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

Partitioning large-sample microarray-based gene expression profiles using principal components analysis.

Leif E Peterson1

  • 1Department of Medicine, Baylor College of Medicine, One Baylor Plaza ST-924, Houston, TX 77030, USA. peterson@bcm.tmc.edu

Computer Methods and Programs in Biomedicine
|January 1, 2003
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

Evaluating bone marrow dosimetry with the addition of bone marrow structures to the medical internal radiation dose phantom.

Precision radiation oncology·2025
Same author

Fast Progression in Amyotrophic Lateral Sclerosis Is Associated With Greater TDP-43 Burden in Spinal Cord.

Journal of neuropathology and experimental neurology·2021
Same author

Hippocampal Transcriptome Changes After Subarachnoid Hemorrhage in Mice.

Frontiers in neurology·2021
Same author

Determinants of outcome impact of vein of Marshall ethanol infusion when added to catheter ablation of persistent atrial fibrillation: A secondary analysis of the VENUS randomized clinical trial.

Heart rhythm·2021
Same author

Effect of Catheter Ablation With Vein of Marshall Ethanol Infusion vs Catheter Ablation Alone on Persistent Atrial Fibrillation: The VENUS Randomized Clinical Trial.

JAMA·2020
Same author

An Injury Prevention Program for Professional Ballet: A Randomized Controlled Investigation.

Orthopaedic journal of sports medicine·2020
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
See all related articles

Principal components analysis (PCA) groups genes with similar expression profiles using latent factors. This method effectively identifies gene clusters from DNA microarray data, accounting for over 90% of total variance.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput gene expression studies generate vast datasets.
  • Identifying patterns in gene expression is crucial for understanding biological processes.
  • Existing methods may not efficiently group genes with similar expression profiles.

Purpose of the Study:

  • To apply Principal Components Analysis (PCA) for grouping genes with similar expression profiles.
  • To utilize the CLUSFAVOR computer program for implementing PCA on DNA microarray data.
  • To identify latent factors that explain significant variance in gene expression.

Main Methods:

  • Factor model of the correlation matrix (R) using PCA.
  • Calculation of eigenvalues and eigenvectors.

Related Experiment Videos

  • Extraction of factors with eigenvalues exceeding unity.
  • Analysis of factor loadings to identify gene groupings.

    • Main Results:

    • PCA effectively reproduces total variation in gene expression data.
    • Extracting factors with eigenvalues > 1 accounted for >90% of total variance.
    • Bipolar factors identified distinct gene groups with contrasting expression profiles.
    • CLUSFAVOR program facilitated the identification of genes with similar loading patterns.

    Conclusions:

    • PCA provides a heuristic for assembling natural groupings of genes with similar expression profiles.
    • This approach complements traditional cluster analysis by focusing on major variance components.
    • PCA is a valuable tool for analyzing large-scale gene expression data from DNA microarrays.