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

Using ANOVA for gene selection from microarray studies of the nervous system.

Paul Pavlidis1

  • 1Department of Biomedical Informatics and Columbia Genome Center, Columbia University, Rm 121J, 1150 St. Nicholas Avenue, New York, NY 10032, USA. pp175@columbia.edu

Methods (San Diego, Calif.)
|November 5, 2003
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

Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue.

bioRxiv : the preprint server for biology·2026
Same author

Persistent hindrances to data re-use in single-cell genomics.

Scientific data·2026
Same author

Application of large language models to the annotation of cell lines and mouse strains in genomics data.

bioRxiv : the preprint server for biology·2026
Same author

Using semantic search to find publicly available gene-expression datasets.

Bioinformatics (Oxford, England)·2026
Same author

Cataloging the potential functional diversity of Cacna1e splice variants using long-read sequencing.

BMC genomics·2025
Same author

Global partnerships in rare disease research.

Disease models & mechanisms·2025

This study details statistical methods, including analysis of variance (ANOVA), for detecting differential gene expression. It covers experimental design, statistical power, sample size, and multiple testing correction, with R code examples provided.

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Differential gene expression analysis is crucial for understanding biological processes.
  • Robust statistical methods are needed for accurate detection of expression changes.

Purpose of the Study:

  • To present statistical hypothesis testing methods for detecting differential gene expression.
  • To discuss practical considerations in experimental design, power, and sample size.
  • To describe methods for multiple testing correction and their application.

Main Methods:

  • Utilizes statistical hypothesis testing, including analysis of variance (ANOVA).
  • Addresses practical aspects of experimental design, statistical power, and sample size determination.
  • Explains multiple testing correction methods and their implementation.

Related Experiment Videos

Main Results:

  • Provides a framework for robust differential expression analysis.
  • Demonstrates the application of ANOVA and multiple testing correction.
  • Offers practical guidance for researchers in R.

Conclusions:

  • The presented methods enable reliable detection of differential gene expression.
  • Consideration of experimental design and multiple testing is essential for valid results.
  • R code and data are available to facilitate reproducible research.