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

Regression approaches for microarray data analysis.

Mark R Segal1, Kam D Dahlquist, Bruce R Conklin

  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143-0560, USA. mark@biostat.ucsf.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|February 26, 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

A multi-omics study reveals pathway-level insights and predictive biomarkers in pediatric TB.

Clinical proteomics·2026
Same author

Discovery and Validation of SVEP1 and Other Novel Cardiovascular Biomarkers For Patients with Kidney Failure On Maintenance Hemodialysis.

medRxiv : the preprint server for health sciences·2026
Same author

Integrating Genomics and Proteomics to Identify Causal Proteins and Biologic Pathways for Incident Atrial Fibrillation in CKD.

Journal of the American Society of Nephrology : JASN·2026
Same author

Clinical cure of chronic hepatitis B is associated with priming and perpetuation of hepatic CD4<sup>+</sup> T cell responses.

Science translational medicine·2026
Same author

Leveraging human genetic variation to therapeutically target hundreds of genes with dominant & dispensable disease alleles.

medRxiv : the preprint server for health sciences·2026
Same author

Using image classifiers to predict CMT2A disease-relevant mitochondrial motility phenotypes in iPSC motor neurons.

bioRxiv : the preprint server for biology·2026
Same journal

Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

New methods for analyzing gene expression data from microarrays are crucial due to high dimensionality and gene correlations. Regularized regression techniques like lasso are recommended over basic gene harvesting for accurate results.

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Genetics

Background:

  • Microarray studies present challenges like high gene numbers versus limited samples and correlated gene expression due to biological pathways.
  • These challenges are amplified in regression analyses aiming to link multiple gene expressions to external phenotypes.

Purpose of the Study:

  • To evaluate existing and novel procedures for two-sample gene expression comparisons using microarrays.
  • To assess the effectiveness of gene harvesting and regularized regression methods in handling high-dimensional microarray data.

Main Methods:

  • Critique of existing gene expression analysis methods.
  • Detailed evaluation of the gene harvesting technique.
  • Application and comparison of regularized regression procedures: lasso, least angle regression, and support vector machines.

Related Experiment Videos

  • Analysis of model selection and solution multiplicity.

    • Main Results:

    • Gene harvesting, without constraints, can produce artifactual results.
    • Regularized regression methods, when appropriately constrained, offer more reliable solutions.
    • The study highlights the importance of addressing model selection and solution multiplicity in microarray data analysis.

    Conclusions:

    • Regularized regression procedures are essential for robust analysis of high-dimensional, correlated gene expression data from microarrays.
    • Careful consideration of constraints and regularization is necessary to avoid artifacts in gene expression analysis.
    • The findings are validated using a mouse model of cardiomyopathy.