Analysis of microarray data: a network-based approach
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2008
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| 245 | 1 | 0 | |a Analysis of microarray data |b a network-based approach |c ed. by Frank Emmert-Streib ... |
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Datensatz im Suchindex
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I-
Contents
Preface XIII
List of Contributors XVII
1 Introduction to DNA Microarrays 1
Chris Seidel
1.1 Introduction 1
1.1.1 The Genome is an Information Scaffold 2
1.1.2 Gene Expression is Detected by Hybridization 3
1.1.2.1 Hybridization is Used to Measure Gene Expression 4
1.1.2.2 Microarrays Provide a New Twist to an Old Technique 5
1.2 Types of Arrays 5
1.2.1 Spotted Microarrays 6
1.2.2 Affymetrix GeneChips 6
1.2.2.1 Other In Situ Synthesis Platforms 7
1.2.2.2 Uses of Microarrays 8
1.3 Array Content 11
1.3.1 ESTs Are the First View 11
1.3.1.1 Probe Design 12
1.4 Normalization and Scaling 14
1.4.1 Be Unbiased, Be Complete 18
1.4.2 Sequence Counts 18
References 19
2 Comparative Analysis of Clustering Methods for Microarray Data 27
Dongxiao Zhu, Mary-Lee Dequeiant, and Hua Li
2.1 Introduction 27
2.2 Measuring Distance Between Genes or Clusters 28
2.3 Network Models 34
2.3.1 Boolean Network 34
2.3.2 Coexpression Network 34
2.3.3 Bayesian Network 36
Analysis of Microarray Data: A Network-Based Approach. Edited by F. Emmert-Streib and M. Dehmer
Copyright © 2008 WILEY-VCH Verlag GmbH Co. KGaA, Weinheim
ISBN: 978-3-527-31822-3
VI Contents
2.3.4 Co-Occurrence Network 37
2.4 Network Constrained Clustering Method 38
2.4.1 Extract the Giant Connected Component 38
2.4.2 Compute "Network Constrained Distance Matrix" 39
2.5 Network Constrained Clustering Results 39
2.5.1 Yeast Galactose Metabolism Pathway 40
2.5.2 Retinal Gene Expression Data 43
2.5.3 Mouse Segmentation Clock Data 46
2.6 Discussion and Conclusion 47
References 48
3 Finding Verified Edges in Genetic/Gene Networks:
Bilayer Verification for Network Recovery in the Presence
of H idden Confou nders 51
Jason E. Aten
3.1 Introduction: Gene and Genetic Networks 51
3.2 Background and Prior Theory 53
3.2.1 Motivation 53
3.2.2 Bayesian Networks Theory 53
3.2.2.1 d-Separation at Colliders 55
3.2.2.2 Placing Genetic Tests Within the Bayesian Network Framework 56
3.2.3 Learning Network Structure from Observed Conditional
Independencies 58
3.2.4 Prior Work: The PC Algorithm 58
3.2.4.1 PC Algorithm 58
3.2.5 Prior Work: The Local Causal Discovery Algorithm 59
3.2.5.1 LCD Algorithm 60
3.3 New Theory 61
3.3.1 Novel Algorithm: The RVL Algorithm for Learning DAGs Efficiently 61
3.3.1.1 Algorithm: Recursive v-Structure Location 61
3.3.2 Novel Theory: Bilayer Verification and the RW Algorithm for
Verifying Graphs in the Presence of Unobserved Confounders 62
3.3.2.1 Algorithm: Recursive v-Structures with Verification 67
3.4 Methods 68
3.4.1 C3H/HeJ x C57BL/6J Microarray Data 68
3.4.2 Parameters and the v-Structure Test 68
3.4.2.1 Mechanics of the v-Structure Test 69
3.4.3 Special Handling for Genotypes 70
3.5 Results and Further Application 70
3.5.1 Estimating a False-Positive Rates for the v-Structure Test 70
3.5.2 Learning an Aortic Lesion Network 77
3.5.3 Further Utilizing Networks: Assigning Functional Roles to Genes 77
3.5.4 Future Work 79
References 80
Contents VII
4 Computational Inference of Biological Causal Networks -
Analysis of Therapeutic Compound Effects 83
Jing Yu, Gabriel Helmlinger, Muriel Saulnier, and Anna Ceorgieva
4.1 Introduction 83
4.2 Basic Theory of Bayesian Networks 84
4.2.1 Bayesian Scoring Metrics 86
4.2.2 Heuristic Search Methods 87
4.2.3 Inference Score 88
4.3 Methods 88
4.3.1 Experimental Design 88
4.3.2 Tissue Contamination 88
4.3.3 Gene list Prefiltering 89
4.3.4 Outlier Removal 89
4.3.5 Further Screening of the Gene list 90
4.3.6 Data Pair-Up for Using DBN 91
4.3.7 Applying DBN 92
4.4 Results 93
4.4.1 Computational Results 93
4.4.2 Biological Findings 93
4.5 Discussion and Conclusions 96
References 97
5 Reverse Engineering Gene Regulatory Networks
with Various Machine Learning Methods 101
Marco Crzegorczyk, Dirk Husmeier, and Adriano V. Werhli
5.1 Introduction 101
5.2 Methods 102
5.2.1 Relevance Networks 103
5.2.2 Gaussian Graphical Models 104
5.2.3 Bayesian Networks 106
5.2.3.1 Introduction to Bayesian Networks 106
5.2.3.2 Learning Causal Relationships 108
5.2.3.3 Bayesian Network Scoring Metrics 109
5.2.3.4 The Gaussian BGe Scoring Metric 110
5.2.3.5 Structure Learning Via MCMC Simulations 113
5.2.3.6 Learning Bayesian Networks from Interventional Data 118
5.3 The RAF Signalling Pathway 120
5.4 Criteria for Evaluating Learning Performances 122
5.5 Data 125
5.6 Simulations 128
5.7 Results 129
5.8 Discussion 131
5.9 Conclusion 140
References 140
VIII Contents
6 Statistical Methods for Inference of Genetic Networks and
Regulatory Modules 143
Hongzhe Li
6.1 Introduction 143
6.2 Network Inference Based on Gaussian Graphical Models 145
6.2.1 Gaussian Graphical Models 146
6.2.2 Threshold Gradient Descent Regularizarion 346
6.2.3 Model Selection by Cross-Validation and Bootstrap 148
6.2.4 Simulation Results and Application to Real Data Set 149
6.3 Methods for Identifying Regulatory Modules 151
6.3.1 The SRMM for Identifying Transcriptional Modules 151
6.3.2 An EM Algorithm Based on Lasso 152
6.3.3 Selection of the Number of Modules K and the Tuning
Parameter s 153
6.3.4 Application to Yeast Stress Data Set 154
6.4 Inference of Transcriptional Networks 155
6.4.1 Functional Response Model with Time-Varying Coefficients
for MTC Gene Expression Data 156
6.4.2 Estimation Using B-Splines 157
6.4.3 A Group SCAD Penalization Procedure 157
6.4.4 Numerical Algorithm, Properties, and Application 158
6.5 Discussion, Conclusions, and Future Research 160
6.5.1 Incorporating Network Information into Analysis of Microarray
Gene Expression Data 160
6.5.2 Development of Statistical and Computational Methods for
Integrating Gene Expression Data and Epigenomic Data 163
6.5.3 Final Remarks 163
References 164
7 A Model of Genetic Networks with Delayed Stochastic Dynamics 169
Andre S. Ribeiro
7.1 Introduction 169
7.2 Experimental Observations of Gene Expression 171
7.2.1 The Stochastic Nature of Gene Expression 172
7.2.2 Time Delays in Transcription and Translation 173
7.3 The Delayed Stochastic Simulation Algorithm 176
7.3.1 Stochastic Simulation Algorithm 176
7.3.2 The Delayed Stochastic Simulation Algorithm 178
7.4 Modeling Gene Expression as a Multiple Time-Delayed
Stochastic Event 179
7.5 A Gene Regulatory Network Model 180
7.6 Applications 186
7.6.1 Modeling Single Gene Expression 186
7.6.2 Bistability of a Toggle Switch as a Result of Time Delays
in Transcription 190
Contents IX
7.7 A Model of the P53-Mdm2 Feedback Loop Network 194
7.8 Summary, Conclusions, and Applications 200
References 201
8 Probabilistic Boolean Networks as Models for Gene Regulation 205
Yufei Huang and Edward R. Dougherty
8.1 Introduction 205
8.2 Modeling Gene Regulation with Probabilistic Boolean Networks 207
8.2.1 Preliminaries 207
8.2.2 Probabilistic Boolean Networks 210
8.2.2.1 Context-Sensitive PBNs and PBNs with Random Perturbation 213
8.3 Reverse Engineering Regulatory Networks with PBN-Based Microarray
Expression Data 215
8.3.1 A Disjoint Bayesian Solution of Constructing Probabilistic
Boolean Networks 216
8.3.1.1 Experimental Results 218
8.3.2 A Full Bayesian Solution 219
8.3.2.1 Melanoma Application 220
8.4 Optimal Control of Context-Sensitive PBN 221
8.4.1 Introduction to Network Intervention 221
8.4.2 Defining the Transition Probability of a Context-Sensitive PBN 223
8.4.3 External Intervention with Finite-Horizon Control 224
8.4.3.1 Melanoma Application 227
8.4.4 External Intervention with Infinite-Horizon Control 228
8.4.4.1 The Discounted Approach 230
8.4.4.2 The Average-Cost-Per-Stage Approach 233
8.4.5 Melanoma Application 235
References 240
9 Structural Equation for Identification of Genetic Networks 243
Momiao Xiong
9.1 Introduction 243
9.2 Models 245
9.3 Covariance Matrix 249
9.4 Estimation 250
9.4.1 Likelihood Function 250
9.4.2 Maximum Likelihood Estimators 251
9.4.3 Asymptotic Properties of the Maximum Likelihood Estimators
and Test Statistics 254
9.4.4 Two-Stage Least Square Method 255
9.4.4.1 Reduce Form 256
9.4.4.2 Two-Stage Least Squares Estimation 256
9.4.4.3 Unweighted Least Squares (ULS) 257
9.4.4.4 Generalized Least Squares (GLS) 257
9.5 Model Selection 258
X I Contents
9.5.1 Model Selection Criterion 258
9.5.2 Genetic Algorithms (GAs) 259
9.5.3 Illustration of Structural Equations for Modeling Genetic
Networks 260
9.6 Identification of Differentially Expressed Genetic Networks 267
9.6.1 The Generalized T2 Statistic for Testing the Differential Expression
of Genetic Networks 267
9.6.2 Nonlinear Tests for Identifying Differentially Expressed Genetic
Networks 268
9.6.3 Examples 269
9.7 Differentially Regulated Genetic Networks 272
9.7.1 Index for Measuring Difference in Regulation of Genetic
Networks 272
9.7.2 Examples 274
9.8 Conclusions 279
References 280
10 Detecting Pathological Pathways of a Complex Disease by a
Comparative Analysis of Networks 285
Frank Emmert-Streib and Matthias Dehmer
10.1 Introduction 285
10.2 Outline of Our Method 287
10.3 Detecting Pathological Pathways 288
10.3.1 Pathways as Undirected Dependency Graphs 289
10.4 Measuring the Similarity of Pathways 292
10.4.1 GED for Graphs With Unique Vertex Labels 295
10.4.2 Statistical Significance of the GED 298
10.5 Results for the Chronic Fatigue Syndrome 298
10.5.1 Influence of Measurement Errors 300
10.6 Discussions and Conclusions 302
References 303
11 Predicting Functional Modules Using Microarray and Protein
Interaction Data 307
Yu Chen and Dong Xu
11.1 Introduction 307
11.2 Materials and Methods 309
11.2.1 Data sets 309
11.2.2 Protein Function Annotation and GO Index 310
11.2.3 Construction of Probabilistic Functional Network 310
11.2.4 Identification of Functional Modules by Clustering the Network 311
11.2.5 Evaluation of Topological and Functional Properties of Modules 312
11.3 Results 314
11.3.1 Modules Discovered from the Probabilistic Functional Network 314
11.3.2 Evaluation of Modules 316
Contents I XI
11.3.3 Module Organization in Yeast Gene Interaction Network 320
11.4 Discussion 324
References 326
12 Computational Reconstruction of Transcriptional Regulatory Modules
of the Yeast Cell Cycle 331
Wei-Sheng Wu, Wen-Hsiung Li, and Bor-Sen Chen
12.1 Introduction 331
12.2 Methods 332
12.2.1 Data Sets 332
12.2.2 Temporal Relationship Identification Algorithm 333
12.2.3 The Module Finding Algorithm 334
12.3 Results 337
12.3.1 Validation of the Identified Modules 337
12.3.2 Identification of Important Cell Cycle TFs and Their
Combinations 338
12.3.3 The M/Gl Phase 338
12.3A The Gl Phase 341
12.3.5 The S Phase 342
12.3.6 The SG2 and G2/M Phases 342
12.4 Discussion 343
12 A.I Relationships Between Two TFs of a Module 343
12.4.2 Advantages of MOFA 343
12.4.3 Parameter Settings of MOFA 345
12.4.4 Refining Clusters from Spellman el al. 346
12.5 Conclusions 347
References 350
13 Pathway-Based Methods for Analyzing Microarray Data 355
Herbert Pang, Inyoung Kim, and Hongyu Zhao
13.1 Introduction 355
13.2 Methods 356
13.2.1 Random Forests Based Approach 356
13.2.1.1 Random Forests Classification 356
13.2.1.2 Random Forests Regression 358
13.2.2 Regression Model Based Approach 359
13.2.2.1 Bayesian Hierarchical Model 359
13.2.2.2 A Bayesian MCMC Approach 359
13.3 Real Data Analysis 360
13.3.1 Pathways and Gene Sets 361
13.3.2 Data Analysis Using Random Forests 362
13.3.2.1 Canine Data Set 361
13.3.2.2 Breast Cancer Data Set 371
13.3.2.3 Diabetes Data Set 372
13.3.2.4 Comparison with Other Machine Learning Approaches 373
XII Contents
13.3.3 Data Analysis Using Bayesian Approach 374
13.4 Conclusions and Discussion 378
References 380
14 The Most Probable Genetic Interaction Networks Inferred from
Gene Expression Patterns 385
Timothy R. Lezon, Jayanth R. Banavar, Marek Cieplak,
Nina V. Fedorqff, and Amos Maritan
14.1 Introduction 385
14.2 Entropy Maximization 386
14.3 Recovering the Data 391
14A Integrating Over Interactions 393
14.5 Higher Order Interactions 395
14.6 Network Analysis 398
14.6.1 Metabolic Oscillations in Yeast 398
14.6.2 Polishing of the Data and Selection of Subsets of Genes 399
14.6.3 The Nature of the Network 400
14.6.4 The Biological Interpretation of the Network 404
14.6.5 The Larger Subset of Genes 407
14.6.6 Metabolic Oscillations with Longer Periods 407
14.6.7 Three-Gene Interactions 409
14.7 Conclusion 409
References 410
Index 413 |
| adam_txt |
I-
Contents
Preface XIII
List of Contributors XVII
1 Introduction to DNA Microarrays 1
Chris Seidel
1.1 Introduction 1
1.1.1 The Genome is an Information Scaffold 2
1.1.2 Gene Expression is Detected by Hybridization 3
1.1.2.1 Hybridization is Used to Measure Gene Expression 4
1.1.2.2 Microarrays Provide a New Twist to an Old Technique 5
1.2 Types of Arrays 5
1.2.1 Spotted Microarrays 6
1.2.2 Affymetrix GeneChips 6
1.2.2.1 Other In Situ Synthesis Platforms 7
1.2.2.2 Uses of Microarrays 8
1.3 Array Content 11
1.3.1 ESTs Are the First View 11
1.3.1.1 Probe Design 12
1.4 Normalization and Scaling 14
1.4.1 Be Unbiased, Be Complete 18
1.4.2 Sequence Counts 18
References 19
2 Comparative Analysis of Clustering Methods for Microarray Data 27
Dongxiao Zhu, Mary-Lee Dequeiant, and Hua Li
2.1 Introduction 27
2.2 Measuring Distance Between Genes or Clusters 28
2.3 Network Models 34
2.3.1 Boolean Network 34
2.3.2 Coexpression Network 34
2.3.3 Bayesian Network 36
Analysis of Microarray Data: A Network-Based Approach. Edited by F. Emmert-Streib and M. Dehmer
Copyright © 2008 WILEY-VCH Verlag GmbH Co. KGaA, Weinheim
ISBN: 978-3-527-31822-3
VI Contents
2.3.4 Co-Occurrence Network 37
2.4 Network Constrained Clustering Method 38
2.4.1 Extract the Giant Connected Component 38
2.4.2 Compute "Network Constrained Distance Matrix" 39
2.5 Network Constrained Clustering Results 39
2.5.1 Yeast Galactose Metabolism Pathway 40
2.5.2 Retinal Gene Expression Data 43
2.5.3 Mouse Segmentation Clock Data 46
2.6 Discussion and Conclusion 47
References 48
3 Finding Verified Edges in Genetic/Gene Networks:
Bilayer Verification for Network Recovery in the Presence
of H idden Confou nders 51
Jason E. Aten
3.1 Introduction: Gene and Genetic Networks 51
3.2 Background and Prior Theory 53
3.2.1 Motivation 53
3.2.2 Bayesian Networks Theory 53
3.2.2.1 d-Separation at Colliders 55
3.2.2.2 Placing Genetic Tests Within the Bayesian Network Framework 56
3.2.3 Learning Network Structure from Observed Conditional
Independencies 58
3.2.4 Prior Work: The PC Algorithm 58
3.2.4.1 PC Algorithm 58
3.2.5 Prior Work: The Local Causal Discovery Algorithm 59
3.2.5.1 LCD Algorithm 60
3.3 New Theory 61
3.3.1 Novel Algorithm: The RVL Algorithm for Learning DAGs Efficiently 61
3.3.1.1 Algorithm: Recursive v-Structure Location 61
3.3.2 Novel Theory: Bilayer Verification and the RW Algorithm for
Verifying Graphs in the Presence of Unobserved Confounders 62
3.3.2.1 Algorithm: Recursive v-Structures with Verification 67
3.4 Methods 68
3.4.1 C3H/HeJ x C57BL/6J Microarray Data 68
3.4.2 Parameters and the v-Structure Test 68
3.4.2.1 Mechanics of the v-Structure Test 69
3.4.3 Special Handling for Genotypes 70
3.5 Results and Further Application 70
3.5.1 Estimating a False-Positive Rates for the v-Structure Test 70
3.5.2 Learning an Aortic Lesion Network 77
3.5.3 Further Utilizing Networks: Assigning Functional Roles to Genes 77
3.5.4 Future Work 79
References 80
Contents VII
4 Computational Inference of Biological Causal Networks -
Analysis of Therapeutic Compound Effects 83
Jing Yu, Gabriel Helmlinger, Muriel Saulnier, and Anna Ceorgieva
4.1 Introduction 83
4.2 Basic Theory of Bayesian Networks 84
4.2.1 Bayesian Scoring Metrics 86
4.2.2 Heuristic Search Methods 87
4.2.3 Inference Score 88
4.3 Methods 88
4.3.1 Experimental Design 88
4.3.2 Tissue Contamination 88
4.3.3 Gene list Prefiltering 89
4.3.4 Outlier Removal 89
4.3.5 Further Screening of the Gene list 90
4.3.6 Data Pair-Up for Using DBN 91
4.3.7 Applying DBN 92
4.4 Results 93
4.4.1 Computational Results 93
4.4.2 Biological Findings 93
4.5 Discussion and Conclusions 96
References 97
5 Reverse Engineering Gene Regulatory Networks
with Various Machine Learning Methods 101
Marco Crzegorczyk, Dirk Husmeier, and Adriano V. Werhli
5.1 Introduction 101
5.2 Methods 102
5.2.1 Relevance Networks 103
5.2.2 Gaussian Graphical Models 104
5.2.3 Bayesian Networks 106
5.2.3.1 Introduction to Bayesian Networks 106
5.2.3.2 Learning Causal Relationships 108
5.2.3.3 Bayesian Network Scoring Metrics 109
5.2.3.4 The Gaussian BGe Scoring Metric 110
5.2.3.5 Structure Learning Via MCMC Simulations 113
5.2.3.6 Learning Bayesian Networks from Interventional Data 118
5.3 The RAF Signalling Pathway 120
5.4 Criteria for Evaluating Learning Performances 122
5.5 Data 125
5.6 Simulations 128
5.7 Results 129
5.8 Discussion 131
5.9 Conclusion 140
References 140
VIII Contents
6 Statistical Methods for Inference of Genetic Networks and
Regulatory Modules 143
Hongzhe Li
6.1 Introduction 143
6.2 Network Inference Based on Gaussian Graphical Models 145
6.2.1 Gaussian Graphical Models 146
6.2.2 Threshold Gradient Descent Regularizarion 346
6.2.3 Model Selection by Cross-Validation and Bootstrap 148
6.2.4 Simulation Results and Application to Real Data Set 149
6.3 Methods for Identifying Regulatory Modules 151
6.3.1 The SRMM for Identifying Transcriptional Modules 151
6.3.2 An EM Algorithm Based on Lasso 152
6.3.3 Selection of the Number of Modules K and the Tuning
Parameter s 153
6.3.4 Application to Yeast Stress Data Set 154
6.4 Inference of Transcriptional Networks 155
6.4.1 Functional Response Model with Time-Varying Coefficients
for MTC Gene Expression Data 156
6.4.2 Estimation Using B-Splines 157
6.4.3 A Group SCAD Penalization Procedure 157
6.4.4 Numerical Algorithm, Properties, and Application 158
6.5 Discussion, Conclusions, and Future Research 160
6.5.1 Incorporating Network Information into Analysis of Microarray
Gene Expression Data 160
6.5.2 Development of Statistical and Computational Methods for
Integrating Gene Expression Data and Epigenomic Data 163
6.5.3 Final Remarks 163
References 164
7 A Model of Genetic Networks with Delayed Stochastic Dynamics 169
Andre S. Ribeiro
7.1 Introduction 169
7.2 Experimental Observations of Gene Expression 171
7.2.1 The Stochastic Nature of Gene Expression 172
7.2.2 Time Delays in Transcription and Translation 173
7.3 The Delayed Stochastic Simulation Algorithm 176
7.3.1 Stochastic Simulation Algorithm 176
7.3.2 The Delayed Stochastic Simulation Algorithm 178
7.4 Modeling Gene Expression as a Multiple Time-Delayed
Stochastic Event 179
7.5 A Gene Regulatory Network Model 180
7.6 Applications 186
7.6.1 Modeling Single Gene Expression 186
7.6.2 Bistability of a Toggle Switch as a Result of Time Delays
in Transcription 190
Contents IX
7.7 A Model of the P53-Mdm2 Feedback Loop Network 194
7.8 Summary, Conclusions, and Applications 200
References 201
8 Probabilistic Boolean Networks as Models for Gene Regulation 205
Yufei Huang and Edward R. Dougherty
8.1 Introduction 205
8.2 Modeling Gene Regulation with Probabilistic Boolean Networks 207
8.2.1 Preliminaries 207
8.2.2 Probabilistic Boolean Networks 210
8.2.2.1 Context-Sensitive PBNs and PBNs with Random Perturbation 213
8.3 Reverse Engineering Regulatory Networks with PBN-Based Microarray
Expression Data 215
8.3.1 A Disjoint Bayesian Solution of Constructing Probabilistic
Boolean Networks 216
8.3.1.1 Experimental Results 218
8.3.2 A Full Bayesian Solution 219
8.3.2.1 Melanoma Application 220
8.4 Optimal Control of Context-Sensitive PBN 221
8.4.1 Introduction to Network Intervention 221
8.4.2 Defining the Transition Probability of a Context-Sensitive PBN 223
8.4.3 External Intervention with Finite-Horizon Control 224
8.4.3.1 Melanoma Application 227
8.4.4 External Intervention with Infinite-Horizon Control 228
8.4.4.1 The Discounted Approach 230
8.4.4.2 The Average-Cost-Per-Stage Approach 233
8.4.5 Melanoma Application 235
References 240
9 Structural Equation for Identification of Genetic Networks 243
Momiao Xiong
9.1 Introduction 243
9.2 Models 245
9.3 Covariance Matrix 249
9.4 Estimation 250
9.4.1 Likelihood Function 250
9.4.2 Maximum Likelihood Estimators 251
9.4.3 Asymptotic Properties of the Maximum Likelihood Estimators
and Test Statistics 254
9.4.4 Two-Stage Least Square Method 255
9.4.4.1 Reduce Form 256
9.4.4.2 Two-Stage Least Squares Estimation 256
9.4.4.3 Unweighted Least Squares (ULS) 257
9.4.4.4 Generalized Least Squares (GLS) 257
9.5 Model Selection 258
X I Contents
9.5.1 Model Selection Criterion 258
9.5.2 Genetic Algorithms (GAs) 259
9.5.3 Illustration of Structural Equations for Modeling Genetic
Networks 260
9.6 Identification of Differentially Expressed Genetic Networks 267
9.6.1 The Generalized T2 Statistic for Testing the Differential Expression
of Genetic Networks 267
9.6.2 Nonlinear Tests for Identifying Differentially Expressed Genetic
Networks 268
9.6.3 Examples 269
9.7 Differentially Regulated Genetic Networks 272
9.7.1 Index for Measuring Difference in Regulation of Genetic
Networks 272
9.7.2 Examples 274
9.8 Conclusions 279
References 280
10 Detecting Pathological Pathways of a Complex Disease by a
Comparative Analysis of Networks 285
Frank Emmert-Streib and Matthias Dehmer
10.1 Introduction 285
10.2 Outline of Our Method 287
10.3 Detecting Pathological Pathways 288
10.3.1 Pathways as Undirected Dependency Graphs 289
10.4 Measuring the Similarity of Pathways 292
10.4.1 GED for Graphs With Unique Vertex Labels 295
10.4.2 Statistical Significance of the GED 298
10.5 Results for the Chronic Fatigue Syndrome 298
10.5.1 Influence of Measurement Errors 300
10.6 Discussions and Conclusions 302
References 303
11 Predicting Functional Modules Using Microarray and Protein
Interaction Data 307
Yu Chen and Dong Xu
11.1 Introduction 307
11.2 Materials and Methods 309
11.2.1 Data sets 309
11.2.2 Protein Function Annotation and GO Index 310
11.2.3 Construction of Probabilistic Functional Network 310
11.2.4 Identification of Functional Modules by Clustering the Network 311
11.2.5 Evaluation of Topological and Functional Properties of Modules 312
11.3 Results 314
11.3.1 Modules Discovered from the Probabilistic Functional Network 314
11.3.2 Evaluation of Modules 316
Contents I XI
11.3.3 Module Organization in Yeast Gene Interaction Network 320
11.4 Discussion 324
References 326
12 Computational Reconstruction of Transcriptional Regulatory Modules
of the Yeast Cell Cycle 331
Wei-Sheng Wu, Wen-Hsiung Li, and Bor-Sen Chen
12.1 Introduction 331
12.2 Methods 332
12.2.1 Data Sets 332
12.2.2 Temporal Relationship Identification Algorithm 333
12.2.3 The Module Finding Algorithm 334
12.3 Results 337
12.3.1 Validation of the Identified Modules 337
12.3.2 Identification of Important Cell Cycle TFs and Their
Combinations 338
12.3.3 The M/Gl Phase 338
12.3A The Gl Phase 341
12.3.5 The S Phase 342
12.3.6 The SG2 and G2/M Phases 342
12.4 Discussion 343
12 A.I Relationships Between Two TFs of a Module 343
12.4.2 Advantages of MOFA 343
12.4.3 Parameter Settings of MOFA 345
12.4.4 Refining Clusters from Spellman el al. 346
12.5 Conclusions 347
References 350
13 Pathway-Based Methods for Analyzing Microarray Data 355
Herbert Pang, Inyoung Kim, and Hongyu Zhao
13.1 Introduction 355
13.2 Methods 356
13.2.1 Random Forests Based Approach 356
13.2.1.1 Random Forests Classification 356
13.2.1.2 Random Forests Regression 358
13.2.2 Regression Model Based Approach 359
13.2.2.1 Bayesian Hierarchical Model 359
13.2.2.2 A Bayesian MCMC Approach 359
13.3 Real Data Analysis 360
13.3.1 Pathways and Gene Sets 361
13.3.2 Data Analysis Using Random Forests 362
13.3.2.1 Canine Data Set 361
13.3.2.2 Breast Cancer Data Set 371
13.3.2.3 Diabetes Data Set 372
13.3.2.4 Comparison with Other Machine Learning Approaches 373
XII Contents
13.3.3 Data Analysis Using Bayesian Approach 374
13.4 Conclusions and Discussion 378
References 380
14 The Most Probable Genetic Interaction Networks Inferred from
Gene Expression Patterns 385
Timothy R. Lezon, Jayanth R. Banavar, Marek Cieplak,
Nina V. Fedorqff, and Amos Maritan
14.1 Introduction 385
14.2 Entropy Maximization 386
14.3 Recovering the Data 391
14A Integrating Over Interactions 393
14.5 Higher Order Interactions 395
14.6 Network Analysis 398
14.6.1 Metabolic Oscillations in Yeast 398
14.6.2 Polishing of the Data and Selection of Subsets of Genes 399
14.6.3 The Nature of the Network 400
14.6.4 The Biological Interpretation of the Network 404
14.6.5 The Larger Subset of Genes 407
14.6.6 Metabolic Oscillations with Longer Periods 407
14.6.7 Three-Gene Interactions 409
14.7 Conclusion 409
References 410
Index 413 |
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| discipline | Chemie / Pharmazie Biologie |
| discipline_str_mv | Chemie / Pharmazie Biologie |
| format | Book |
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| index_date | 2024-07-02T20:49:25Z |
| indexdate | 2024-07-20T09:40:05Z |
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| isbn | 9783527318223 |
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| subject_GND | (DE-588)4123037-1 (DE-588)4544227-7 |
| title | Analysis of microarray data a network-based approach |
| title_auth | Analysis of microarray data a network-based approach |
| title_exact_search | Analysis of microarray data a network-based approach |
| title_exact_search_txtP | Analysis of microarray data a network-based approach |
| title_full | Analysis of microarray data a network-based approach ed. by Frank Emmert-Streib ... |
| title_fullStr | Analysis of microarray data a network-based approach ed. by Frank Emmert-Streib ... |
| title_full_unstemmed | Analysis of microarray data a network-based approach ed. by Frank Emmert-Streib ... |
| title_short | Analysis of microarray data |
| title_sort | analysis of microarray data a network based approach |
| title_sub | a network-based approach |
| topic | Puces à ADN DNA microarrays Oligonucleotide Array Sequence Analysis Datenanalyse (DE-588)4123037-1 gnd Microarray (DE-588)4544227-7 gnd |
| topic_facet | Puces à ADN DNA microarrays Oligonucleotide Array Sequence Analysis Datenanalyse Microarray |
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