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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...

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Related Experiment Video

Updated: May 18, 2026

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

A single-sample microarray normalization method to facilitate personalized-medicine workflows.

Stephen R Piccolo1, Ying Sun, Joshua D Campbell

  • 1Department of Pharmacology and Toxicology, University of Utah, 201 Presidents Circle, Salt Lake City, UT 84112, USA.

Genomics
|September 11, 2012
PubMed
Summary
This summary is machine-generated.

Single Channel Array Normalization (SCAN) corrects gene-expression microarray biases within individual samples. This method improves signal-to-noise ratios and reduces variability without needing multiple samples, ideal for personalized medicine.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Published on: July 27, 2021

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Last Updated: May 18, 2026

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

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

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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

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene-expression microarrays offer high-throughput biological analysis but suffer from technological biases and variability.
  • Existing normalization methods often require aggregate sample processing, which is impractical for serial data like in personalized medicine.

Purpose of the Study:

  • To develop a single-sample normalization technique for gene-expression microarrays.
  • To address the limitations of existing methods in personalized medicine workflows.

Main Methods:

  • Developed Single Channel Array Normalization (SCAN), a novel single-sample normalization technique.
  • SCAN models probe-nucleotide composition effects on fluorescence intensity to correct biases.
  • The method was validated through benchmark comparisons against existing techniques.

Main Results:

  • SCAN significantly increases the signal-to-noise ratio within individual samples.
  • SCAN decreases inter-sample variation.
  • SCAN performs comparably to or better than existing methods.

Conclusions:

  • SCAN provides an effective single-sample normalization solution for gene-expression microarrays.
  • The method is independent of external reference samples and applicable to any single-channel microarray platform.
  • SCAN is particularly suitable for personalized medicine applications with serially arriving samples.