Automatic Techniques for Gridding cDNA Microarray Images

TL;DR

This paper compares four automatic gridding techniques for cDNA microarray images, highlighting the derivative of the sum profile as the most accurate method for various image qualities.

Contribution

It introduces and evaluates four novel automatic gridding techniques, improving accuracy and reducing user intervention in microarray image analysis.

Findings

Derivative of the sum profile technique is highly accurate.

Automatic methods outperform manual intervention.

Good performance on both high and low quality images.

Abstract

Microarray is considered an important instrument and powerful new technology for large-scale gene sequence and gene expression analysis. One of the major challenges of this technique is the image processing phase. The accuracy of this phase has an important impact on the accuracy and effectiveness of the subsequent gene expression and identification analysis. The processing can be organized mainly into four steps: gridding, spot isolation, segmentation, and quantification. Although several commercial software packages are now available, microarray image analysis still requires some intervention by the user, and thus a certain level of image processing expertise. This paper describes and compares four techniques that perform automatic gridding and spot isolation. The proposed techniques are based on template matching technique, standard deviation, sum, and derivative of these profiles. Experimental results show that the accuracy of the derivative of the sum profile is highly accurate compared to other techniques for good and poor quality microarray images.