ArticleBioinformatics (Oxford, England)2006
Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset.
Article in Bioinformatics (Oxford, England), 2006. 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.
- Axl regulated survival/proliferation network and its therapeutic intervention in mouse models of glomerulonephritis.Arthritis research & therapy · 2022Article
- ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis.Computational toxicology (Amsterdam, Netherlands) · 2022Article
- Differences in peripheral immune system gene expression in frontotemporal degeneration.Medicine · 2022Article
- Bayesian correlated clustering to integrate multiple datasets.Bioinformatics (Oxford, England) · 2012Article
- Patient-specific data fusion defines prognostic cancer subtypes.PLoS computational biology · 2011Article
- WebGimm: An integrated web-based platform for cluster analysis, functional analysis, and interactive visualization of results.Source code for biology and medicine · 2011Article
- Discovering transcriptional modules by Bayesian data integration.Bioinformatics (Oxford, England) · 2010Article
- A semi-parametric Bayesian model for unsupervised differential co-expression analysis.BMC bioinformatics · 2010Article
- Genomics Portals: integrative web-platform for mining genomics data.BMC genomics · 2010Article
- CLEAN: CLustering Enrichment ANalysis.BMC bioinformatics · 2009Article
- Influence of fatty acid diets on gene expression in rat mammary epithelial cells.Physiological genomics · 2009Article
- Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data.BMC bioinformatics · 2007Article
- Application of gene shaving and mixture models to cluster microarray gene expression data.Cancer informatics · 2007Article
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Authors and funding
6 authors.
Funding
Abstract
motivationIdentifying groups of co-regulated genes by monitoring their expression over various experimental conditions is complicated by the fact that such co-regulation is condition-specific. Ignoring the context-specific nature of co-regulation significantly reduces the ability of clustering procedures to detect co-expressed genes due to additional 'noise' introduced by non-informative measurements.
resultsWe have developed a novel Bayesian hierarchical model and corresponding computational algorithms for clustering gene expression profiles across diverse experimental conditions and studies that accounts for context-specificity of gene expression patterns. The model is based on the Bayesian infinite mixtures framework and does not require a priori specification of the number of clusters. We demonstrate that explicit modeling of context-specificity results in increased accuracy of the cluster analysis by examining the specificity and sensitivity of clusters in microarray data. We also demonstrate that probabilities of co-expression derived from the posterior distribution of clusterings are valid estimates of statistical significance of created clusters. AVAILABILITY: The open-source package gimm is available at http://eh3.uc.edu/gimm.
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