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

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