Computer scientists and electrical engineers present an advanced graduate textbook on the main methods, tools, and techniques for microarray image and data analysis. The topics include gridding methods for DNA microarray images, non-statistical segmentation methods for DNA microarray images, microarray image restoration and noise filtering, quality control and analysis algorithms for tissue microarrays as biomarker validation tools, systematic and stochastic biclustering algorithms for microarray data-analysis, and the multidimensional visualization of microarray data. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis methods from the multidisciplinary international research community. Delivering a detailed discussion of the biological aspects and applications of microarrays, the book:
- Describes the key stages of image processing, gridding, segmentation, compression, quantification, and normalization
- Features cutting-edge approaches to clustering, biclustering, and the reconstruction of regulatory networks
- Covers different types of microarrays such as DNA, protein, tissue, and low- and high-density oligonucleotide arrays
- Examines the current state of various microarray technologies, including their availability and affordability
- Explains how data generated by microarray experiments are analyzed to obtain meaningful biological conclusions
An essential reference for academia and industry, Microarray Image and Data Analysis: Theory and Practice provides readers with valuable tools and techniques that extend to a wide range of biological studies and microarray platforms.
| Preface |
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ix | |
| Editor |
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xiii | |
| Contributors |
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xv | |
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Chapter 1 Introduction to Microarrays |
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1 | (40) |
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Chapter 2 Biological Aspects: Types and Applications of Microarrays |
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41 | (36) |
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Chapter 3 Gridding Methods for DNA Microarray Images |
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77 | (32) |
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Chapter 4 Machine Learning-Based DNA Microarray Image Gridding |
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109 | (20) |
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Chapter 5 Non-Statistical Segmentation Methods for DNA Microarray Images |
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129 | (20) |
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Chapter 6 Statistical Segmentation Methods for DNA Microarray Images |
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149 | (22) |
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Chapter 7 Microarray Image Restoration and Noise Filtering |
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171 | (24) |
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Chapter 8 Compression of DNA Microarray Images |
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195 | (32) |
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Miguel Hernandez-Cabronero |
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Chapter 9 Image Processing of Affymetrix Microarrays |
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227 | (28) |
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Jose Manuel Arteaga-Salas |
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Chapter 10 Treatment of Noise and Artifacts in Affymetrix Arrays |
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255 | (28) |
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Chapter 11 Quality Control and Analysis Algorithms for Tissue Microarrays as Biomarker Validation Tools |
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283 | (30) |
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Chapter 12 CNV-Interactome-Transcriptome Integration to Detect Driver Genes in Cancerology |
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313 | (26) |
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Chapter 13 Mining Gene-Sample-Time Microarray Data |
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339 | (30) |
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Chapter 14 Systematic and Stochastic Biclustering Algorithms for Microarray Data Analysis |
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369 | (32) |
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Chapter 15 Reconstruction of Regulatory Networks from Microarray Data |
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401 | (30) |
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Chapter 16 Multidimensional Visualization of Microarray Data |
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431 | (28) |
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Chapter 17 Bioconductor Tools for Microarray Data Analysis |
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459 | (22) |
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| Index |
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481 | |
Luis Rueda is professor for the School of Computer Science, University of Windsor, Ontario, Canada. Before joining the University of Windsor, he earned a Ph.D from Carleton University, Ottawa, Ontario, Canada and spent two years at the University of Concepción, Chile. A member of IEEE, the Association for Computing Machinery, and the International Society for Computational Biology, he holds three patents on data encryption, secrecy, and stealth; has published over 100 journal and conference papers; and has participated in numerous editorial and technical committees. His research is primarily focused on machine learning and pattern recognition in transcriptomics, interactomics, and genomics.
