Computational Network Analysis with R - Info and Reading Options

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Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Chapter 1 Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks -- 1.1 Introduction -- 1.1.1 An Introduction to Omics and Systems Biology -- 1.1.2 Correlation Networks in Omics and Systems Biology -- 1.1.3 Network Modules and Differential Network Approaches -- 1.1.4 Aims of this Chapter -- 1.2 What is DiffCorr? -- 1.2.1 Background -- 1.2.2 Methods -- 1.2.3 Main Functions in DiffCorr -- 1.2.4 Installing the DiffCorr Package -- 1.3 Constructing Co-Expression (Correlation) Networks from Omics Data - Transcriptome Data set -- 1.3.1 Downloading the Transcriptome Data set -- 1.3.2 Data Filtering -- 1.3.3 Calculation of the Correlation and Visualization of Correlation Networks -- 1.3.4 Graph Clustering -- 1.3.5 Gene Ontology Enrichment Analysis -- 1.4 Differential Correlation Analysis by DiffCorr Package -- 1.4.1 Calculation of Differential Co-Expression between Organs in Arabidopsis -- 1.4.2 Exploring the Metabolome Data of Flavonoid-Deficient Arabidopsis -- 1.4.3 Avoiding Pitfalls in (Differential) Correlation Analysis -- 1.5 Conclusion -- Acknowledgments -- Conflicts of Interest -- References -- Chapter 2 Analytical Models and Methods for Anomaly Detection in Dynamic, Attributed Graphs -- 2.1 Introduction -- 2.2 Chapter Definitions and Notation -- 2.3 Anomaly Detection in Graph Data -- 2.3.1 Neighborhood-Based Techniques -- 2.3.2 Frequent Subgraph Techniques -- 2.3.3 Anomalies in Random Graphs -- 2.4 Random Graph Models -- 2.4.1 Models with Attributes -- 2.4.2 Dynamic Graph Models -- 2.5 Spectral Subgraph Detection in Dynamic, Attributed Graphs -- 2.5.1 Problem Model -- 2.5.2 Filter Optimization -- 2.5.3 Residuals Analysis in Attributed Graphs -- 2.6 Implementation in R -- 2.7 Demonstration in Random Synthetic Backgrounds

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