Overview

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.

Full Product Details

Author:   Yinglin Xia ,  Jun Sun ,  Ding-Geng Chen
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   1st ed. 2018
Weight:   0.957kg
ISBN:  

9789811315336

ISBN 10:   9811315337
Pages:   505
Publication Date:   20 October 2018
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1: Introduction to R, RStudio and ggplot2.- Chapter 2: What are Microbiome Data?.- Chapter 3: Bioinformatic and Statistical Analyses of Microbiome Data.- Chapter 4: Power and Sample Size Calculation in Hypothesis Testing Microbiome Data.- Chapter 5: Microbiome Data Management.- Chapter 6: Exploratory Analysis of Microbiome Data.- Chapter 7: Comparisons of Diversities, OTUs and Taxa among Groups.- Chapter 8: Community Composition Study.- Chapter 9: Modeling Over-dispersed Microbiome Data.- Chapter 10: Linear Regression Modeling metadata.- Chapter 11: Modeling Zero-Inflated Microbiome Data.

Reviews

Statistical Analysis of Microbiome Data With R represents a very good foundational resource for bioinformaticians and statisticians interested in this emerging area of research. (Kim-Anh Le Cao, Biometrical Journal, Vol. 61, 2019)

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