Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Bayesian mixture model based clustering of replicated microarray data.

M Medvedovic1, K Y Yeung, R E Bumgarner

  • 1Department of Environmental Health, Center for Genome Information, University of Cincinnati Medical Center, 3223 Eden Avenue ML 56, Cincinnati, OH 45267-0056, USA. Mario.Medvedovic@uc.edu

Bioinformatics (Oxford, England)
|February 12, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Acute poisoning by dexmedetomidine-containing chewing gum in a child.

Pathology·2021
Same author

A prospective interventional study to examine the effect of a silver alloy and hydrogel-coated catheter on the incidence of catheter-associated urinary tract infection.

Hong Kong medical journal = Xianggang yi xue za zhi·2017
Same author

Evolutionary Divergence of Aggregatibacter actinomycetemcomitans.

Journal of dental research·2015
Same author

The regulation of mitochondrial DNA copy number in glioblastoma cells.

Cell death and differentiation·2013
Same author

Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data.

Bioinformatics (Oxford, England)·2012
Same author

Progression of BRAF-induced thyroid cancer is associated with epithelial-mesenchymal transition requiring concomitant MAP kinase and TGFβ signaling.

Oncogene·2011
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Bayesian mixture models enhance gene expression clustering with experimental replicates. The infinite mixture model with elliptical variance structure offers superior precision in identifying co-expressed genes.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cluster analysis of gene expression data aids in understanding biological processes.
  • Experimental replicates improve clustering precision by reducing measurement variability.
  • Bayesian mixture models efficiently utilize information by modeling between-replicates variability.

Purpose of the Study:

  • To develop and evaluate Bayesian mixture-based clustering procedures for gene expression data with experimental replicates.
  • To assess the impact of modeling between-replicates variability on clustering accuracy.
  • To introduce a modified Gibbs sampler for improved convergence and an infinite mixture model for robust clustering.

Main Methods:

  • Development of finite and infinite Bayesian mixture models with varying between-replicates variance structures.

Related Experiment Videos

  • Estimation of clusterings using a Gibbs sampler.
  • Analysis of synthetic and real-world gene expression datasets.
  • Implementation of a 'reverse annealing' heuristic for Gibbs sampler convergence.

    • Main Results:

    • Two experimental replicates can significantly improve precision, especially with high between-replicates variability.
    • Accurate modeling of intra-gene variability is crucial for identifying co-expressed genes.
    • The infinite Bayesian mixture model with an 'elliptical' variance structure outperformed other tested methods.
    • The modified Gibbs sampler effectively addressed convergence issues.
    • The infinite mixture model identified data structure without prior knowledge of the number of clusters.

    Conclusions:

    • Bayesian infinite mixture models provide a robust framework for gene expression data clustering with replicates.
    • Precise modeling of variability is key to accurate co-expression analysis.
    • The developed methods and software (GIMM) are valuable tools for bioinformatics research.