Edited by Matthias Dehmer, Yongtang Shi, and Frank Emmert-Streib Computational Network Analysis with R www.ebook3000.com “Quantitative and Network Biology” Series editors M Dehmer and F Emmert-Streib Advisory Board: Albert-László Barabási Northeastern University & Harvard Medical School, USA Douglas Lauffenburger Massachusetts Institute of Technology, USA Satoru Miyano University of Tokyo, Japan Ilya Shmulevich Institute for Systems Biology & University of Washington, USA Previous Volumes of this Series: Volume Dehmer, M., Emmert-Streib, F., Graber, A., Salvador, A (eds.) Applied Statistics for Network Biology Volume Emmert-Streib, F., Dehmer, M (eds.) Statistical Diagnostics for Cancer Analyzing High-Dimensional Data Methods in Systems Biology 2013 2011 ISBN: 978-3-527-32434-7 ISBN: 978-3-527-32750-8 Volume Dehmer, M., Varmuza, K., Bonchev, D (eds.) Statistical Modelling of Molecular Descriptors in QSAR/QSPR Volume Emmert-Streib, F., Dehmer, M (eds.) Advances in Network Complexity 2013 ISBN: 978-3-527-33291-5 2012 ISBN: 978-3-527-32434-7 www.ebook3000.com Volume Dehmer, M., Emmert-Streib, F., Pickl, S (eds.) Volume Dehmer, M., Shi, Y., Emmert-Streib, F Computational Network Theory Computational Network Analysis with R Applications in Biology, Medicine and Chemistry 2015 2016 978-3-527-33724-8 ISBN: 978-3-527-33958-7 Volume Dehmer, M., Chen, Z., Li, X., Shi, Y., Emmert-Streib, F Mathematical Foundations and Applications of Graph Entropy 2016 ISBN: 978-3-527-33909-9 www.ebook3000.com Quantitative and Network Biology Series editors M Dehmer and F Emmert-Streib Volume Computational Network Analysis with R Applications in Biology, Medicine, and Chemistry Edited by Matthias Dehmer, Yongtang Shi, and Frank Emmert-Streib www.ebook3000.com The Editors Prof Matthias Dehmer UMIT –The Health and Life Sciences University Eduard Wallnoefer Zentrum 6060 Hall Austria All books published by Wiley-VCH are carefully produced Nevertheless, authors, editors, and publisher not warrant the information contained in these books, including this book, to be free of errors Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate and Library of Congress Card No.: applied for Nankai University College of Computer and Control Engineering 300071 Tianjin P.R China Prof Yongtang Shi British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Bibliographic information published by the Deutsche Nationalbibliothek Nankai University Center for Combinatorics No 94 Weijin Road 300071 Tianjin China The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at Prof Frank Emmert-Streib Tampere University of Technology Predictive Medicine and Analytics Lab Department of Signal Processing Tampere Finland Cover Andrey Prokhorov/iStock (Background Picture) © 2017 Wiley-VCH Verlag GmbH & Co KGaA, Boschstr 12, 69469 Weinheim, Germany All rights reserved (including those of translation into other languages) No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers Registered names, trademarks, etc used in this book, even when not specifically marked as such, are not to be considered unprotected by law Print ISBN: 978-3-527-33958-7 ePDF ISBN: 978-3-527-69440-2 ePub ISBN: 978-3-527-69437-2 Mobi ISBN: 978-3-527-69438-9 oBook ISBN: 978-3-527-69436-5 Typesetting SPi Global, Chennai, India Printed on acid-free paper www.ebook3000.com VII Contents List of Contributors XV Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks Atsushi Fukushima and Kozo Nishida 1.1 1.1.1 1.1.2 1.1.3 1.1.4 1.2 1.2.1 1.2.2 1.2.3 1.2.4 1.3 Introduction An Introduction to Omics and Systems Biology Correlation Networks in Omics and Systems Biology Network Modules and Differential Network Approaches Aims of this Chapter What is DiffCorr? Background Methods Main Functions in DiffCorr Installing the DiffCorr Package Constructing Co-Expression (Correlation) Networks from Omics Data – Transcriptome Data set Downloading the Transcriptome Data set Data Filtering Calculation of the Correlation and Visualization of Correlation Networks 11 Graph Clustering 15 Gene Ontology Enrichment Analysis 17 Differential Correlation Analysis by DiffCorr Package 21 Calculation of Differential Co-Expression between Organs in Arabidopsis 21 Exploring the Metabolome Data of Flavonoid-Deficient Arabidopsis 26 Avoiding Pitfalls in (Differential) Correlation Analysis 29 Conclusion 30 Acknowledgments 30 Conflicts of Interest 30 References 30 1.3.1 1.3.2 1.3.3 1.3.4 1.3.5 1.4 1.4.1 1.4.2 1.4.3 1.5 www.ebook3000.com VIII Contents 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 2.2 2.3 2.3.1 2.3.2 2.3.3 2.4 2.4.1 2.4.2 2.5 2.5.1 2.5.2 2.5.3 2.6 2.7 2.8 2.9 Introduction 35 Chapter Definitions and Notation 36 Anomaly Detection in Graph Data 37 Neighborhood-Based Techniques 37 Frequent Subgraph Techniques 38 Anomalies in Random Graphs 39 Random Graph Models 41 Models with Attributes 41 Dynamic Graph Models 43 Spectral Subgraph Detection in Dynamic, Attributed Graphs Problem Model 44 Filter Optimization 46 Residuals Analysis in Attributed Graphs 47 Implementation in R 50 Demonstration in Random Synthetic Backgrounds 51 Data Analysis Example 55 Summary 58 Acknowledgments 58 References 59 Bayesian Computational Algorithms for Social Network Analysis 63 Alberto Caimo and Isabella Gollini 3.1 3.2 3.3 3.3.1 3.3.2 3.4 3.4.1 3.5 3.5.1 3.5.2 3.5.3 3.6 Introduction 63 Social Networks as Random Graphs 64 Statistical Modeling Approaches to Social Network Analysis 64 Exponential Random Graph Models (ERGMs) 65 Latent Space Models (LSMs) 65 Bayesian Inference for Social Network Models 66 R-Based Software Tools 67 Data 67 Bayesian Inference for Exponential Random Graph Models 68 Bayesian Inference for Latent Space Models 71 Predictive Goodness-of-Fit (GoF) Diagnostics 76 Conclusions 80 References 81 Threshold Degradation in R Using iDEMO Chien-Yu Peng and Ya-Shan Cheng 4.1 4.2 4.2.1 4.2.1.1 Introduction 83 Statistical Overview: Degradation Models 85 Wiener Degradation-Based Process 85 Lifetime Information 86 www.ebook3000.com 83 44 Contents 4.2.1.2 4.2.2 4.2.2.1 4.2.2.2 4.2.3 4.2.3.1 4.2.3.2 4.2.4 4.2.5 4.2.6 4.3 4.3.1 4.3.2 4.3.3 4.3.3.1 4.3.3.2 4.3.3.3 4.3.4 4.3.4.1 4.3.4.2 4.3.5 4.3.6 4.4 4.4.1 4.4.2 4.4.3 4.5 Log-Likelihood Function 87 Gamma Degradation-Based Process 88 Lifetime Information 88 Log-Likelihood Function 89 Inverse Gaussian Degradation-Based Process 89 Lifetime Distribution 90 Log-Likelihood Function 91 Model Selection Criteria 91 Choice of Λ(t) 91 Threshold Degradation 92 iDEMO Interface and Functions 92 Overview of the Package iDEMO Functionality 93 Data Input Format 93 Starting the iDEMO 93 Import Data 94 Basic Information 95 Degradation Model Selection 96 Single Degradation Model Analysis 96 Parameter Estimation 97 Lifetime Information 98 Odds and Ends 101 Computational Details 101 Case Applications 101 Laser Example 102 Fatigue Example 106 ADT Example 112 Concluding Remarks 122 References 122 Optimization of Stratified Sampling with the R Package SamplingStrata: Applications to Network Data 125 Marco Ballin and Giulio Barcaroli 5.1 5.2 5.2.1 5.2.2 Networks and Stratified Sampling 125 The R Package SamplingStrata 126 General Setting 126 A General Procedure for the Optimization of Strata in a Frame 130 An Example 132 Application to Networks 139 Use of Networks as Frames 139 Sampling Massive Networks 145 Conclusions 149 References 149 5.2.3 5.3 5.3.1 5.3.2 5.4 www.ebook3000.com IX X Contents Exploring the Role of Small Molecules in Biological Systems Using Network Approaches 151 Rajarshi Guha and Sourav Das 6.1 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.3.5 6.3.6 6.4 6.5 The Role of Networks in Drug Discovery 152 R for Network Analyses 153 Linking Small Molecules to Targets, Pathways, and Diseases 154 Drug–Target Networks 154 Disease Networks 155 SAR Networks 156 Assay Networks 157 Scaffold Networks 158 Scaffold-Document Networks 159 R as a Platform for Network Analyses in Drug Discovery 162 Discussion 165 Acknowledgments 165 References 166 Performing Network Alignments with R 173 Qiang Huang and Ling-Yun Wu 7.1 7.2 7.2.1 7.2.1.1 7.2.1.2 7.2.1.3 7.2.2 7.2.3 7.2.3.1 7.2.3.2 7.2.3.3 7.3 7.3.1 7.3.1.1 7.3.1.2 7.3.1.3 7.3.1.4 7.3.1.5 7.3.2 7.3.2.1 7.3.2.2 7.3.2.3 7.3.2.4 7.3.3 7.3.3.1 7.3.3.2 Introduction 173 Problems, Models, and Algorithms 175 Problems 176 Pairwise Network Alignment 176 Network Querying 178 Multiple Network Alignment 179 Models and Algorithms 180 Comparison and Challenges 180 NQ Versus PNA 180 PNA Versus MNA 182 Challenges 182 Algorithms Based on Conditional Random Fields CNetQ for Network Querying 183 General Framework 183 Feature Function 185 Gap Penalty 185 Network Simplification 186 Real Examples 186 CNetA for Pairwise Network Alignment 186 Iterative Bidirectional Mapping Strategy 187 Simulated Data 188 Comparison 188 Evaluation Measures 189 CNetMA for Multiple Network Alignment 189 Græmlin 189 IsoRank 190 www.ebook3000.com 183 Contents 7.3.3.3 7.3.3.4 7.4 7.4.1 7.4.1.1 7.4.1.2 7.4.2 7.4.2.1 7.4.2.2 7.4.2.3 7.4.3 7.4.3.1 7.4.3.2 7.4.4 7.5 MNA Examples 190 CNetMA 191 Performing Network Alignments with R Installation 193 CRF Package 193 Corbi Package 193 Usage 193 Input File Format 194 Output File Format 194 Arguments 194 Examples 195 Network Querying 195 Pairwise Network Alignment 195 Web Services and Tool Functions 196 Discussion 196 References 197 𝓵1 -Penalized Methods in High-Dimensional Gaussian Markov Random Fields 201 Luigi Augugliaro, Angelo M Mineo, and Ernst C Wit 8.1 8.2 8.3 8.4 8.4.1 8.4.2 8.5 8.5.1 8.5.2 8.5.3 Introduction 201 Graph Theory: Terminology and Basic Topological Notions 202 Probabilistic Graphical Models 203 Markov Random Field 204 Ising Model and Extensions 205 Gaussian Markov Random Fields 206 Sparse Inference in High-dimensional GMRFs 207 Neighborhood Selection 207 The R Package simone 209 Osteolytic Lesions Data Set: An Analysis by Neighborhood Selection Method 210 Graphical Lasso Estimator 215 The R Package glasso: Computing the Gradient and Coefficient Solution Path on a Simulated Data Set 217 Computational Aspects of the glasso Estimator: the Block-Coordinate Descent Algorithm 223 Faster Computation via Exact Covariance Thresholding 225 Lung Cancer Microarray Data: An Analysis by glasso Estimator 227 The Joint Graphical Lasso 233 Computational Aspects of the jglasso Estimator: ADMM Algorithm 235 The R Package JGL 239 Lung Cancer Microarray Data: An Analysis by jglasso Estimator 241 Structured Graphical Lasso 243 8.5.4 8.5.5 8.5.6 8.5.7 8.5.8 8.5.9 8.5.10 8.5.11 8.5.12 8.5.13 193 www.ebook3000.com XI ... masked from ’package:pcaMethods’: ## ## leverage par(mfrow=c(1, 2)) plot(im(GSE5632.cor[nrow(GSE5632.cor):1, ]), col=cm.colors(25 6), main="GSE5632 ") plot(im(GSE5630.cor[nrow(GSE5630.cor):1, ]), col=cm.colors(25 6), ... libxml2-dev" first source("http://bioconductor.org/biocLite .R" ) 1.2 What is DiffCorr? biocLite(c("pcaMethods", "multtest ")) install.packages("DiffCorr ") library(DiffCorr) ## Loading required package:... acquired quickly and routinely RNA sequencing with NGS (RNA-seq) measures nearly every transcript of cellular systems (i.e., transcriptome) [5–7] The term omics refers to the comprehensive analysis