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.
Inhalt
Chapter 1: Introduction to R, RStudio and ggplot2
1.1 Introduction to R
1.2 Introduction to RStudio
1.3 Introduction to ggplot2
1.4 Introduction to R Packages for Microbiome Data
Chapter 2: What are Microbiome Data?
2.1 Phylogenetics--The Basics
2.2 What Microbiome Data Look Like?
2.2.1 Basic Data Structure and Format of Microbiome Data
2.2.2 OUT Table
2.2 3 Response Variables and Covariates
2.3 Some Specific Features of Microbiome Data
Chapter 3: Bioinformatic and Statistical Analyses of Microbiome Data
3.1 Overview of Bioinformatic Analysis
3.1.1 Taxonomic Diversity: from the 16S-based Approach
3.1.2 Taxonomic Profiling of Shotgun Metage
nomes3.1.3 Introduction to Bioinformatic tools
o QIIME
o Mothur
o 16S rRNA Gene Sequence Data Analysis using QIIME and Mothur
o Other Biostatistics Tools
3.2 Statistical Analysis of Microbiome Community Composition
3.2.1 Alpha Diversity Analysis and Statistical Measurements
3.2.2 Beta Diversity Analysis and Statistical Measurements
3.3 Multivariate Statistical Techniques
3.3.1Data Visualization: Principal Component and Principal Coordinates Analyses
3.3.2 Classification and Clustering with Visualization
3.4 Hypothesis Testing and Statistical Modeling
3.4.1 Statistical Testing of Microbiome Community
3.4.2 Multivariate Statistical Methods and Modeling of Microbiome Community and Environmental Covariates
3.4.3 Mediational and Longitudinal Microbiom
e Data Analysis3.4.4 Host Interactions and Interventions
3.4.5 Mediation Analysis and Longitudinal Analysis
3.5 Multiple Comparisons and Testing Correlation
3.6 Correlation Analysis of Microbiome Community and Environmental Covariates
Chapter 4: Power and Sample Size Calculation in Hypothesis Testing Microbiome Data
4.1 Statistical Hypothesis Testing and Power Analysis
4.1.1 Hypothesis Testing
4.1.2 Power Analysis and Sample Size Calculation
4.2 Comparing Diversity or a Taxon of Interest between Two Groups
4.2.1 Hypotheses and Basic Power and Sample Size Formulas4.2.2 Diversity Data for Vitamin D and Vitamin D Receptor Study
4.2.3 Theory of Power for a Test for Comparing Proportions
4.2.4 Power of Fisher's Exact Test for Comparing Proportions
4.2.5 R Function power.t.test
4.3 Comparing Diversity across More than Two Groups 4.3.1 Hypotheses and Theory of Power for One-Way ANOVA
4.3.2 Examples
4.3.2 R Function pwr.avova.test
4.4 Comparing the Frequency of all Taxa across Groups
4.4.1 Hypotheses Testing and Power and Sample Size Calculations for Comparing all Taxa
4.4.2 Dirichlet-multinomial model in Power and Sample Size Analyses
4.4.3 Power and Size Calculations using HMP Package
4.5 Power and Sample Size Estimation using Pairwise Distances and PERMANOVA
4.5.1 PERMANOVA and Estimation of PERMANOVA Power
4.5.2 Examples using micropower Package
4.6 Power Calculations using ANOSIM Package
Chapter 5: Microbiome Data Management
5.1 Data Importing and Merging datasets or components
5.1.1 Importing the Output from QIIME
5.1.2 Importing the Output from mo
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