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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: May 20, 2026

High-Density DNA and RNA microarrays - Photolithographic Synthesis, Hybridization and Preparation of Large Nucleic Acid Libraries

High-Density DNA and RNA microarrays - Photolithographic Synthesis, Hybridization and Preparation of Large Nucleic Acid Libraries

Published on: August 12, 2019

A novel and fast normalization method for high-density arrays.

Maarten van Iterson¹, Floor A M Duijkers, Jules P P Meijerink

¹Center for Human and Clinical Genetics, Leiden University Medical Center.

Statistical Applications in Genetics and Molecular Biology

|August 2, 2012

Summary

This summary is machine-generated.

A new P-spline smoother method offers faster and simpler microarray normalization than traditional loess algorithms. This approach, avoiding random data subsets, provides more stable and reliable normalization curves for various array types.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Intensity-dependent normalization methods like loess are common for microarrays but computationally intensive.
Current loess implementations use data subsets to improve speed, but the impact of this is unassessed.
A novel, fast, and simple intensity-dependent normalization method for microarrays has been developed.

Purpose of the Study:

To develop a novel intensity-dependent normalization method for microarrays.
To assess the impact of using random data subsets for normalization.
To compare the novel method with existing loess algorithms.

Main Methods:

Developed a novel normalization method based on P-spline scatterplot smoother.
Utilized all data points for normalization.
Incorporated optional weighing of observations.

Main Results:

Using random subsets for normalization can lead to unstable results and should be avoided.
A constant weighted P-spline normalization provides a more reliable curve than subset-based normalization, demonstrated with DNA methylation arrays.
The novel method is simpler and faster than current loess algorithms.

Conclusions:

The novel P-spline based method is a faster and simpler alternative to loess normalization.
This method is applicable to both one- and two-colour array data.
An R package, TurboNorm, is available for implementing this method.