To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to make this decoupling a reality. The proposed system integrates and extends traditional analytical-based methods and custom-designed novel algorithms. The book first explores a new technique that takes advantage of a multiview approach to image analysis and addresses the challenges of applying powerful traditional techniques, such as clustering, to full-scale microarray experiments. It then presents an effective feature identification approach, an innovative technique that renders highly detailed surface models, a new approach to subgrid detection, a novel technique for the background removal process, and a useful technique for removing "noise." The authors also develop an expectation–maximization (EM) algorithm for modeling gene regulatory networks from gene expression time series data. The final chapter describes the overall benefits of these techniques in the biological and computer sciences and reviews future research topics. This book systematically brings together the fields of image processing, data analysis, and molecular biology to advance the state of the art in this important area. Although the text focuses on improving the processes involved in the analysis of microarray image data, the methods discussed can be applied to a broad range of medical and computer vision analysis areas. Cover page......Page 1 Title Page......Page 4 ISBN 9781420091533......Page 5 Table of Contents......Page 6 List of Figures......Page 11 List of Algorithms......Page 17 Preface and Acknowledgments......Page 18 Biographies......Page 19 1. Introduction......Page 21 1.1 Overview......Page 22 1.2 Current state of art......Page 23 1.3 Experimental approach......Page 25 1.4 Key issues......Page 27 1.4.3 Gene spot quantification......Page 28 1.4.4 Slide and experiment normalization......Page 29 1.5 Contribution to knowledge......Page 30 1.6 Structure of the book......Page 33 2.1 Introduction......Page 37 2.2.1 Inheritance and the structure of DNA......Page 38 2.2.2 Central dogma......Page 41 2.3.1 Gene expression......Page 42 2.3.2 Microarrays......Page 44 2.3.3 Process summary......Page 46 2.3.4 Final output......Page 47 2.4 Microarray analysis......Page 50 2.4.1 Addressing......Page 51 2.4.2 Segmentation......Page 53 2.4.3 Feature extraction......Page 61 2.4.4 GenePix interpretation......Page 62 2.4.5 Gene morphology......Page 65 2.5 Copasetic microarray analysis framework overview......Page 67 2.6 Summary......Page 72 3.1 Introduction......Page 73 3.2.1 Surface artifacts......Page 75 3.2.2 ITE precursor......Page 79 3.2.3 The method......Page 83 3.3.1 Experiment results......Page 87 3.3.2 Strengths and weaknesses......Page 95 3.4 Summary......Page 97 4.1 Introduction......Page 99 4.2.1 The algorithm......Page 102 4.2.2 Analysis......Page 105 4.3.1 Search grid analysis......Page 110 4.3.2 Synthetic data......Page 111 4.3.3 Real-world data......Page 115 4.3.4 Strengths and weaknesses......Page 117 4.4 Summary......Page 118 5.1 Introduction......Page 119 5.2 Image layout - master blocks......Page 121 5.2.1 The algorithm......Page 123 5.2.2 Evaluation......Page 126 5.3 Image structure – meta-blocks......Page 133 5.3.1 Stage one - create meta-block......Page 134 5.3.2 Stage two - external gene spot locations (phase I)......Page 135 5.3.3 Stage three - internal gene spot locations (phase I)......Page 137 5.3.4 Stage four - external gene spot locations (phase II)......Page 139 5.3.5 Stage five - internal gene spot locations (phase II)......Page 142 5.4 Summary......Page 145 6.1 Introduction......Page 147 6.2.2 The method......Page 149 6.3 Evaluation of feature identification......Page 158 6.3.1 Finding a gene spot’s location and morphology......Page 160 6.3.2 Recovering weak genes......Page 162 6.3.3 Strengths and weaknesses......Page 166 6.4.1 Peak signal-to-noise ratio for validation......Page 167 6.4.2 Strengths and weaknesses......Page 169 6.5 Summary......Page 171 7.1 Background......Page 173 7.2.1 Step 1: Filter the image......Page 178 7.2.2 Step 2: Spot spacing calculation......Page 180 7.2.3 Step 3: Subgrid shape detection......Page 181 7.2.4 Step 4: SubGrid detection......Page 189 7.3 Experimental results......Page 195 7.4 Conclusions......Page 208 8.1 Introduction......Page 209 8.2 Existing techniques......Page 210 8.3 A new technique......Page 212 8.3.1 Description......Page 213 8.3.2 Example and pseudo-code......Page 214 8.4.1 Dataset characteristics......Page 216 8.4.2 Synthetic data......Page 217 8.4.3 Real data......Page 218 8.5 Conclusions......Page 222 9.1 Introduction......Page 225 9.2 Existing techniques......Page 226 9.3.1 Description......Page 229 9.3.2 Pseudo-code and example......Page 230 9.4 Experiments and results......Page 231 9.4.2 Synthetic data......Page 232 9.4.3 Real data......Page 234 9.5 Conclusions......Page 237 10.1 Introduction......Page 239 10.2 Stochastic dynamic model for gene expression data......Page 241 10.3 An EM algorithm for parameter identification......Page 243 10.4.1 Modeling of yeast gene expression time series......Page 248 10.4.2 Modeling of virus gene expression time series......Page 251 10.4.3 Modeling of human malaria and worm gene expression time series......Page 254 10.5.1 Model quality evaluation......Page 255 10.5.2 Comparisons with existing modeling methods......Page 260 10.6 Conclusions and Future Work......Page 262 11.1 Introduction......Page 265 11.2 Achievements......Page 266 11.2.1 Noise reduction......Page 267 11.2.3 Gene spot quantification......Page 269 11.3.1 Technical......Page 270 11.3.2 Practical......Page 271 11.4 Contributions to computer science domain......Page 272 11.4.2 Practical......Page 273 11.5.1 Image transformation engine......Page 275 11.5.3 Image layout and image structure......Page 276 11.5.6 Other image sets......Page 277 11.5.7 Distributed communication subsystems......Page 278 12.1.1 Building the chips......Page 279 12.1.2 Digital generation......Page 281 12.2.1 Linear transform generation......Page 283 12.2.2 Square root transform generation......Page 284 12.2.3 Inverse transform generation......Page 286 12.2.4 Movement transform generation......Page 287 12.3 Appendix C: Clustering......Page 288 12.3.1 K-means algorithm......Page 292 12.3.3 Hierarchical clustering......Page 293 12.4 Appendix D: A glance on mining gene expression data......Page 295 12.4.1 Data analysis......Page 296 12.4.2 New challenges and opportunities......Page 297 12.5.1 Autocorrelation......Page 298 12.5.2 Generalized "circular" Hough transform......Page 299
To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to make this decoupling a reality. The proposed system integrates and extends traditional analytical-based methods and custom-designed novel algorithms.
The book first explores a new technique that takes advantage of a multiview approach to image analysis and addresses the challenges of applying powerful traditional techniques, such as clustering, to full-scale microarray experiments. It then presents an effective feature identification approach, an innovative technique that renders highly detailed surface models, a new approach to subgrid detection, a novel technique for the background removal process, and a useful technique for removing noise. The authors also develop an expectation–maximization (EM) algorithm for modeling gene regulatory networks from gene expression time series data. The final chapter describes the overall benefits of these techniques in the biological and computer sciences and reviews future research topics.
This book systematically brings together the fields of image processing, data analysis, and molecular biology to advance the state of the art in this important area. Although the text focuses on improving the processes involved in the analysis of microarray image data, the methods discussed can be applied to a broad range of medical and computer vision analysis areas.
"This book systematically brings together the fields of image processing, data analysis, and molecular biology to advance the state of the art in this important area. Although the text focuses on improving the processes involved in the analysis of microarray image data, the methods discussed can be applied to a broad range of medical and computer vision analysis areas."--Jacket
