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

Approximate geodesic distances reveal biologically relevant structures in microarray data.

Jens Nilsson1, Thoas Fioretos, Mattias Höglund

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

Bioinformatics (Oxford, England)
|January 31, 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

Thymic output in human newborns is shaped by environmental exposures and a common TCRD genetic variant.

Journal of human immunity·2026
Same author

Institutional lessons from the Nordic management model.

Conservation biology : the journal of the Society for Conservation Biology·2026
Same author

<i>TWIST2</i> high expression defines a novel subtype of B-cell precursor acute lymphoblastic leukemia.

HemaSphere·2026
Same author

Stage-Independent Real-Time Subtype Classification and Comprehensive Biopsy Profiling of Urothelial Carcinomas by the Lund Taxonomy System.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2026
Same author

Correction: Diagnostic whole transcriptome sequencing in a series of 1233 FFPE solid tumor samples.

British journal of cancer·2026
Same author

Re: Joshua J. Meeks. Urothelial Cancer: What Is the Role of Expression-based Subtypes to Guide Neoadjuvant Therapy in Muscle-invasive Bladder Cancer? Eur Urol Focus. In press. https://doi.org/10.1016/j.euf.2025.11.014.

European urology focus·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

This study shows that approximate geodesic distance, calculated using the Isomap algorithm, better visualizes biological similarities in gene expression data than Euclidean distance. This approach improves microarray data interpretation by accounting for nonlinearities.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene expression data from microarrays exist in high-dimensional space.
  • Gene regulatory networks constrain expression profiles to a data manifold.
  • Understanding this manifold is key to interpreting biological similarities.

Purpose of the Study:

  • To evaluate if geodesic distance better captures biological similarities than Euclidean distance.
  • To assess the utility of the Isomap algorithm for microarray data analysis.
  • To investigate the importance of nonlinear data structures in gene expression.

Main Methods:

  • Applied the Isomap algorithm to compute approximate geodesic distances.
  • Utilized microarray data from lymphoma and lung cancer samples.

Related Experiment Videos

  • Employed multidimensional scaling for data visualization.

    • Main Results:

    • Geodesic distance visualizations were more biologically relevant than Euclidean distance.
    • The Isomap algorithm proved effective for interpreting gene expression data.
    • Nonlinear structures within gene expression data significantly impact similarity measures.

    Conclusions:

    • Approximate geodesic distance offers superior biological insight compared to Euclidean distance.
    • The Isomap algorithm is a valuable tool for analyzing complex gene expression data.
    • Accounting for nonlinearities is crucial for accurate biological interpretation of microarray data.