Overview
- Written by experts actively engaged in the field
- Includes timely discussions and presentations on methodological development in microbiome studies and real-world applications
- Includes data and computer programs that are publicly available, allowing readers to replicate the statistical analyses
- Offers a framework for analysing microbiome data
Part of the book series: ICSA Book Series in Statistics (ICSABSS)
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Table of contents (12 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ 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.
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Authors and Affiliations
Bibliographic Information
Book Title: Statistical Analysis of Microbiome Data with R
Authors: Yinglin Xia, Jun Sun, Ding-Geng Chen
Series Title: ICSA Book Series in Statistics
DOI: https://doi.org/10.1007/978-981-13-1534-3
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2018
Hardcover ISBN: 978-981-13-1533-6Published: 20 October 2018
Softcover ISBN: 978-981-13-4645-3Published: 16 December 2018
eBook ISBN: 978-981-13-1534-3Published: 06 October 2018
Series ISSN: 2199-0980
Series E-ISSN: 2199-0999
Edition Number: 1
Number of Pages: XXIII, 505
Number of Illustrations: 17 b/w illustrations, 67 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Statistics for Life Sciences, Medicine, Health Sciences, Big Data