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.
Autorentext
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.
Inhalt
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 (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 Opt...