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Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.
High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.
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.
"This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--
Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
The biological complexity essentially includes and involves processes that are mediated through explicitly non-linear interactions that are often typically entangled in nature. These comprise a myriad of interactions among a vast number of entities such as genes, proteins, metabolites, and species, widely varying in scale. These interactions render biological systems across spatial and temporal scales as complex adaptive systems having features like: self-organisation, modularity, emergence, non-linear interactions, collective response and adaptation. The theory of complex networks offers an appropriate formal framework for modelling such complex systems. The enormous wealth of biological data generated by high-throughput techniques, as also through empirical investigations can be analysed using the aforementioned formal framework to obtain important insights into biological complexity.The concept of networks can be used:1) to explore the relationships between entities resulting in network generation; 2) to guide the analytic procedure based on existing network(s) as prior knowledge; and 3) to analyze the prior network(s) regarding their topology and attributes. Complex networks, being ubiquitous, permeate the biological systems across spatial and temporal scales. The objective of this collection is to highlight some very salient features of such inherent complexity in biological systems by adopting a network theoretic perspective. The anticipated pay-off is obtaining a deeper insight explicitly into the systems-level interactions and the emergent complex behaviour of the systems. Also, investigating the propulsive forces which lend various networks with akin topological characteristics that would help to merge vivid information related to various molecular interactions into a single framework, thereby permitting a structural perspective of the cellular dynamics.The application may include – exploring the disease/environmental stress response and trait mechanism using different omics platforms, candidate gene discovery and validation, network-guided discovery and deployment of omics approaches in biology; modern genetic improvement methods for delivering genes in addition to high throughput and precise phenotyping methodologies, exploring the disease/environmental stress response mechanism, marker re-prioritization, network-guided biomarker discovery etc.
This book is a collection of principles and current practices in omics research, applied to skeletal muscle physiology and disorders. The various sections are categorized according to the level of biological organization, namely, genomics (DNA), transcriptomics (RNA), proteomics (protein), and metabolomics (metabolite). With skeletal muscle as the unifying theme, and featuring contributions from leading experts in this traditional field of research, it highlights the importance of skeletal muscle tissue in human development, health and successful ageing. It also discusses other fascinating topics like developmental biology, muscular dystrophies, exercise, insulin resistance and atrophy due to disuse, ageing or other muscle diseases, conveying the vast opportunities for generating new hypotheses as well as testing existing hypotheses by combining high-throughput techniques with proper experiment designs, bioinformatics and statistical analyses. Presenting the latest research techniques, this book is a valuable resource for the physiology community, particularly researchers and grad students who want to explore the new opportunities for omics technologies in basic physiology research.