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

Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset.

X Liu1, S Sivaganesan, K Y Yeung

  • 1Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.

Bioinformatics (Oxford, England)
|May 20, 2006
PubMed
Summary

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

Measurement of the forward-backward charge asymmetry and extraction of sin2thetaWeff in pp-->Z/gamma* + X-->e+e- +X events produced at sqrt[s] = 1.96 TeV.

Physical review letters·2008
Same author

Measurement of the electron charge asymmetry in pp[over ]-->W+X-->enu+X events at sqrt[s]=1.96 TeV.

Physical review letters·2008
Same author

Search for anomalous Wtb couplings in single top quark production.

Physical review letters·2008
Same author

Search for neutral Higgs bosons in multi-b-jet events in pp[over] collisions at sqrt[s]=1.96 TeV.

Physical review letters·2008
Same author

Observation of the doubly strange b baryon Omegab-.

Physical review letters·2008
Same author

Measurement of Bs0 mixing parameters from the flavor-tagged decay Bs0-->J/psiphi.

Physical review letters·2008

This study introduces a new Bayesian model to accurately cluster gene expression, accounting for condition-specific patterns. This approach improves the detection of co-expressed genes by reducing noise from non-informative measurements.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene co-regulation is often condition-specific, complicating analysis.
  • Ignoring context-specific co-regulation introduces noise and reduces clustering accuracy.
  • Accurate identification of co-regulated genes is crucial for understanding biological pathways.

Purpose of the Study:

  • To develop a novel Bayesian hierarchical model for clustering gene expression profiles.
  • To account for the context-specificity of gene expression patterns in clustering.
  • To improve the accuracy and reliability of co-expressed gene detection.

Main Methods:

  • Developed a Bayesian infinite mixtures model for gene expression clustering.
  • Created corresponding computational algorithms to implement the model.

Related Experiment Videos

  • Utilized microarray data to evaluate the model's performance.

    • Main Results:

    • The novel model accurately clusters gene expression profiles across diverse conditions.
    • Explicitly modeling context-specificity increases cluster analysis accuracy (specificity and sensitivity).
    • Posterior probabilities of co-expression are valid estimates of statistical significance.

    Conclusions:

    • The developed Bayesian model effectively addresses context-specificity in gene co-regulation.
    • This approach enhances the ability to identify biologically relevant gene clusters.
    • The open-source package 'gimm' is available for broader application.