Microarray Image Analysis: An Algorithmic Approach (Chapman & Hall CRC Computer Science & Data Analysis)

300 Pages·2010·4.802 MB·English

by  Karl Fraser, Zidong Wang, Xiaohu Liu

Microarray
Image Analysis
An Algorithmic Approach
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Computer Science and Data Analysis Series
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Microarray
Image Analysis
An Algorithmic Approach
Karl Fraser
Zidong Wang
Xiaohui Liu
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Library of Congress Cataloging‑in‑Publication Data
Fraser, Karl.
Microarray image analysis : an algorithmic approach / Karl Fraser, Zidong Wang,
Xiaohui Liu.
p. cm. -- (Computer science and data analysis series)
Includes bibliographical references and index.
ISBN 978-1-4200-9153-3 (hardcover : alk. paper)
1. DNA microarrays. 2. Image processing--Digital techniques. I. Wang, Zidong. II.
Liu, Xiaohui. III. Title.
QP624.5.D726F73 2010
572.8’636--dc22 2009044676
Visit the Taylor & Francis Web site at
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Table of Contents
List of Figures xiii
List of Algorithms xix
Preface and Acknowledgments xxi
Biographies xxiii
1 Introduction 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Current state of art . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Experimental approach . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Key issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.1 Noise reduction . . . . . . . . . . . . . . . . . . . . . . 8
1.4.2 Gene spot identification . . . . . . . . . . . . . . . . . 8
1.4.3 Gene spot quantification . . . . . . . . . . . . . . . . . 8
1.4.4 Slide and experiment normalization. . . . . . . . . . . 9
1.5 Contribution to knowledge . . . . . . . . . . . . . . . . . . . . 10
1.6 Structure of the book . . . . . . . . . . . . . . . . . . . . . . . 13
2 Background 17
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 Molecular biology . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2.1 Inheritance and the structure of DNA . . . . . . . . . 18
2.2.2 Central dogma . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Microarraytechnology . . . . . . . . . . . . . . . . . . . . . . 22
2.3.1 Gene expression. . . . . . . . . . . . . . . . . . . . . . 22
2.3.2 Microarrays . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.3 Process summary . . . . . . . . . . . . . . . . . . . . . 26
2.3.4 Final output . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 Microarrayanalysis . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4.1 Addressing . . . . . . . . . . . . . . . . . . . . . . . . 31
2.4.2 Segmentation . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.3 Feature extraction . . . . . . . . . . . . . . . . . . . . 41
2.4.4 GenePix interpretation . . . . . . . . . . . . . . . . . . 42
2.4.5 Gene morphology . . . . . . . . . . . . . . . . . . . . . 45
2.5 Copasetic microarrayanalysis framework overview . . . . . . 47
vii
viii Table of Contents
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3 Data Services 53
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.2 Image transformation engine . . . . . . . . . . . . . . . . . . . 55
3.2.1 Surface artifacts . . . . . . . . . . . . . . . . . . . . . 55
3.2.2 ITE precursor . . . . . . . . . . . . . . . . . . . . . . . 59
3.2.3 The method . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3.1 Experiment results . . . . . . . . . . . . . . . . . . . . 67
3.3.2 Strengths and weaknesses . . . . . . . . . . . . . . . . 75
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4 Structure Extrapolation I 79
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.2 Pyramidic contextual clustering . . . . . . . . . . . . . . . . . 82
4.2.1 The algorithm. . . . . . . . . . . . . . . . . . . . . . . 82
4.2.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.3.1 Search grid analysis . . . . . . . . . . . . . . . . . . . 90
4.3.2 Synthetic data . . . . . . . . . . . . . . . . . . . . . . 91
4.3.3 Real-worlddata . . . . . . . . . . . . . . . . . . . . . . 95
4.3.4 Strengths and weaknesses . . . . . . . . . . . . . . . . 97
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5 Structure Extrapolation II 99
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.2 Image layout - master blocks . . . . . . . . . . . . . . . . . . 101
5.2.1 The algorithm. . . . . . . . . . . . . . . . . . . . . . . 103
5.2.2 Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . 106
5.3 Image structure – meta-blocks . . . . . . . . . . . . . . . . . . 113
5.3.1 Stage one - create meta-block . . . . . . . . . . . . . . 114
5.3.2 Stage two - external gene spot locations (phase I) . . . 115
5.3.3 Stage three - internal gene spot locations (phase I) . . 117
5.3.4 Stage four - external gene spot locations (phase II) . . 119
5.3.5 Stage five - internal gene spot locations (phase II) . . 122
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6 Feature Identification I 127
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
6.2 Spatial binding . . . . . . . . . . . . . . . . . . . . . . . . . . 129
6.2.1 Pyramidic contextual clustering - revisited . . . . . . . 129
6.2.2 The method . . . . . . . . . . . . . . . . . . . . . . . . 129
6.3 Evaluation of feature identification . . . . . . . . . . . . . . . 138
6.3.1 Finding a gene spot’s location and morphology . . . . 140
Table of Contents ix
6.3.2 Recovering weak genes . . . . . . . . . . . . . . . . . . 142
6.3.3 Strengths and weaknesses . . . . . . . . . . . . . . . . 146
6.4 Evaluation of copasetic microarrayanalysis framework . . . . 147
6.4.1 Peak signal-to-noise ratio for validation . . . . . . . . 147
6.4.2 Strengths and weaknesses . . . . . . . . . . . . . . . . 149
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
7 Feature Identification II 153
7.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
7.2 Proposed approach – subgrid detection . . . . . . . . . . . . . 158
7.2.1 Step 1: Filter the image . . . . . . . . . . . . . . . . . 158
7.2.2 Step 2: Spot spacing calculation. . . . . . . . . . . . . 160
7.2.3 Step 3: Subgrid shape detection . . . . . . . . . . . . . 161
7.2.4 Step 4: SubGrid detection . . . . . . . . . . . . . . . . 169
7.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . 175
7.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
8 Chained Fourier Background Reconstruction 189
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
8.2 Existing techniques . . . . . . . . . . . . . . . . . . . . . . . . 190
8.3 A new technique . . . . . . . . . . . . . . . . . . . . . . . . . 192
8.3.1 Description . . . . . . . . . . . . . . . . . . . . . . . . 193
8.3.2 Example and pseudo-code . . . . . . . . . . . . . . . . 194
8.4 Experiments and results . . . . . . . . . . . . . . . . . . . . . 196
8.4.1 Dataset characteristics . . . . . . . . . . . . . . . . . . 196
8.4.2 Synthetic data . . . . . . . . . . . . . . . . . . . . . . 197
8.4.3 Real data . . . . . . . . . . . . . . . . . . . . . . . . . 198
8.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
9 Graph-Cutting for Improving Microarray Gene Expression
Reconstructions 205
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
9.2 Existing techniques . . . . . . . . . . . . . . . . . . . . . . . . 206
9.3 Proposed technique . . . . . . . . . . . . . . . . . . . . . . . . 209
9.3.1 Description . . . . . . . . . . . . . . . . . . . . . . . . 209
9.3.2 Pseudo-code and example . . . . . . . . . . . . . . . . 210
9.4 Experiments and results . . . . . . . . . . . . . . . . . . . . . 211
9.4.1 Dataset characteristics . . . . . . . . . . . . . . . . . . 212
9.4.2 Synthetic data . . . . . . . . . . . . . . . . . . . . . . 212
9.4.3 Real data . . . . . . . . . . . . . . . . . . . . . . . . . 214
9.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
x Table of Contents
10 StochasticDynamicModelingofShortGeneExpressionTime
Series Data 219
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
10.2 Stochastic dynamic model for gene expression data . . . . . . 221
10.3 An EM algorithm for parameter identification . . . . . . . . . 223
10.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 228
10.4.1 Modeling of yeast gene expression time series . . . . . 228
10.4.2 Modeling of virus gene expression time series . . . . . 231
10.4.3 Modelingofhumanmalariaandwormgeneexpression
time series . . . . . . . . . . . . . . . . . . . . . . . . . 234
10.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
10.5.1 Model quality evaluation . . . . . . . . . . . . . . . . . 235
10.5.2 Comparisons with existing modeling methods . . . . . 240
10.6 Conclusions and Future Work . . . . . . . . . . . . . . . . . . 242
11 Conclusions 245
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
11.2 Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
11.2.1 Noise reduction . . . . . . . . . . . . . . . . . . . . . . 247
11.2.2 Gene spot identification . . . . . . . . . . . . . . . . . 249
11.2.3 Gene spot quantification . . . . . . . . . . . . . . . . . 249
11.2.4 Slide and experiment normalization. . . . . . . . . . . 250
11.3 Contributions to microarray biology domain . . . . . . . . . . 250
11.3.1 Technical . . . . . . . . . . . . . . . . . . . . . . . . . 250
11.3.2 Practical. . . . . . . . . . . . . . . . . . . . . . . . . . 251
11.4 Contributions to computer science domain . . . . . . . . . . . 252
11.4.1 Technical . . . . . . . . . . . . . . . . . . . . . . . . . 253
11.4.2 Practical. . . . . . . . . . . . . . . . . . . . . . . . . . 253
11.5 Future researchtopics . . . . . . . . . . . . . . . . . . . . . . 255
11.5.1 Image transformation engine . . . . . . . . . . . . . . 255
11.5.2 Pyramidic contextual clustering . . . . . . . . . . . . . 256
11.5.3 Image layout and image structure . . . . . . . . . . . . 256
11.5.4 Spatial binding . . . . . . . . . . . . . . . . . . . . . . 257
11.5.5 Combining microarrayimage channel data . . . . . . . 257
11.5.6 Other image sets . . . . . . . . . . . . . . . . . . . . . 257
11.5.7 Distributed communication subsystems. . . . . . . . . 258
12 Appendices 259
12.1 Appendix A: Microarray variants . . . . . . . . . . . . . . . . 259
12.1.1 Building the chips . . . . . . . . . . . . . . . . . . . . 259
12.1.2 Digital generation . . . . . . . . . . . . . . . . . . . . 261
12.2 Appendix B: Basic transformations . . . . . . . . . . . . . . . 263
12.2.1 Linear transform generation . . . . . . . . . . . . . . . 263
12.2.2 Square root transform generation . . . . . . . . . . . . 264
12.2.3 Inverse transform generation . . . . . . . . . . . . . . 266
Table of Contents xi
12.2.4 Movement transform generation. . . . . . . . . . . . . 267
12.3 Appendix C: Clustering . . . . . . . . . . . . . . . . . . . . . 268
12.3.1 K-means algorithm . . . . . . . . . . . . . . . . . . . . 272
12.3.2 Fuzzy c-means algorithm. . . . . . . . . . . . . . . . . 273
12.3.3 Hierarchicalclustering . . . . . . . . . . . . . . . . . . 273
12.3.4 Distances . . . . . . . . . . . . . . . . . . . . . . . . . 275
12.4 Appendix D: A glance on mining gene expression data . . . . 275
12.4.1 Data analysis . . . . . . . . . . . . . . . . . . . . . . . 276
12.4.2 New challenges and opportunities . . . . . . . . . . . . 277
12.4.3 Data mining methods for gene expression analysis . . 278
12.5 Appendix E: Autocorrelation and GHT . . . . . . . . . . . . 278
12.5.1 Autocorrelation . . . . . . . . . . . . . . . . . . . . . . 278
12.5.2 Generalized “circular” Hough transform . . . . . . . . 279
References 281
Index 301