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Computational network analysis with R : applications in biology, medicine, and chemistry / edited by Matthias Dehmer, Yongtang Shi, and Frank Emmert-Streib. -- Weinheim, Germany : Wiley-VCH, [2016]. – (58.17115/C738a) |
Contents
List of Contributors XV
1
Using the DiffCorr Package to Analyze and Visualize Differential
Correlations in Biological Networks 1
1.1
Introduction 1
1.2
What is DiffCorr? 4
1.3
Constructing Co-Expression (Correlation) Networks from Omics Data-
Transcriptome Data set 8
1.4
Differential Correlation Analysis by DiffCorr Package 21
1.5
Conclusion 30
Acknowledgments 30
Conflicts of Interest 30
References 30
2
Analytical Models and Methods for Anomaly Detection in Dynamic,
Attributed Graphs 35
2.1
Introduction 35
2.2
Chapter Definitions and Notation
36
2.3
Anomaly Detection in Graph Data
37
2.4
Random Graph Models 41
2.5
Spectral Subgraph Detection in Dynamic, Attributed Graphs 44
2.6
Implementation in R 50
2.7
Demonstration in Random Synthetic Backgrounds 51
2.8
Data Analysis Example 55
2.9
Summary 58
Acknowledgments 58
References 59
3
Bayesian Computational Algorithms for Social Network Analysis 63
3.1
Introduction 63
3.2
Social Networks as Random Graphs
64
3.3
Statistical Modeling Approaches to Social Network Analysis 64
3.4
Bayesian Inference for Social Network Models 66
3.5
Data 67
3.6
Conclusions 80
References 81
4 Threshold Degradation in R Using
iDEMO 83
4.1
Introduction 83
4.2
Statistical Overview: Degradation Models
85 ;
4.3
iDEMO Interface and Functions 92
4.4
Case Applications 101
4.5
Concluding Remarks 122
References 122
5
Optimization of Stratified Sampling with the R Package SamplingStrata:
Applications to Network Data 125
5.1
Networks and Stratified Sampling
125
5.2
The R Package SamplingStrata 126
5.3
Application to Networks 139
5.4
Conclusions 149
References 149
6
Exploring the Role of Small Molecules in Biological Systems Using
Network Approaches 151
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
6.4
R as a Platform for Network Analyses in Drug Discovery 162
6.5
Discussion 165
Acknowledgments 165
References 166
7
Performing Network Alignments with R
173
7.1
Introduction 173
7.2
Problems, Models, and Algorithms
175
7.3
Algorithms Based on Conditional Random Fields 183
7.4
Performing Network Alignments with R
193
7.5
Discussion 196
References 197
8
l-Penalized Methods in High-Dimensional Gaussian Markov Random Fields
201
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
8.5
Sparse Inference in High-dimensional GMRFs 207
8.6
Selecting the Optimal Value of the Tuning Parameter 252
8.7
Summary and Conclusion 256
References 259
9
Cluster Analysis of Social Networks Using R 267
9.1
Introduction 267
9.2
Cluster Analysis in Social Networks
268
9.3
Cluster Analysis in Social Networks Using R 270
9.4
Discussion and Further Readings
285
References 286
10
Inference and Analysis of Gene Regulatory Networks in R 289
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
10.8
Conclusion 302
References 303
11
Visualization of Biological Networks Using NetBioV 307
11.1
Introduction 307
11.2
Network Visualization 310
11.3
NetBioV 313
11.4
Example: Visualization of Networks Using NetBioV 319
11.5
Conclusion 325
11.6
Appendix 326
11.7
Spiral View 329
References 330
Index
335
