The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from b
Statistical Analysis of Microbiome Data with R
β Scribed by Yinglin Xia, Jun Sun, Ding-Geng Chen
- Publisher
- Springer
- Year
- 2018
- Tongue
- English
- Leaves
- 518
- Series
- ICSA Book Series in Statistics
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authorsβ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research.
The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.
β¦ Table of Contents
Front Matter ....Pages i-xxiii
Bioinformatic Analysis of Microbiome Data (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 1-27
What Are Microbiome Data? (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 29-41
Introductory Overview of Statistical Analysis of Microbiome Data (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 43-75
Introduction to R, RStudio and ggplot2 (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 77-127
Power and Sample Size Calculations for Microbiome Data (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 129-166
Community Diversity Measures and Calculations (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 167-190
Exploratory Analysis of Microbiome Data and Beyond (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 191-249
Univariate Community Analysis (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 251-283
Multivariate Community Analysis (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 285-330
Compositional Analysis of Microbiome Data (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 331-393
Modeling Over-Dispersed Microbiome Data (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 395-451
Modeling Zero-Inflated Microbiome Data (Yinglin Xia, Jun Sun, Ding-Geng Chen)....Pages 453-496
Back Matter ....Pages 497-505
β¦ Subjects
Statistics, Biostatistics, Microbiome Data, R
π SIMILAR VOLUMES
<p><p>Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going bac
R is a data analysis tool, graphical environment, and programming language. Without any prior experience in programming or statistical software, this book will help you quickly become a knowledgeable user of R. Now is the time to take control of your data and start producing superior statistical ana
<p><strong>Statistical Analysis of Financial Data</strong> covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illust
<strong>Statistical Analysis of Financial Data</strong> covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrat