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

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

Gordon K Smyth1

  • 1Walter and Eliza Hall Institute. smyth@wehi.edu.au

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

The roles of the acetyltransferase domains of the chromatin regulators KAT6A and KAT6B in vivo.

Development (Cambridge, England)·2026
Same author

KAT6A is essential for developmental control gene expression in neural stem and progenitor cells.

PLoS genetics·2026
Same author

Acetyl-carnitine improves hyperactivity and learning deficits in <i>KAT6A</i> haploinsufficient mice.

Life science alliance·2026
Same author

Interleukin 4 selectively expands functional type 1 conventional dendritic cells from bone marrow progenitors.

Cell reports·2025
Same author

Dividing out quantification uncertainty enables assessment of differential transcript usage with limma and edgeR.

Nucleic acids research·2025
Same author

MORC2 is a phosphorylation-dependent DNA compaction machine.

Nature communications·2025
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
Same journal

Corrigendum to: Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Statistical applications in genetics and molecular biology·2025
See all related articles

This study introduces a robust hierarchical model for identifying differentially expressed genes in microarray experiments. The new approach, using moderated t-statistics, offers stable inference for small sample sizes and complex experimental designs.

Area of Science:

  • Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Identifying differentially expressed genes is crucial for understanding biological responses in microarray experiments.
  • Existing methods, like Lonnstedt and Speed's (2002) posterior odds, are limited in applicability to general experimental designs.
  • Hierarchical parametric models offer a framework for robust statistical inference in gene expression analysis.

Purpose of the Study:

  • To extend the Lonnstedt and Speed (2002) hierarchical model for broad application to diverse microarray experiments.
  • To develop a practical and robust statistical approach for identifying differentially expressed genes across various experimental setups.
  • To adapt the model for both single-channel and two-color microarray data with arbitrary sample sizes and treatments.

Related Experiment Videos

Main Methods:

  • The study reformulates the hierarchical model within the framework of general linear models.
  • Consistent, closed-form estimators for model hyperparameters are derived, ensuring robust performance with small numbers of arrays and incomplete data.
  • The posterior odds statistic is transformed into a moderated t-statistic, utilizing posterior residual standard deviations for improved stability.

Main Results:

  • The empirical Bayes approach effectively shrinks estimated sample variances towards a pooled estimate, enhancing inference stability for small sample sizes.
  • Moderated t-statistics reduce the number of required hyperparameters compared to posterior odds, eliminating the need for non-null prior knowledge on fold changes.
  • The moderated t-statistic follows a t-distribution with augmented degrees of freedom, and the approach extends to moderated F-statistics for composite null hypotheses.

Conclusions:

  • The developed moderated t-statistic approach provides a stable and practical method for differential gene expression analysis in microarray studies.
  • This method demonstrates robust performance, even with limited data, and is applicable to complex experimental designs.
  • The findings are validated through simulation studies and application to publicly available gene expression datasets.