STATISTICAL ANALYSIS OF MICROBIOME DATA WITH R
Language: English
Published by Springer, 2018
- Hardcover
- New

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- Title
- STATISTICAL ANALYSIS OF MICROBIOME DATA WITH R
- Author
- Xia, Yinglin; Sun, Jun; Chen, Ding-Geng
- Publisher
- Springer
- Publication year
- 2018
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 9811315337
- ISBN 13
- 9789811315336
- Series
- Book 14 of 19: ICSA Book Series in Statistics
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Majestic Books
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