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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...

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Updated: Jun 2, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Assessing affymetrix GeneChip microarray quality.

Matthew N McCall¹, Peter N Murakami, Margus Lukk

¹Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N, Wolfe St,, Baltimore, MD, USA.

BMC Bioinformatics

|May 10, 2011

Summary

This summary is machine-generated.

Assessing microarray quality is crucial for reliable biological data. This study defines quality metrics and finds 10% of public microarray data are poor quality, highlighting variability in experimental results.

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Last Updated: Jun 2, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

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Published on: March 15, 2011

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

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Area of Science:

Genomics and Bioinformatics
Molecular Biology
Biotechnology

Background:

Microarray technology is widely used in biological sciences with rapidly increasing applications and data analyses.
Initiatives like ERCC and MAQC aim to establish standards for microarrays.
A lack of consensus exists in defining and measuring microarray quality, hindering its general acceptance as a reliable technology.

Purpose of the Study:

To define microarray quality and evaluate existing Affymetrix GeneChip quality metrics.
To develop and validate a single-array quality metric for clinical applications.
To assess the quality of publicly available microarray data.

Main Methods:

Defined microarray quality and reviewed Affymetrix GeneChip metrics.
Developed a single-array quality metric based on multi-array metrics.
Assessed over 22,000 Affymetrix arrays from 809 studies in the Gene Expression Omnibus (GEO).

Main Results:

The best-performing multi-array quality metrics require simultaneous assessment of multiple arrays.
A novel single-array quality metric was developed and shown to perform comparably to multi-array metrics.
Approximately 10% of publicly available microarray data in GEO were found to be of poor quality.

Conclusions:

Microarray data quality varies significantly across hybridizations, studies, and laboratories.
Some experiments yield unusable microarray data, emphasizing the need for robust quality assessment.
The developed concepts and metrics are applicable to other high-throughput technologies.