticleThe annals of applied statistics2014
A HIERARCHICAL BAYESIAN MODEL FOR INFERENCE OF COPY NUMBER VARIANTS AND THEIR ASSOCIATION TO GENE EXPRESSION.
Article in The annals of applied statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
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- Bayesian inference of networks across multiple sample groups and data types.Biostatistics (Oxford, England) · 2020Article
- Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability.Epilepsia open · 2018Article
- Bayesian Variable Selection Methods for Matched Case-Control Studies.The international journal of biostatistics · 2017Article
- A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.PLoS computational biology · 2016Article
- Article
- A HIERARCHICAL BAYESIAN MODEL FOR INFERENCE OF COPY NUMBER VARIANTS AND THEIR ASSOCIATION TO GENE EXPRESSION.The annals of applied statistics · 2014Article
- A bayesian integrative model for genetical genomics with spatially informed variable selection.Cancer informatics · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurements via a hidden Markov model. We employ selection priors that exploit the dependencies across adjacent copy number states and investigate MCMC stochastic search techniques for posterior inference. Our approach results in a unified modeling framework for simultaneously inferring copy number variants (CNV) and identifying their significant associations with mRNA transcripts abundance. We show performance on simulated data and illustrate an application to data from a genomic study on human cancer cell lines.
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Registered trials
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