Tutorial for Chapter 12
Corrected Tutorial for Chapter 12
Data for Chapter 12
Description: This book presents state-of-the-art methods, software and applications surrounding weighted networks. Most methods also apply also to unweighted networks. The book reviews data mining methods and analysis strategies. Applications and exercises guide the reader on how to use these methods in practice, e.g. in systems-biologic or systems-genetic applications. The material is self-contained and only requires a minimum knowledge of statistics. The accessible material is intended for students, faculty, and data analysts in many fields including bioinformatics, computational biology, statistics, computer science, biology, genetics, applied mathematics, physics, and social science. Networks have been applied to analyze a variety of high dimensional data including gene expression-, epigenetic-, methylation-, RNA-seq, proteomics-, and fMRI- data. Theoretical chapters explore the fascinating topological structure of networks and relate network methods to traditional data mining techniques (e.g. clustering techniques). Powerful systems-level analysis methods result from combining network- with data mining methods. Networks can be used to define data reduction techniques, clustering procedures (fuzzy clustering), variable selection methods (pathway-based screening), visualization methods, data exploratory methods, and intuitive approaches for integrating disparate data sets. Although aspects of weighted network analysis relate to standard data mining techniques, the intuitive network language and analysis framework transcend any particular analysis technique. The book can be studied in an interactive fashion using R software and data available from a companion webpage. It includes R code for producing most of its figures. While it focuses on the WGCNA R package, it also describes other software packages.
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