Power and Sample Size Calculations for Microbiome Data

Abstract

Intro; Preface; Acknowledgements; Contents; About the Authors; 1 Bioinformatic Analysis of Microbiome Data; 1.1 Introduction to Microbiome Study; 1.1.1 What Is the Human Microbiome?; 1.1.2 Microbiome Research and DNA Sequencing; 1.2 Introduction to Phylogenetics; 1.3 16S rRNA Sequencing Approach; 1.3.1 The Advantages of 16S rRNA Sequencing; 1.3.2 Bioinformatic Analysis of 16S rRNA Sequencing Data; 1.3.2.1 Processing of Samples, DNA and Library; 1.3.2.2 DNA Sequencing and Quality Checking; 1.3.2.3 Cluster 16S rRNA Sequences into OTUs; 1.3.2.4 Limitations of 16S rRNA Sequencing Approach 1.4 Shotgun Metagenomic Sequencing Approach1.4.1 Definition of Metagenomics; 1.4.2 Advantages of Shotgun Metagenomic Sequencing; 1.4.3 Bioinformatic Analysis of Shotgun Metagenomic Data; 1.4.3.1 Processing of Samples, DNA and Library; 1.4.3.2 Quality Checking; 1.4.3.3 Assembly; 1.4.3.4 Binning; 1.4.3.5 Annotation; Genome and Metagenome Functional Annotations; Gene Prediction and Functional Annotation; 1.4.3.6 Challenges of Analyzing Shotgun Metagenomic Data; 1.5 Bioinformatics Data Analysis Tools; 1.5.1 QIIME; 1.5.2 mothur; 1.5.3 Analyzing 16S rRNA Sequence Data Using QIIME and Mothur 1.6 SummaryReferences; 2 What Are Microbiome Data?; 2.1 Microbiome Data; 2.2 Microbiome Data Structure; 2.2.1 Microbiome Data Are Structured as a Phylogenetic Tree; 2.2.2 Feature-by-Sample Contingency Table; 2.2.3 OTU Table; 2.2.4 Taxa Count Table; 2.2.5 Taxa Percent Table; 2.3 Features of Microbiome Data; 2.3.1 Microbiome Data Are Compositional; 2.3.2 Microbiome Data Are High Dimensional and Underdetermined; 2.3.3 Microbiome Data Are Over-Dispersed; 2.3.4 Microbiome Data Are Often Sparse with Many Zeros; 2.4 An Example of Over-Dispersed and Zero-Inflated Microbiome Data 2.5 Challenges of Modeling Microbiome Data2.6 Summary; References; 3 Introductory Overview of Statistical Analysis of Microbiome Data; 3.1 Research Themes and Statistical Hypotheses in Human Microbiome Studies; 3.2 Classic Statistical Methods and Models in Microbiome Studies; 3.2.1 Classic Statistical Tests; 3.2.2 Multivariate Statistical Tools; 3.2.3 Over-Dispersed and Zero-Inflated Models; 3.3 Newly Developed Multivariate Statistical Methods; 3.3.1 Dirichlet-Multinomial Model; 3.3.2 UniFrac Distance Metric Family; 3.3.3 Multivariate Bayesian Models; 3.3.4 Phylogenetic LASSO and Microbiome 3.4 Compositional Analysis of Microbiome Data3.5 Longitudinal Data Analysis and Causal Inference in Microbiome Studies; 3.5.1 Standard Longitudinal Models; 3.5.2 Newly Developed Over-Dispersed and Zero-Inflated Longitudinal Models; 3.5.3 Regression-Based Time Series Models; 3.5.4 Detecting Causality: Causal Inference and Mediation Analysis of Microbiome Data; 3.5.5 Meta-analysis of Microbiome Data; 3.6 Introduction of Statistical Packages; 3.7 Limitations of Existing Statistical Methods and Future Development; References; 4 Introduction to R, RStudio and ggplot2