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

Methodological study of affine transformations of gene expression data with proposed robust non-parametric

Henrik Bengtsson1, Ola Hössjer

  • 1Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. hb@maths.lth.se

BMC Bioinformatics
|March 3, 2006
PubMed
Summary

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

Allele Frequency Spectra as a General Tool for Modeling Genetic Diversity.

Acta biotheoretica·2026
Same author

Gene frequency changes and a per-generation time-scale for continuous-time populations with arbitrary size variations.

Evolution; international journal of organic evolution·2026
Same author

Corrigendum to "Evaluating antimicrobial efficacy in medical devices: The critical role of simulating in use test conditions" [Biomater. Adv. 172 (2025), 214241].

Biomaterials advances·2026
Same author

A clarification of historical concepts of effective population size and their applicability to subdivided populations.

Theoretical population biology·2025
Same author

Titration and Treatment of Virtual Type 2 Diabetes Patients using an Adaptive Automated Insulin Delivery System.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Predicting Treatment Outcome of Patients with Type 2 Diabetes on Once-Daily Basal Insulin Injections using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Affine normalization effectively addresses non-linear effects in gene expression data, improving differential expression analysis. This robust method is available in the R aroma package for broad application.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray data analysis relies heavily on accurate low-level processing and normalization.
  • Existing normalization methods vary in effectiveness, necessitating further investigation.
  • Understanding microarray data characteristics is crucial for robust analysis.

Purpose of the Study:

  • To methodologically study affine models for gene expression data.
  • To investigate the strengths and weaknesses of existing normalization techniques within an affine model framework.
  • To propose a novel, robust normalization method applicable to diverse microarray data.

Main Methods:

  • Methodological study of affine models applied to gene expression data.
  • Revisiting and analyzing existing normalization methods (e.g., lowess, quantile, dye-swap) using the affine model.

Related Experiment Videos

  • Development and application of a robust non-parametric multi-dimensional affine normalization method.

    • Main Results:

    • An affine model effectively explains non-linear, intensity-dependent systematic effects in log-ratios.
    • Proposed affine normalization corrects artifacts for non-differentially expressed genes and ensures log-ratio symmetry.
    • Affine normalization unifies empirical distributions across channels, similar to quantile normalization.

    Conclusions:

    • Affine normalization is fundamental for accurate identification of differentially expressed genes.
    • The proposed method enhances data quality by reducing systematic biases.
    • All presented normalization methods are accessible via the R aroma package.