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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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Competitive Genomic Screens of Barcoded Yeast Libraries

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Using the maximum between-class variance for automatic gridding of cDNA microarray images.

Gui-Fang Shao¹, Fan Yang, Qian Zhang

¹Department of Automation, Xiamen University, Xiamen 361005, P.R. China. gfshao@xmu.edu.cn

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|May 25, 2013

Summary

This summary is machine-generated.

This study introduces a fully automatic gridding technique for microarray image analysis, eliminating human intervention and preset parameters. The novel method enhances accuracy and robustness, improving gene expression results even with noisy data.

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Area of Science:

Bioinformatics
Computational Biology
Image Analysis

Background:

Gridding is crucial for microarray image analysis, separating spots into distinct areas.
Current gridding methods often require human intervention or preset parameters, limiting applicability and causing variations in gene expression results.
Improper gridding due to misalignment and noise negatively impacts high-throughput analysis.

Purpose of the Study:

To develop a fully automatic gridding technique for microarray images.
To overcome the limitations of traditional mathematical morphology gridding methods.
To improve the accuracy and robustness of gridding in the presence of noise.

Main Methods:

A preprocessing algorithm for noise reduction.
An improved Otsu method for optimal thresholding and spot localization.
Heuristic techniques for optimizing gridding results based on spot distribution estimation.

Main Results:

The proposed method demonstrates superiority over traditional morphology-based gridding.
The technique is robust in the presence of high noise levels.
Experiments on six datasets confirm the method's effectiveness.

Conclusions:

The developed fully automatic gridding technique is simple and effective.
It eliminates the need for human intervention and parameter presetting.
The method is applicable to various types of microarray images, improving gene expression analysis.