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

Analysis of variance for gene expression microarray data.

M K Kerr1, M Martin, G A Churchill

  • 1The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 31, 2001
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

Systems genetics uncover new loci containing functional gene candidates in <i>Mycobacterium tuberculosis</i>-infected Diversity Outbred mice.

bioRxiv : the preprint server for biology·2024
Same author

Genetic background influences susceptibility to chemotherapy-induced hematotoxicity.

The pharmacogenomics journal·2017
Same author

Identification of quantitative trait loci associated with acylsugar accumulation using intraspecific populations of the wild tomato, Lycopersicon pennellii.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2014
Same author

High-precision genetic mapping of behavioral traits in the diversity outbred mouse population.

Genes, brain, and behavior·2013
Same author

Discovery of blood transcriptomic markers for depression in animal models and pilot validation in subjects with early-onset major depression.

Translational psychiatry·2012
Same author

Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models.

Molecular psychiatry·2010
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

This study introduces ANOVA methods for normalizing microarray data, enabling accurate gene expression analysis. These techniques correct for biases and provide reliable error estimates for gene expression changes.

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Spotted cDNA microarrays offer cost-effective, large-scale gene expression analysis.
  • Simultaneous measurement of thousands of mRNAs across multiple samples is possible.
  • Appropriate statistical analysis of microarray data remains a challenge, particularly regarding bias and error estimation.

Purpose of the Study:

  • To address the need for valid estimates of relative gene expression from microarray data.
  • To develop methods for correcting biases from ancillary sources of variation.
  • To establish a framework for estimating error variation and constructing error bars for gene expression changes.

Main Methods:

  • Application of Analysis of Variance (ANOVA) methods for microarray data normalization.

Related Experiment Videos

  • Utilizing ANOVA to correct for potential confounding effects in gene expression analysis.
  • Developing a statistical framework for the interpretation of microarray data.

    • Main Results:

    • ANOVA methods effectively normalize microarray data.
    • Gene expression estimates are corrected for ancillary sources of variation.
    • Reliable error variation estimates can be obtained for changes in gene expression.

    Conclusions:

    • ANOVA provides a robust framework for analyzing and interpreting microarray data.
    • This approach enhances the accuracy and reliability of gene expression measurements.
    • The proposed methods facilitate a deeper understanding of gene expression patterns.