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

Visualizing and clustering high throughput sub-cellular localization imaging.

Nicholas A Hamilton1, Rohan D Teasdale

  • 1ARC Centre of Excellence in Bioinformatics, The University of Queensland, Brisbane, Queensland 4072, Australia. n.hamilton@imb.uq.edu.au

BMC Bioinformatics
|February 5, 2008
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

Disruption of Retriever Function Impacts Retrograde Trafficking From Endosomes.

Cell biology international·2025
Same author

Pediatric Trauma Care Standardization: A Statewide Survey of Trauma Providers and Program Managers.

The Journal of surgical research·2023
Same author

Impact of institutional prophylaxis guidelines on rates of pediatric venous thromboembolism following trauma-A multicenter study from the pediatric trauma society research committee.

The journal of trauma and acute care surgery·2023
Same author

Sugar transporter Slc37a2 regulates bone metabolism in mice via a tubular lysosomal network in osteoclasts.

Nature communications·2023
Same author

Multifaceted Roles of Retromer in EGFR Trafficking and Signaling Activation.

Cells·2022
Same author

Venous thromboembolic screening in pediatric trauma: A prospective cohort study of risk-stratified ultrasonography.

The journal of trauma and acute care surgery·2022
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

A new method, iCluster, visualizes and clusters large sub-cellular localization image sets. This tool aids in comparing images and identifying variations, proving valuable for cell biology research.

Area of Science:

  • Cell Biology
  • Microscopy
  • Bioinformatics

Background:

  • Automatic imaging technologies generate vast datasets, necessitating efficient comparison methods.
  • Sub-cellular protein expression varies significantly between cells, often undetected in large image sets.
  • Current methods struggle with visualizing and comparing extensive image collections.

Purpose of the Study:

  • Introduce iCluster, a novel methodology for visualizing, clustering, and comparing large sub-cellular localization image sets.
  • Develop a system to define normal variation within images of the same sample for accurate comparisons.
  • Enable efficient analysis of high-throughput microscopy data.

Main Methods:

  • iCluster generates distinguishing statistics for each image in a set.

Related Experiment Videos

  • Statistics are mapped to 2D or 3D space, preserving inter-image distances.
  • The complete image set is visualized, grouping statistically similar images spatially.

    • Main Results:

    • Tested on 502 images with 10 known sub-cellular localizations.
    • 3D visualization achieved 83.2% 3-neighbor classification accuracy.
    • 2D visualization showed 68.9% accuracy, with clear visual clustering.
    • Computational cost was low, enabling real-time interaction with up to 1400 images.

    Conclusions:

    • Automated spatial layout effectively enables comparison and discrimination of high-throughput sub-cellular imaging.
    • Potential applications include image database curation, interactive classification, and outlier detection.
    • Provides cell biologists an invaluable tool for observing the full range of modern microscopy imaging data.