Computational Network Analysis with R Applications in Biology, Medicine and Chemistry
, by Dehmer, Matthias; Shi, Yongtang; Emmert-streib, Frank
- ISBN: 9783527339587 | 3527339582
- Cover: Hardcover
- Copyright: 12/12/2016
This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics.
With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping.
Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.
With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping.
Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.
Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology, and University of Coimbra (Portugal). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) and also holds a position at the Universitat der Bundeswehr Munchen. His research interests are in applied mathematics, bioinformatics, systems biology, graph theory, complexity and information theory. He has written over 180 publications in his research areas.
Yongtang Shi studied mathematics at Northwest University (Xi'an, China) and received his Ph.D in applied mathematics from Nankai University (Tianjin, China). He visited Technische Universitat Bergakademie Freiberg (Germany), UMIT (Austria) and Simon Fraser University (Canada). Currently, he is an associate professor at the Center for Combinatorics of Nankai University. His research interests are in graph theory and its applications, especially the applications of graph theory in mathematical chemistry, computer science and information theory. He has written over 40 publications in graph theory and its applications.
Frank Emmert-Streib studied physics at the University of Siegen (Germany) gaining his PhD in theoretical physics from the University of Bremen (Germany). He received postdoctoral training from the Stowers Institute for Medical Research (Kansas City, USA) and the University of Washington (Seattle, USA). Currently, he is associate professor for Computational Biology at Tampere University of Technology (Finland). His main research interests are in the field of computational medicine, network biology and statistical genomics.
Yongtang Shi studied mathematics at Northwest University (Xi'an, China) and received his Ph.D in applied mathematics from Nankai University (Tianjin, China). He visited Technische Universitat Bergakademie Freiberg (Germany), UMIT (Austria) and Simon Fraser University (Canada). Currently, he is an associate professor at the Center for Combinatorics of Nankai University. His research interests are in graph theory and its applications, especially the applications of graph theory in mathematical chemistry, computer science and information theory. He has written over 40 publications in graph theory and its applications.
Frank Emmert-Streib studied physics at the University of Siegen (Germany) gaining his PhD in theoretical physics from the University of Bremen (Germany). He received postdoctoral training from the Stowers Institute for Medical Research (Kansas City, USA) and the University of Washington (Seattle, USA). Currently, he is associate professor for Computational Biology at Tampere University of Technology (Finland). His main research interests are in the field of computational medicine, network biology and statistical genomics.
List of Contributors XV
1 Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks 1
Atsushi Fukushima and Kozo Nishida
1.1 Introduction 1
1.1.1 An Introduction to Omics and Systems Biology 1
1.1.2 Correlation Networks in Omics and Systems Biology 1
1.1.3 Network Modules and Differential Network Approaches 2
1.1.4 Aims of this Chapter 4
1.2 What is DiffCorr? 4
1.2.1 Background 4
1.2.2 Methods 5
1.2.3 Main Functions in DiffCorr 5
1.2.4 Installing the DiffCorr Package 6
1.3 Constructing Co-Expression (Correlation) Networks from Omics Data – Transcriptome Data set 8
1.3.1 Downloading the Transcriptome Data set 8
1.3.2 Data Filtering 9
1.3.3 Calculation of the Correlation and Visualization of Correlation Networks 11
1.3.4 Graph Clustering 15
1.3.5 Gene Ontology Enrichment Analysis 17
1.4 Differential Correlation Analysis by DiffCorr Package 21
1.4.1 Calculation of Differential Co-Expression between Organs in Arabidopsis 21
1.4.2 Exploring the Metabolome Data of Flavonoid-Deficient Arabidopsis 26
1.4.3 Avoiding Pitfalls in (Differential) Correlation Analysis 29
1.5 Conclusion 30
Conflicts of Interest 30
2 Analytical Models and Methods for Anomaly Detection in Dynamic, Attributed Graphs 35
Benjamin A. Miller, Nicholas Arcolano, Stephen Kelley, and Nadya T. Bliss
2.1 Introduction 35
2.2 Chapter Definitions and Notation 36
2.3 Anomaly Detection in Graph Data 37
2.3.1 Neighborhood-Based Techniques 37
2.3.2 Frequent Subgraph Techniques 38
2.3.3 Anomalies in Random Graphs 39
2.4 Random Graph Models 41
2.4.1 Models with Attributes 41
2.4.2 Dynamic Graph Models 43
2.5 Spectral Subgraph Detection in Dynamic, Attributed Graphs 44
2.5.1 Problem Model 44
2.5.2 Filter Optimization 46
2.5.3 Residuals Analysis in Attributed Graphs 47
2.6 Implementation in R 50
2.7 Demonstration in Random Synthetic Backgrounds 51
2.8 Data Analysis Example 55
2.9 Summary 58
3 Bayesian Computational Algorithms for Social Network Analysis 63
Alberto Caimo and Isabella Gollini
3.1 Introduction 63
3.2 Social Networks as Random Graphs 64
3.3 Statistical Modeling Approaches to Social Network Analysis 64
3.3.1 Exponential Random Graph Models (ERGMs) 65
3.3.2 Latent Space Models (LSMs) 65
3.4 Bayesian Inference for Social Network Models 66
3.4.1 R-Based Software Tools 67
3.5 Data 67
3.5.1 Bayesian Inference for Exponential Random Graph Models 68
3.5.2 Bayesian Inference for Latent Space Models 71
3.5.3 Predictive Goodness-of-Fit (GoF) Diagnostics 76
3.6 Conclusions 80
4 Threshold Degradation in R Using iDEMO 83
Chien-Yu Peng and Ya-Shan Cheng
4.1 Introduction 83
4.2 Statistical Overview: Degradation Models 85
4.2.1 Wiener Degradation-Based Process 85
4.2.2 Gamma Degradation-Based Process 88
4.2.3 Inverse Gaussian Degradation-Based Process 89
4.2.3.1 Lifetime Distribution 90
4.2.3.2 Log-Likelihood Function 91
4.2.4 Model Selection Criteria 91
4.2.5 Choice of Λ(t) 91
4.2.6 Threshold Degradation 92
4.3 iDEMO Interface and Functions 92
4.3.1 Overview of the Package iDEMO Functionality 93
4.3.2 Data Input Format 93
4.3.3 Starting the iDEMO 93
4.3.4 Single Degradation Model Analysis 96
4.3.5 Odds and Ends 101
4.3.6 Computational Details 101
4.4 Case Applications 101
4.4.1 Laser Example 102
4.4.2 Fatigue Example 106
4.4.3 ADT Example 112
4.5 Concluding Remarks 122
5 Optimization of Stratified Sampling with the R Package Sampling Strata: Applications to Network Data 125
Marco Ballin and Giulio Barcaroli
5.1 Networks and Stratified Sampling 125
5.2 The R Package Sampling Strata 126
5.2.1 General Setting 126
5.2.2 A General Procedure for the Optimization of Strata in a Frame 130
5.2.3 An Example 132
5.3 Application to Networks 139
5.3.1 Use of Networks as Frames 139
5.3.2 Sampling Massive Networks 145
5.4 Conclusions 149
6 Exploring the Role of Small Molecules in Biological Systems Using Network Approaches 151
Rajarshi Guha and Sourav Das
6.1 The Role of Networks in Drug Discovery 152
6.2 R for Network Analyses 153
6.3 Linking Small Molecules to Targets, Pathways, and Diseases 154
6.3.1 Drug–Target Networks 154
6.3.2 Disease Networks 155
6.3.3 SAR Networks 156
6.3.4 Assay Networks 157
6.3.5 Scaffold Networks 158
6.3.6 Scaffold-Document Networks 159
6.4 R as a Platform for Network Analyses in Drug Discovery 162
6.5 Discussion 165
7 Performing Network Alignments with R 173
Qiang Huang and Ling-Yun Wu
7.1 Introduction 173
7.2 Problems, Models, and Algorithms 175
7.2.1 Problems 176
7.2.2 Models and Algorithms 180
7.2.3 Comparison and Challenges 180
7.3 Algorithms Based on Conditional Random Fields 183
7.3.1 CNetQ for Network Querying 183
7.3.2 CNetA for Pairwise Network Alignment 186
7.3.3 CNetMA for Multiple Network Alignment 189
7.4 Performing Network Alignments with R 193
7.4.1 Installation 193
7.4.2 Usage 193
7.4.3 Examples 195
7.4.4 Web Services and Tool Functions 196
7.5 Discussion 196
8 L1-Penalized Methods in High-Dimensional Gaussian Markov Random Fields 201
Luigi Augugliaro, Angelo M. Mineo, and Ernst C. Wit
8.1 Introduction 201
8.2 Graph Theory: Terminology and Basic Topological Notions 202
8.3 Probabilistic Graphical Models 203
8.4 Markov Random Field 204
8.4.1 Ising Model and Extensions 205
8.4.2 Gaussian Markov Random Fields 206
8.5 Sparse Inference in High-dimensional GMRFs 207
8.5.1 Neighborhood Selection 207
8.5.2 The R Package simone 209
8.5.3 Osteolytic Lesions Data Set: An Analysis by Neighborhood Selection Method 210
8.5.4 Graphical Lasso Estimator 215
8.5.5 The R Package glasso: Computing the Gradient and Coefficient Solution Path on a Simulated Data Set 217
8.5.6 Computational Aspects of the glasso Estimator: the Block-Coordinate Descent Algorithm 223
8.5.7 Faster Computation via Exact Covariance Thresholding 225
8.5.8 Lung Cancer Microarray Data: An Analysis by glasso Estimator 227
8.5.9 The Joint Graphical Lasso 233
8.5.10 Computational Aspects of the jglasso Estimator: ADMM Algorithm 235
8.5.11 The R Package JGL 239
8.5.12 Lung Cancer Microarray Data: An Analysis by jglasso Estimator 241
8.5.13 Structured Graphical Lasso 243
8.5.14 The R Package sglasso 248
8.5.15 Neisseria meningitidis Data Set: An Analysis by fglasso Estimator 250
8.6 Selecting the Optimal Value of the Tuning Parameter 252
8.7 Summary and Conclusion 256
9 Cluster Analysis of Social Networks Using R 267
Malika Charrad
9.1 Introduction 267
9.2 Cluster Analysis in Social Networks 268
9.2.1 Social Network Data 268
9.2.2 Clustering in Social Networks 269
9.3 Cluster Analysis in Social Networks Using R 270
9.3.1 R Packages for Cluster Analysis 270
9.3.2 Data Loading and Formatting 270
9.3.3 Agglomerative Hierarchical Clustering 274
9.3.4 Edge Betweenness Clustering Algorithm 279
9.3.5 Fast Greedy Modularity Optimization Algorithm 281
9.3.6 Walktrap Algorithm 283
9.4 Discussion and Further Readings 285
10 Inference and Analysis of Gene Regulatory Networks in R 289
Ricardo de M. Simoes, Matthias Dehmer, Constantine Mitsiades, and Frank Emmert-Streib
10.1 Introduction 289
10.2 Multiple Myeloma 290
10.3 Installation of Required R Packages from CRAN and Bioconductor 291
10.4 Data Preprocessing 292
10.5 Bc3net Gene Regulatory Network Inference 294
10.6 Retrieving and Generating Gene Sets for a Functional Analysis 297
10.7 Pathway and Other Gene Set Collections 298
10.7.1 Functional Enrichment Analysis of Gene Regulatory Networks 300
10.8 Conclusion 302
11 Visualization of Biological Networks Using NetBioV 307
Shailesh Tripathi, Salissou Moutari, Matthias Dehmer, and Frank Emmert-Streib
11.1 Introduction 307
11.2 Network Visualization 310
11.3 NetBioV 313
11.3.1 Global Network Layouts 313
11.3.2 Modular Network Layout 316
11.3.3 Layered Network (Multiroot) Layout 317
11.3.4 Other Features 318
11.4 Example: Visualization of Networks Using NetBioV 319
11.4.1 Loading Library and Data 320
11.4.2 Global Layout Style 320
11.4.3 Modular Layout Style 322
11.5 Conclusion 325
11.6 Appendix 326
11.6.1 R Code for the Visualization in Figures 11.2 and 11.3 326
11.7 Spiral View 329
11.7.1 Spiral Layout Style in Figure 11.7 329
Index 335
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