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

Normalization of microarray data using a spatial mixed model analysis which includes splines.

David Baird1, Peter Johnstone, Theresa Wilson

  • 1AgResearch, Lincoln, New Zealand. david.baird@agresearch.co.nz

Bioinformatics (Oxford, England)
|July 3, 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

Phase II trial of combination radiation, hormone, and immunotherapy in grade group 5 prostate cancer.

Journal for immunotherapy of cancer·2026
Same author

Empowering through structured boundaries: an integral model for fostering balanced eating and nutritional well-being.

Journal of rare diseases (Berlin, Germany)·2026
Same author

De-diagnosing chronic lymphocytic leukaemia: An ethical and scientific case for changing diagnostic criteria.

HemaSphere·2025
Same author

Early Detection of Metastatic Progression by Circulating Tumor DNA in Patients Undergoing Bladder-Preserving Trimodality Therapy.

The Journal of urology·2025
Same author

Impact of recent clinical trials on meta-analysis of stereotactic body radiation therapy for spine metastases and urgent call for consistent study endpoints.

Annals of palliative medicine·2025
Same author

Improving Antiemetic and Analgesic Prescribing Practices for Patients With Advanced Liver Disease: A Clinical Audit.

Cureus·2025

New spatial mixed models effectively normalize microarray data, reducing unwanted variation. This improves the identification of gene expression differences in complex experiments.

Area of Science:

  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray experiments generate vast datasets with numerous sources of variation.
  • Identifying these variations is crucial for accurate gene expression analysis.

Purpose of the Study:

  • To develop novel normalization methods for microarray data analysis.
  • To improve the accuracy of identifying gene expression differences.

Main Methods:

  • Utilized spatial mixed models incorporating splines for analyzing two-colour spotted microarrays.
  • Applied methods to address both within-slide and between-slide variations.

Main Results:

  • The developed model explains a significant portion (45-85%) of slide variation using minimal degrees of freedom.

Related Experiment Videos

  • Outperformed intensity-dependent normalization methods, accounting for twice as much unwanted variation.
  • Introduced an EST index for rapid assessment of gene of interest.

    • Conclusions:

    • Spatial mixed models offer a robust approach for microarray data normalization.
    • These methods enhance the reliability of gene expression difference detection.
    • The EST index provides a valuable tool for researchers in gene analysis.