Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

DNA Microarrays

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

Turns and Downturns in Aging Drivers.

medRxiv : the preprint server for health sciences·2026

Same author

A Scoping Review of Strategies to Increase Newcomer Family Access to Early Childhood Services.

Prevention science : the official journal of the Society for Prevention Research·2026

Same author

Measuring Real-World Understanding of Patterns in Data Graphics.

IEEE computer graphics and applications·2025

Same author

The Professional Identity of STEM Faculty as Instructors of Course-based Research Experiences.

Frontiers in education·2024

Same author

Temporal speech cue perception in listeners with cochlear implants depends on the time between those cues and previous sound energy.

The Journal of the Acoustical Society of America·2024

Same author

An algorithm for forensic toolmark comparisons.

Forensic science international. Synergy·2024

Same journal

Mammalian Respiratory Chain Complex Assemblies and Their Links to Mitochondria Stress-Induced Human Diseases.

Advances in experimental medicine and biology·2026

Same journal

Enzyme Assemblies in Nucleotide Metabolism: Structure, Regulation, and Disease Implications.

Advances in experimental medicine and biology·2026

Same journal

The Pyruvate Dehydrogenase Complex: A 90-Year-Old Enigma Shaping the Future of Structural Enzymology.

Advances in experimental medicine and biology·2026

Same journal

Regulation of the Anti-termination RNA Transcription Complex by Lon-Mediated Lambda N Degradation.

Advances in experimental medicine and biology·2026

Same journal

PCNA Macromolecular Complexes: PCNA Serves as a Molecular Hub Regulating Multiple Cellular Processes Inside and Outside of the Nucleus.

Advances in experimental medicine and biology·2026

Same journal

Dynamic Assemblies in Genome Maintenance.

Advances in experimental medicine and biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Clustering microarray data to determine normalization method.

Marie Vendettuoli¹, Erin Doyle, Heike Hofmann

¹Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50010, USA. mariev@iastate.edu

Advances in Experimental Medicine and Biology

|March 25, 2011

Summary

This summary is machine-generated.

This study introduces a seeded clustering algorithm to identify unknown microarray normalization methods. This approach helps ensure data reproducibility and adherence to Minimum Information About a Microarray Experiment (MIAME) standards.

More Related Videos

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

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

Published on: July 27, 2021

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jun 3, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

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

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

Published on: July 27, 2021

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray experiments require Minimum Information About a Microarray Experiment (MIAME) standards for reproducibility.
Normalization methods are crucial for microarray data analysis but are often poorly reported.
Accurate reporting of normalization is essential for result interpretation and experiment replication.

Purpose of the Study:

To propose a seeded clustering algorithm for identifying or verifying unknown microarray normalization methods.
To address the challenge of inaccurate or missing normalization information in published microarray studies.
To enhance the reliability and reproducibility of microarray data analysis.

Main Methods:

Generating descriptive statistics (mean, variance, quantiles, moments) from normalized gene expression data.
Clustering microarray chips based on these descriptive statistics.
Using a known dataset of normalization methods as seeds for clustering unknown datasets.

Main Results:

The seeded clustering algorithm successfully grouped chips based on their normalization methods.
Descriptive statistics effectively differentiate between various normalization techniques.
The method demonstrated potential in identifying normalization methods in unknown or doubtful situations.

Conclusions:

Seeded clustering provides a robust method for inferring normalization procedures in microarray experiments.
This technique can improve adherence to MIAME standards by verifying normalization details.
The approach aids researchers in understanding and validating complex gene expression data.