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E-raamat: Computational Network Analysis with R: Applications in Biology, Medicine and Chemistry

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Applications in Biology Medicine and Chemistry. The ultimate reference for R in Network Analysis.

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.
List of Contributors xv
1 Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks 1(34)
Atsushi Fukushima
Kozo Nishida
1.1 Introduction
1(3)
1.1.1 An Introduction to Omics and Systems Biology
1(1)
1.1.2 Correlation Networks in Omics and Systems Biology
1(1)
1.1.3 Network Modules and Differential Network Approaches
2(2)
1.1.4 Aims of this
Chapter
4(1)
1.2 What is DiffCorr?
4(4)
1.2.1 Background
4(1)
1.2.2 Methods
5(1)
1.2.3 Main Functions in DiffCorr
5(1)
1.2.4 Installing the DiffCorr Package
6(2)
1.3 Constructing Co-Expression (Correlation) Networks from Omics Data - Transcriptome Data set
8(13)
1.3.1 Downloading the Transcriptome Data set
8(1)
1.3.2 Data Filtering
9(2)
1.3.3 Calculation of the Correlation and Visualization of Correlation Networks
11(4)
1.3.4 Graph Clustering
15(2)
1.3.5 Gene Ontology Enrichment Analysis
17(4)
1.4 Differential Correlation Analysis by DiffCorr Package
21(9)
1.4.1 Calculation of Differential Co-Expression between Organs in Arabidopsis
21(5)
1.4.2 Exploring the Metabolome Data of Flavonoid-Deficient Arabidopsis
26(3)
1.4.3 Avoiding Pitfalls in (Differential) Correlation Analysis
29(1)
1.5 Conclusion
30(1)
Acknowledgments
30(1)
Conflicts of Interest
30(1)
References
30(5)
2 Analytical Models and Methods for Anomaly Detection in Dynamic, Attributed Graphs 35(28)
Benjamin A. Miller
Nicholas Arcolano
Stephen Kelley
Nadya T. Bliss
2.1 Introduction
35(1)
2.2
Chapter Definitions and Notation
36(1)
2.3 Anomaly Detection in Graph Data
37(4)
2.3.1 Neighborhood-Based Techniques
37(1)
2.3.2 Frequent Subgraph Techniques
38(1)
2.3.3 Anomalies in Random Graphs
39(2)
2.4 Random Graph Models
41(3)
2.4.1 Models with Attributes
41(2)
2.4.2 Dynamic Graph Models
43(1)
2.5 Spectral Subgraph Detection in Dynamic, Attributed Graphs
44(6)
2.5.1 Problem Model
44(2)
2.5.2 Filter Optimization
46(1)
2.5.3 Residuals Analysis in Attributed Graphs
47(3)
2.6 Implementation in R
50(1)
2.7 Demonstration in Random Synthetic Backgrounds
51(4)
2.8 Data Analysis Example
55(3)
2.9 Summary
58(1)
Acknowledgments
58(1)
References
59(4)
3 Bayesian Computational Algorithms for Social Network Analysis 63(20)
Alberto Caimo
Isabella Gollini
3.1 Introduction
63(1)
3.2 Social Networks as Random Graphs
64(1)
3.3 Statistical Modeling Approaches to Social Network Analysis
64(2)
3.3.1 Exponential Random Graph Models (ERGMs)
65(1)
3.3.2 Latent Space Models (LSMs)
65(1)
3.4 Bayesian Inference for Social Network Models
66(1)
3.4.1 R-Based Software Tools
67(1)
3.5 Data
67(13)
3.5.1 Bayesian Inference for Exponential Random Graph Models
68(3)
3.5.2 Bayesian Inference for Latent Space Models
71(5)
3.5.3 Predictive Goodness-of-Fit (GoF) Diagnostics
76(4)
3.6 Conclusions
80(1)
References
81(2)
4 Threshold Degradation in R Using iDEMO 83(42)
Chien-Yu Peng
Ya-Shan Cheng
4.1 Introduction
83(2)
4.2 Statistical Overview: Degradation Models
85(7)
4.2.1 Wiener Degradation-Based Process
85(3)
4.2.1.1 Lifetime Information
86(1)
4.2.1.2 Log-Likelihood Function
87(1)
4.2.2 Gamma Degradation-Based Process
88(1)
4.2.2.1 Lifetime Information
88(1)
4.2.2.2 Log-Likelihood Function
89(1)
4.2.3 Inverse Gaussian Degradation-Based Process
89(2)
4.2.3.1 Lifetime Distribution
90(1)
4.2.3.2 Log-Likelihood Function
91(1)
4.2.4 Model Selection Criteria
91(1)
4.2.5 Choice of ^(t)
91(1)
4.2.6 Threshold Degradation
92(1)
4.3 iDEMO Interface and Functions
92(9)
4.3.1 Overview of the Package iDEMO Functionality
93(1)
4.3.2 Data Input Format
93(1)
4.3.3 Starting the iDEMO
93(3)
4.3.3.1 Import Data
94(1)
4.3.3.2 Basic Information
95(1)
4.3.3.3 Degradation Model Selection
96(1)
4.3.4 Single Degradation Model Analysis
96(5)
4.3.4.1 Parameter Estimation
97(1)
4.3.4.2 Lifetime Information
98(3)
4.3.5 Odds and Ends
101(1)
4.3.6 Computational Details
101(1)
4.4 Case Applications
101(21)
4.4.1 Laser Example
102(4)
4.4.2 Fatigue Example
106(6)
4.4.3 ADT Example
112(10)
4.5 Concluding Remarks
122(1)
References
122(3)
5 Optimization of Stratified Sampling with the R Package SamplingStrata: Applications to Network Data 125(26)
Marco Ballin
Giulio Barcaroli
5.1 Networks and Stratified Sampling
125(1)
5.2 The R Package SamplingStrata
126(13)
5.2.1 General Setting
126(4)
5.2.2 A General Procedure for the Optimization of Strata in a Frame
130(2)
5.2.3 An Example
132(7)
5.3 Application to Networks
139(10)
5.3.1 Use of Networks as Frames
139(6)
5.3.2 Sampling Massive Networks
145(4)
5.4 Conclusions
149(1)
References
149(2)
6 Exploring the Role of Small Molecules in Biological Systems Using Network Approaches 151(22)
Rajarshi Guha
Sourav Das
6.1 The Role of Networks in Drug Discovery
152(1)
6.2 R for Network Analyses
153(1)
6.3 Linking Small Molecules to Targets, Pathways, and Diseases
154(8)
6.3.1 Drug-Target Networks
154(1)
6.3.2 Disease Networks
155(1)
6.3.3 SAR Networks
156(1)
6.3.4 Assay Networks
157(1)
6.3.5 Scaffold Networks
158(1)
6.3.6 Scaffold-Document Networks
159(3)
6.4 R as a Platform for Network Analyses in Drug Discovery
162(3)
6.5 Discussion
165(1)
Acknowledgments
165(1)
References
166(7)
7 Performing Network Alignments with R 173(28)
Qiang Huang
Ling-Yun Wu
7.1 Introduction
173(2)
7.2 Problems, Models, and Algorithms
175(8)
7.2.1 Problems
176(4)
7.2.1.1 Pairwise Network Alignment
176(2)
7.2.1.2 Network Querying
178(1)
7.2.1.3 Multiple Network Alignment
179(1)
7.2.2 Models and Algorithms
180(1)
7.2.3 Comparison and Challenges
180(3)
7.2.3.1 NQ Versus PNA
180(2)
7.2.3.2 PNA Versus MNA
182(1)
7.2.3.3 Challenges
182(1)
7.3 Algorithms Based on Conditional Random Fields
183(10)
7.3.1 CNetQ for Network Querying
183(3)
7.3.1.1 General Framework
183(2)
7.3.1.2 Feature Function
185(1)
7.3.1.3 Gap Penalty
185(1)
7.3.1.4 Network Simplification
186(1)
7.3.1.5 Real Examples
186(1)
7.3.2 CNetA for Pairwise Network Alignment
186(3)
7.3.2.1 Iterative Bidirectional Mapping Strategy
187(1)
7.3.2.2 Simulated Data
188(1)
7.3.2.3 Comparison
188(1)
7.3.2.4 Evaluation Measures
189(1)
7.3.3 CNetMA for Multiple Network Alignment
189(4)
7.3.3.1 Grmlin
189(1)
7.3.3.2 IsoRank
190(1)
7.3.3.3 MNA Examples
190(1)
7.3.3.4 CNetMA
191(2)
7.4 Performing Network Alignments with R
193(3)
7.4.1 Installation
193(1)
7.4.1.1 CRF Package
193(1)
7.4.1.2 Corbi Package
193(1)
7.4.2 Usage
193(2)
7.4.2.1 Input File Format
194(1)
7.4.2.2 Output File Format
194(1)
7.4.2.3 Arguments
194(1)
7.4.3 Examples
195(1)
7.4.3.1 Network Querying
195(1)
7.4.3.2 Pairwise Network Alignment
195(1)
7.4.4 Web Services and Tool Functions
196(1)
7.5 Discussion
196(1)
References
197(4)
8 l1-Penalized Methods in High-Dimensional Gaussian Markov Random Fields 201(66)
Luigi Augugliaro
Angelo M. Mineo
Ernst C. Wit
8.1 Introduction
201(1)
8.2 Graph Theory: Terminology and Basic Topological Notions
202(1)
8.3 Probabilistic Graphical Models
203(1)
8.4 Markov Random Field
204(3)
8.4.1 Ising Model and Extensions
205(1)
8.4.2 Gaussian Markov Random Fields
206(1)
8.5 Sparse Inference in High-dimensional GMRFs
207(45)
8.5.1 Neighborhood Selection
207(2)
8.5.2 The R Package simone
209(1)
8.5.3 Osteolytic Lesions Data Set: An Analysis by Neighborhood Selection Method
210(5)
8.5.4 Graphical Lasso Estimator
215(2)
8.5.5 The R Package glasso: Computing the Gradient and Coefficient Solution Path on a Simulated Data Set
217(6)
8.5.6 Computational Aspects of the glasso Estimator: the Block-Coordinate Descent Algorithm
223(2)
8.5.7 Faster Computation via Exact Covariance Thresholding
225(2)
8.5.8 Lung Cancer Microarray Data: An Analysis by glasso Estimator
227(6)
8.5.9 The Joint Graphical Lasso
233(2)
8.5.10 Computational Aspects of the jglasso Estimator: ADMM Algorithm
235(4)
8.5.11 The R Package JGL
239(2)
8.5.12 Lung Cancer Microarray Data: An Analysis by jglasso Estimator
241(2)
8.5.13 Structured Graphical Lasso
243(5)
8.5.13.1 Computational Aspects of the sglasso Estimator: Cyclic Coordinate Algorithms
246(2)
8.5.14 The R Package sglasso
248(2)
8.5.15 Neisseria meningitidis Data Set: An Analysis by fglasso Estimator
250(2)
8.6 Selecting the Optimal Value of the Tuning Parameter
252(4)
8.7 Summary and Conclusion
256(3)
References
259(8)
9 Cluster Analysis of Social Networks Using R 267(22)
Malika Charrad
9.1 Introduction
267(1)
9.2 Cluster Analysis in Social Networks
268(2)
9.2.1 Social Network Data
268(1)
9.2.1.1 The Data as a Graph
268(1)
9.2.1.2 The Data as a Matrix
269(1)
9.2.2 Clustering in Social Networks
269(1)
9.3 Cluster Analysis in Social Networks Using R
270(15)
9.3.1 R Packages for Cluster Analysis
270(1)
9.3.2 Data Loading and Formatting
270(4)
9.3.2.1 Removing Zero Edges
271(1)
9.3.2.2 Coercing the Data into a Graph Object
271(1)
9.3.2.3 Creating Social and Task Subgraphs
272(2)
9.3.3 Agglomerative Hierarchical Clustering
274(5)
9.3.3.1 Measuring Similarity/Dissimilarity
274(1)
9.3.3.2 Clustering
275(1)
9.3.3.3 Cluster Validity
276(3)
9.3.4 Edge Betweenness Clustering Algorithm
279(2)
9.3.5 Fast Greedy Modularity Optimization Algorithm
281(2)
9.3.6 Walktrap Algorithm
283(2)
9.4 Discussion and Further Readings
285(1)
References
286(3)
10 Inference and Analysis of Gene Regulatory Networks in R 289(18)
Ricardo de M. Simoes
Matthias Dehmer
Constantine Mitsiades
Frank Emmert-Streib
10.1 Introduction
289(1)
10.2 Multiple Myeloma
290(1)
10.3 Installation of Required R Packages from CRAN and Bioconductor
291(1)
10.4 Data Preprocessing
292(2)
10.5 Bc3net Gene Regulatory Network Inference
294(3)
10.6 Retrieving and Generating Gene Sets for a Functional Analysis
297(1)
10.7 Pathway and Other Gene Set Collections
298(4)
10.7.1 Functional Enrichment Analysis of Gene Regulatory Networks
300(2)
10.8 Conclusion
302(1)
References
303(4)
11 Visualization of Biological Networks Using NetBioV 307(28)
Shailesh Tripathi
Salissou Moutari
Matthias Dehmer
Frank Emmert-Streib
11.1 Introduction
307(3)
11.2 Network Visualization
310(3)
11.3 NetBioV
313(6)
11.3.1 Global Network Layouts
313(3)
11.3.2 Modular Network Layout
316(1)
11.3.3 Layered Network (Multiroot) Layout
317(1)
11.3.4 Other Features
318(1)
11.3.4.1 Information Flow
318(1)
11.3.4.2 Spiral View
318(1)
11.3.4.3 Color Schemes, Node Labeling
318(1)
11.3.4.4 Interface to R and Customization
319(1)
11.4 Example: Visualization of Networks Using NetBioV
319(6)
11.4.1 Loading Library and Data
320(1)
11.4.2 Global Layout Style
320(2)
11.4.2.1 R Code in Figure 11.4
320(2)
11.4.3 Modular Layout Style
322(1)
11.4.3.1 R Code in Figure 11.5
322(1)
11.4.4 Layered Layout Style
323(3)
11.4.4.1 R Code in Figure 11.6
323(2)
11.5 Conclusion
325(1)
11.6 Appendix
326(3)
11.6.1 R Code for the Visualization in Figures 11.2 and 11.3
326(3)
11.7 Spiral View
329(1)
11.7.1 Spiral Layout Style in Figure 11.7
329(1)
References
330(5)
Index 335
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.
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