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

A robust statistical method for detecting differentially expressed genes.

Sunil Mathur1

  • 1Department of Mathematics, University of Mississippi, University, Mississippi, USA.

Applied Bioinformatics
|November 29, 2005
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

Exploring the underlying mechanisms through which financial strategies translate into improved performance in U.S health care organizations.

Health care management review·2026
Same author

Multicentre real-world evaluation of the Haematuria Cancer Risk Score to risk-stratify the detection of bladder cancer in patients referred from primary care.

BJUI compass·2026
Same author

ZNFX1 uses two-component ubiquitin circuitry to quarantine viral RNA.

Molecular cell·2026
Same author

A Phase II Study of Docetaxel and Pembrolizumab plus Interleukin 12 Gene Therapy in Nonmetastatic, Anthracycline-Refractory Triple-Negative Breast Cancer (INTEGRAL).

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Long term outcomes of older patients with classical hodgkin lymphoma: an analysis of the Texas cancer registry.

Annals of hematology·2025
Same author

Comparative Efficacy of Transurethral Laser Ablation Versus Conventional Methods in the Management of Recurrent Non-Muscle-Invasive Bladder Tumours.

Cureus·2025
Same journal

Statistically consistent identification of differentially expressed genes in DNA chip data over the whole expression range: relative variance method.

Applied bioinformatics·2006
Same journal

A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data.

Applied bioinformatics·2006
Same journal

Simulation study of ratio calculation formulae of two-colour cDNA microarray data.

Applied bioinformatics·2006
Same journal

Alternative mRNA polyadenylation can potentially affect detection of gene expression by affymetrix genechip arrays.

Applied bioinformatics·2006
Same journal

Comparisons of annotation predictions for affymetrix GeneChips.

Applied bioinformatics·2006
Same journal

Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists.

Applied bioinformatics·2006
See all related articles

This study introduces a new nonparametric test for identifying differentially expressed genes in DNA microarray experiments. The test is robust and efficient, especially when gene expression data does not follow a normal distribution.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • DNA microarrays enable high-throughput analysis of gene expression and DNA molecule levels.
  • Detecting differential gene expression is crucial for understanding biological processes and disease mechanisms.
  • Existing statistical tests often assume normal distribution, which may not hold true for microarray data.

Purpose of the Study:

  • To propose a novel nonparametric statistical test for identifying differentially expressed genes in replicated microarray experiments.
  • To address the limitations of normality assumptions in existing microarray data analysis.

Main Methods:

  • Development of a strictly nonparametric test statistic that does not assume parent population distribution.
  • Calculation of the p-value and asymptotic power function for the proposed test.

Related Experiment Videos

  • Comparison of the proposed test with existing methods using Monte Carlo simulations under various population distributions (normal, gamma, exponential).

    • Main Results:

    • The proposed nonparametric test demonstrates robustness and high efficiency, particularly for non-normal population distributions.
    • Simulation results indicate competitive or superior performance compared to existing tests under different data scenarios.
    • The test was successfully applied to real microarray data, highlighting its practical utility.

    Conclusions:

    • The developed nonparametric test provides a reliable method for identifying differentially expressed genes in microarray studies.
    • This approach is valuable when the normality assumption is violated, offering a more accurate analysis of biological data.
    • The test enhances the ability to identify key genes affected by experimental variables in diverse biological contexts.