ArticleJournal of computational biology : a journal of computational molecular cell biology2010
A double-layered mixture model for the joint analysis of DNA copy number and gene expression data.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- CiberAMP: AnBiology · 2022Article
- Bayesian variable selection with graphical structure learning: Applications in integrative genomics.PloS one · 2018Article
- A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data.Bioinformatics (Oxford, England) · 2016Article
- Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes.BMC genomics · 2015Article
- Detection of candidate tumor driver genes using a fully integrated Bayesian approach.Statistics in medicine · 2014Article
- A HIERARCHICAL BAYESIAN MODEL FOR INFERENCE OF COPY NUMBER VARIANTS AND THEIR ASSOCIATION TO GENE EXPRESSION.The annals of applied statistics · 2014Article
- Characterizing Cancer-Specific Networks by Integrating TCGA Data.Cancer informatics · 2014Article
- Article
- A Bayesian Graphical Model for Integrative Analysis of TCGA Data.IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics · 2012Article
- Genome-wide identification of significant aberrations in cancer genome.BMC genomics · 2012Article
- Matching of array CGH and gene expression microarray features for the purpose of integrative genomic analyses.BMC bioinformatics · 2012Article
- Comparative analysis of algorithms for integration of copy number and expression data.Nature methods · 2012Article
- TAGCNA: a method to identify significant consensus events of copy number alterations in cancer.PloS one · 2012Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Copy number aberration is a common form of genomic instability in cancer. Gene expression is closely tied to cytogenetic events by the central dogma of molecular biology, and serves as a mediator of copy number changes in disease phenotypes. Accordingly, it is of interest to develop proper statistical methods for jointly analyzing copy number and gene expression data. This work describes a novel Bayesian inferential approach for a double-layered mixture model (DLMM) which directly models the stochastic nature of copy number data and identifies abnormally expressed genes due to aberrant copy number. Simulation studies were conducted to illustrate the robustness of DLMM under various settings of copy number aberration frequency, confounding effects, and signal-to-noise ratio in gene expression data. Analysis of a real breast cancer data shows that DLMM is able to identify expression changes specifically attributable to copy number aberration in tumors and that a sample-specific index built based on the selected genes is correlated with relevant clinical information.
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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.