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

Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data.

T Ideker1, V Thorsson, A F Siegel

  • 1Department of Molecular Biotechnology, University of Washington, Seattle, WA 98195, USA. tideker@systemsbiology.org

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

Characterizing diversity in the tumor-immune microenvironment of distinct subclasses of gastroesophageal adenocarcinomas.

Annals of oncology : official journal of the European Society for Medical Oncology·2020
Same author

On entropy and information in gene interaction networks.

Bioinformatics (Oxford, England)·2018
Same author

Transcriptional repression of IFNβ1 by ATF2 confers melanoma resistance to therapy.

Oncogene·2015
Same author

Transcriptional noise and cellular heterogeneity in mammalian macrophages.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2006
Same author

Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2002
Same author

A new approach to decoding life: systems biology.

Annual review of genomics and human genetics·2001
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 a new statistical test for identifying differentially expressed genes using DNA microarrays. The method directly compares gene expression intensity measurements, improving accuracy in gene expression analysis.

Area of Science:

  • Molecular Biology
  • Statistical Genetics
  • Bioinformatics

Background:

  • Two-color fluorescent DNA microarrays are common in molecular biology labs.
  • Existing methods for analyzing microarray data are still developing.
  • Current approaches often rely on gene expression ratios.

Purpose of the Study:

  • To present a refined statistical test for identifying differentially expressed genes.
  • To move beyond gene expression ratios by directly comparing dye intensities.
  • To provide a more robust method for analyzing DNA microarray data.

Main Methods:

  • Developed a statistical model to account for multiplicative and additive errors in array experiments.
  • Estimated model parameters using maximum likelihood from observed gene intensities.

Related Experiment Videos

  • Applied a generalized likelihood ratio test to each gene for significance testing.
  • Compared the new method with existing approaches for differential gene expression analysis.

    • Main Results:

    • The refined test directly compares repeated measurements of two dye intensities for each gene.
    • The statistical model effectively describes errors influencing array experiments.
    • Significant differences in gene expression were identified in yeast cells under different conditions.
    • The method's performance was compared against current techniques.

    Conclusions:

    • The new statistical test offers a refined approach to identifying differentially expressed genes.
    • Directly comparing dye intensities provides a more robust analysis than ratio-based methods.
    • The error model aids in understanding and comparing intensity variations within and between slides.