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

Hierarchical inverse Gaussian models and multiple testing: application to gene expression data.

Aurelie Labbe1, Mary Thompson

  • 1Universite Laval. alabbe@mat.ulaval.ca

Statistical Applications in Genetics and Molecular Biology
|May 2, 2006
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

Rasch analysis of the professional fulfillment index burnout scale in early-career physical therapists.

Physiotherapy theory and practice·2026
Same author

Diet-induced metabolic and faecal microbiome responses in pet dogs fed a minimally processed versus extruded kibble diet.

Frontiers in veterinary science·2026
Same author

A BAYESIAN GROWTH MIXTURE MODEL FOR COMPLEX SURVEY DATA: CLUSTERING POSTDISASTER PTSD TRAJECTORIES.

The annals of applied statistics·2026
Same author

Association Between Self-Reported Dietary Intake Questionnaires and Objective Measures in an Inpatient Cross-Sectional Study.

Nutrients·2026
Same author

A spatially structured empirical Bayes framework for the evaluation of network-wide safety countermeasures.

Accident; analysis and prevention·2026
Same author

Cellular and molecular profiling of collagenous gastritis implicates pathogenic CD4<sup>+</sup> T cells.

iScience·2026
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

This study introduces Bayesian models using the inverse Gaussian distribution for analyzing gene expression data in microarrays. These models offer robust alternatives to traditional log-normal and gamma distributions, improving hypothesis testing accuracy.

Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Differential gene expression analysis in microarrays is crucial.
  • Existing methods often rely on restrictive distributional assumptions (log-normal, gamma).
  • These assumptions can be violated, impacting the reliability of results.

Purpose of the Study:

  • To propose and evaluate Bayesian models with inverse Gaussian distribution for gene expression data.
  • To assess the performance of these models regarding tail fitting and robustness.
  • To develop a multiple testing procedure suitable for these models.

Main Methods:

  • Bayesian hierarchical models utilizing the inverse Gaussian distribution.
  • Comparison with traditional Bayesian gamma and log-normal models.

Related Experiment Videos

  • Development of a multiple testing procedure based on posterior probabilities.

    • Main Results:

    • Inverse Gaussian models demonstrate competitive performance against traditional models in specific scenarios.
    • The proposed models show good fit to the tails of observed distributions.
    • The multiple testing procedure is well-approximated even with small sample sizes.

    Conclusions:

    • Bayesian inverse Gaussian models offer a robust alternative for differential gene expression analysis.
    • These models improve upon existing methods by addressing distributional assumption violations.
    • The developed multiple testing procedure is effective for microarray data analysis.