Fraser K., Wang Z., Liu X. Microarray Image Analysis. An Algorithmic Approach

CRC Press, 2010. — 300 p.The paradigm shift heralded by the invention of DNA microarray technology has revolutionized the way in which biologists study the interaction and regulation of an organisms gene set. However, the technology is still in the early stages of design and development and there are, therefore, major challenges in the image processing phase of the analysis. Commercial microarray analysis systems typically rely on a human operator a great deal to complete the gene spot identification process. Any errors generated in the analysis stages will typically propagate through to the later stages of processing with the final analysis results not accounting for such errors. To harness the high-throughput potential of this technology it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance.
In this book, an automatic system for microarray image processing is proposed to make this decoupling a reality. The proposed system utilizes, extends and integrates traditional analytical-based methods and custom designed novel algorithms. In some cases, these traditional algorithms were incapable of directly processing the raw microarray imagery and therefore had to be optimized such that they would scale to these particularly large image datasets. In other cases, a novel method of clustering was proposed such that the underlying structure of a microarray image can be determined more readily via usage of the image’s spatial and contextual knowledge. Such a contextual clustering process has potential benefits in other image analysis domains.
The book brings together the disparate fields of image processing, data analysis and molecular biology to make a systematic attempt in advancing the state of the art in this important area. It is envisaged that this automatic system will have significant benefits not only for microarray users directly, e.g., repeatable results and sounder analysis, but also for computer scientists and practitioners in other domains.Background
Data Services
Structure Extrapolation I
Structure Extrapolation II
Feature Identification I
Feature Identification II
Chained Fourier Background Reconstruction
Graph-Cutting for Improving Microarray Gene Expression Reconstructions
Stochastic Dynamic Modeling of Short Gene Expression Time Series Data
ConclusionsA: Microarray variants
B: Basic transformations
C: Clustering
D: A glance on mining gene expression data
E: Autocorrelation and GHT