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

Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm.

Thomas Grotkjaer1, Ole Winther, Birgitte Regenberg

  • 1Center for Microbial Biotechnology BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. tg@biocentrum.dtu.dk

Bioinformatics (Oxford, England)
|November 1, 2005
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

Functional role of small extrachromosomal circular DNA in colorectal cancer.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

An enhancement of extrachromosomal circular DNA enrichment and amplification to address the extremely low overlap between replicates.

The Journal of biological chemistry·2026
Same author

MOTS-c improves intrinsic muscle mitochondrial bioenergetic health and efficiency in a PGC-1α/AMPK-dependent manner.

Free radical biology & medicine·2026
Same author

Extrachromosomal circular miRNA-gene amplifications contribute to the renal cancer phenotype.

Cell reports·2025
Same author

DNA Circles as Vehicles for Genes to Evade Chromosomal Discipline.

Genome biology and evolution·2025
Same author

Spatial-Temporal Diversity of Extrachromosomal DNA Shapes Urothelial Carcinoma Evolution and the Tumor Immune Microenvironment.

Cancer discovery·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

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

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

A new consensus clustering algorithm improves DNA microarray analysis by averaging multiple runs for robust, reproducible results. This method captures subtle expression variations and reduces classification errors, enhancing biological pattern discovery.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional clustering methods like K-means are sensitive to outliers and initialization, hindering result significance assessment in DNA microarray analysis.
  • Existing methods struggle to robustly capture small signal variations crucial for understanding gene expression patterns.

Purpose of the Study:

  • To develop a robust and reproducible consensus clustering algorithm for DNA microarray data analysis.
  • To improve the identification of biologically meaningful transcriptional patterns by averaging results from multiple clustering runs.

Main Methods:

  • Developed a consensus clustering algorithm that averages results over multiple runs to create a co-occurrence matrix of clustering patterns.
  • Applied the algorithm using Variational Bayes Mixtures of Gaussians and K-means clustering on simulated and real DNA microarray datasets.

Related Experiment Videos

  • Compared the consensus clustering approach against state-of-the-art clustering methods.

    • Main Results:

    • Consensus clustering significantly reduced classification error rates on simulated datasets compared to standard methods.
    • The algorithm demonstrated robustness and effectiveness on real biological datasets, identifying meaningful transcriptional patterns.
    • The method provides a flexible framework for quantitatively assessing the homogeneity of different clustering algorithms.

    Conclusions:

    • Consensus clustering offers a robust and reproducible approach to DNA microarray analysis, outperforming traditional methods.
    • The algorithm effectively captures small signal variations and enhances the discovery of biologically relevant gene expression patterns.
    • This framework facilitates a more reliable interpretation of complex genomic data.