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

Hypervariable genes--experimental error or hidden dynamics.

Igor Dozmorov1, Nicholas Knowlton, Yuhong Tang

  • 1Department of Arthritis and Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA. igor-dozmorov@omrf.ouhsc.edu

Nucleic Acids Research
|October 30, 2004
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

Guiding immunotherapy based on the oxford classification activity score in IgA nephropathy.

Frontiers in endocrinology·2026
Same author

Promoter methylation-associated brain-enriched long noncoding RNAs in glioblastoma: a multi-cohort public epigenomic re-analysis.

Epigenomics·2026
Same author

Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.

Biomedical engineering online·2026
Same author

Identification of Novel Risk Loci for Common B-Cell Lymphoma Subtypes Through Cross-Trait Analysis with Idiopathic Inflammatory Myopathies.

Cancers·2026
Same author

Landscape screening identifies the lactate-modifying enzyme AARS2 as a master regulator and therapeutic target in hepatocellular carcinoma.

Gut·2026
Same author

Functional and computational interrogation of juvenile idiopathic arthritis risk loci in CD4+ T cells.

HGG advances·2026

High variability genes (HV genes) in microarrays can reveal biological processes, not just errors. A new method identifies and clusters these HV genes to uncover co-expression patterns, aiding in understanding dynamic biological systems.

Area of Science:

  • * Genomics
  • * Bioinformatics
  • * Systems Biology

Background:

  • * Gene expression variability in microarrays can originate from biological processes, not solely experimental error.
  • * Identifying genes with high variability (HV genes) offers insights into dynamic biological functions.
  • * Existing methods may not adequately distinguish biological variability from technical noise.

Purpose of the Study:

  • * To develop a statistical procedure for selecting hypervariable (HV) genes from microarray data.
  • * To introduce a novel clustering technique, F-means clustering, for grouping HV genes with similar variability patterns.
  • * To demonstrate the utility of F-means clustering in identifying co-expressed HV genes in a disease context.

Main Methods:

  • * Exclusion of low-expressed genes and application of a stabilizing log-transformation.

Related Experiment Videos

  • * Utilization of an F-test to identify HV genes exhibiting statistically significant variability differences compared to a reference group.
  • * Application of a novel F-test clustering ('F-means clustering') to group HV genes based on shared variability patterns.

    • Main Results:

    • * A robust statistical method was established for selecting HV genes.
    • * F-means clustering successfully grouped HV genes, suggesting coordinated biological activity.
    • * The method was illustrated using microarray data from juvenile rheumatoid arthritis patients and healthy controls, identifying potential co-expressed gene modules.

    Conclusions:

    • * The developed statistical procedure effectively identifies biologically relevant HV genes.
    • * F-means clustering provides a novel approach to discover groups of co-expressed HV genes.
    • * This methodology enhances the understanding of dynamic biological processes and disease mechanisms through gene expression analysis.