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 Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

<i>In vitro</i> model reveals structural and metabolic insights into the porcine cecal microbiota in response to β-mannan exposure.

Applied and environmental microbiology·2026
Same author

A Benchmark Evaluation of Chemical Structure Extraction from Patents: Insights and Challenges in Chemical Structure Recognition.

Chemical research in toxicology·2026
Same author

Profiling histone post-translational modifications to identify signatures of epigenetic drug response in T-cell acute lymphoblastic leukemia.

Scientific reports·2026
Same author

OmniCorr: an R-package for visualizing putative host-microbiome interactions using multi-omics data.

Bioinformatics advances·2026
Same author

Patterns of chromosomal instability and epigenetic alterations in colorectal cancer progression: from high-grade dysplasia to liver metastases.

Mutagenesis·2026
Same author

Conservation of the short-day vernalization flowering response pathway in temperate Pooideae grasses.

The New phytologist·2026

Related Experiment Video

Updated: Jun 8, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and

Eva Freyhult1, Mattias Landfors, Jenny Önskog

  • 1Department of Clinical Microbiology, Division of Clinical Bacteriology, Umeå University, Umeå, Sweden. eva.freyhult@medsci.uu.se

BMC Bioinformatics
|October 13, 2010
PubMed
Summary

Choosing the right gene selection and clustering methods is crucial for accurate gene expression analysis. Normalization also positively impacts results, but further research is needed on optimal methods.

More Related Videos

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Related Experiment Videos

Last Updated: Jun 8, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cluster analysis, especially hierarchical clustering, is vital for gene expression data interpretation.
  • Discovering novel classes of individuals or genes relies on effective clustering.
  • Pre-processing steps like normalization and gene selection significantly influence clustering outcomes.

Purpose of the Study:

  • To comprehensively evaluate various cluster analysis methods for gene expression data.
  • To assess the impact of normalization, missing value imputation, standardization, gene selection, and clustering algorithms on performance.
  • To identify optimal combinations of methods for robust biological class discovery.

Main Methods:

  • Evaluation of 2780 distinct cluster analysis pipelines.
  • Utilized seven public 2-channel microarray datasets with known class labels.
  • Assessed performance using the adjusted Rand index, comparing different normalization, imputation, standardization, gene selection, and clustering techniques.

Main Results:

  • Performance varied significantly across datasets and methods; general recommendations are challenging.
  • Normalization, gene selection, and clustering methods critically impact clustering accuracy.
  • Gene selection is paramount; including numerous genes, particularly those with high standard deviation or selected via Principal Component Analysis (PCA), is often beneficial. Hierarchical clustering (Ward's method), k-means, and Mclust demonstrated superior performance.

Conclusions:

  • The selection of clustering and gene selection methods profoundly affects the correct classification of individuals based on expression profiles.
  • Normalization generally enhances clustering, but optimal methods require further investigation.
  • Despite being standard bioinformatics tools, the optimal application and combination of clustering, gene selection, and normalization methods remain complex and underexplored.