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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 17, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Microarray data quality control improves the detection of differentially expressed genes.

Audrey Kauffmann¹, Wolfgang Huber

¹EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. audrey@ebi.ac.uk <audrey@ebi.ac.uk>

|January 19, 2010

Summary

This summary is machine-generated.

Assessing microarray data quality is crucial for biomedical research. Removing outlier arrays or using array weights significantly improves data quality and the detection of differentially expressed genes.

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

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Area of Science:

Biomedical research
Bioinformatics
Genomics

Background:

Microarrays are standard tools in biomedical research.
Objective and automated data quality assessment for microarrays remains challenging and is often overlooked.
Robust quality control is essential for reliable microarray data analysis.

Purpose of the Study:

To compare the effectiveness of outlier removal and array weighting strategies for microarray quality control.
To evaluate these methods against no outlier removal and random array removal.
To identify methods that objectively improve signal-to-noise ratio and gene expression analysis power.

Main Methods:

Comparison of two array-level quality control strategies: outlier removal and array weights.
Utilized five publicly available microarray datasets for analysis.
Benchmarked against control groups: no outlier removal and random array removal.

Main Results:

Removing outlier arrays demonstrably enhances the signal-to-noise ratio.
Improved signal-to-noise ratio leads to increased power in detecting differentially expressed genes.
Array weighting offers similar benefits but has more restricted applications.

Conclusions:

Outlier removal is an effective strategy for improving microarray data quality and analytical power.
Array weighting is a viable alternative with specific use cases.
The presented quality metrics are available in the Bioconductor package arrayQualityMetrics for practical implementation.