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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: Jul 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

Finding differentially expressed genes in two-channel DNA microarray datasets: how to increase reliability of data

Ana Rotter¹, Matjaz Hren, Spela Baebler

¹Department of Biotechnology and Systems Biology, National Institute of Biology, 1000 Ljubljana, Slovenia. ana.rotter@nib.si

Omics : a Journal of Integrative Biology

|September 6, 2008

Summary

This summary is machine-generated.

Choosing the right preprocessing methods is crucial for accurate microarray analysis. This study shows that normalization significantly impacts gene expression results, and combining two distinct methods enhances reliability.

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Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

Two-channel expression microarray data analysis involves numerous preprocessing tools, making method selection challenging.
The choice of preprocessing steps can significantly influence the identification of differentially expressed genes.

Purpose of the Study:

To investigate the impact of different preprocessing methods on gene discovery in two-channel microarray data.
To propose a strategy for improving the reliability of results from microarray data analysis.

Main Methods:

Utilized two in-house and one public two-channel microarray dataset.
Evaluated the influence of background correction, normalization, and duplicate spot correlation on differentially expressed gene identification.
Compared results from various preprocessing pipelines.

Main Results:

The selection of preprocessing methods, particularly normalization, significantly affects the list of differentially expressed genes and their expression values.
Normalization methods showed the highest impact on the study outcomes.
A novel approach combining two distinct normalization methods improved result reliability.

Conclusions:

Preprocessing choices critically influence microarray data analysis outcomes.
Employing two theoretically distinct normalization methods and analyzing their intersection provides a more robust identification of differentially expressed genes.
This combined approach enhances the accuracy and reliability of gene expression analysis.