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

Computational method for reducing variance with Affymetrix microarrays.

Stephen Welle1, Andrew I Brooks, Charles A Thornton

  • 1Department of Medicine, University of Rochester, Rochester, NY 14642, USA. stephen_welle@urmc.rochester.edu

BMC Bioinformatics
|September 3, 2002
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

Longitudinal Psychometric Properties of the Myotonic Dystrophy Health Index in a Large Multicenter Cohort of People Living With Myotonic Dystrophy Type 1.

Muscle & nerve·2026
Same author

Prospective Study of Video Hand Opening Time as a Quantitative Measurement of Myotonia in Patients With Myotonic Dystrophy Type 1.

Neurology·2026
Same author

An Antibody-Oligonucleotide Conjugate for Myotonic Dystrophy Type 1.

The New England journal of medicine·2026
Same author

Establishing biomarkers and clinical endpoints in myotonic dystrophy type 1 (END-DM1): Protocol of an international natural history study.

PloS one·2025
Same author

Elimination of myotonia improves myopathy in a muscleblind knockout model of myotonic dystrophy.

bioRxiv : the preprint server for biology·2025
Same author

Study of Testosterone and Recombinant Human Growth Hormone in Facioscapulohumeral Muscular Dystrophy.

Neurology. Genetics·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

The ratio method for analyzing Affymetrix microarray data reduces variability between arrays, enhancing the ability to detect smaller gene expression differences in human muscle tissue. This improves statistical power for gene expression profiling.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Affymetrix microarrays are widely used for gene expression profiling.
  • Detecting small expression differences (< 1.7-fold) is challenging due to inter-array variability.
  • Computational methods can potentially reduce variability and improve detection of subtle changes.

Purpose of the Study:

  • To compare two Affymetrix data analysis methods: the ratio method and the signal method.
  • To determine if the ratio method reduces inter-array variability compared to the signal method.
  • To assess the impact of these methods on detecting gene expression differences between young and old human muscle.

Main Methods:

  • Six HG-U95A Affymetrix arrays from young human muscle (21-31 yr) and six from older human muscle (62-77 yr) were analyzed.

Related Experiment Videos

  • Gene expression data were processed using both the pairwise comparison (ratio) method and the individual array (signal) method.
  • Statistical analyses included t-tests, rank sum tests, and Significance Analysis of Microarrays (SAM) to identify differentially expressed genes.

    • Main Results:

    • Differences in mean expression between young and old muscle were generally small (< 1.5-fold).
    • The ratio method showed a lower mean within-group coefficient of variation (20%) compared to the signal method (25%).
    • The ratio method identified more significant gene expression differences across multiple statistical tests and improved consistency between scanning methods.

    Conclusions:

    • The ratio method effectively reduces inter-array variance in Affymetrix microarray data.
    • This reduction in variance enhances the statistical power to detect subtle gene expression changes.
    • The ratio method is a valuable computational approach for improving gene expression profiling analysis.