Fraser K. Microarray image analysis: an algorithmic approach / K.Fraser, Z.Wang, X.Liu. - Boca Raton: Chapman & Hall/CRC, 2010. - xxiv, 311 p.: ill. - (Computer science and data analysis series). - Ref.: p.281-299. - Ind.: p.301-311. - ISBN 978-1-4200-9153-3  Место хранения:   017 | Институт автоматики и электрометрии CO РАН | Новосибирск | Библиотека

Оглавление / 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 Microarray technology .................................. 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 Microarray analysis .................................... 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 microarray analysis framework overview ....... 47 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 ..................................... 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-world data ................................. 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 ..................................... 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 6.3.2 Recovering weak genes .......................... 142 6.3.3 Strengths and weaknesses ....................... 146 6.4 Evaluation of copasetic microarray analysis 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 10 Stochastic Dynamic Modeling of Short Gene Expression Time 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 Modeling of human malaria and worm gene expression 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 research topics ................................ 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 microarray image 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 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 Hierarchical clustering ........................ 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