Statistical Analysis of Microbiome Data with R (Hardcover, 2018)

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 nomes 3.1.3 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 Analysis 3.4.4 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 Formulas 4.2.2 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 thur 5.1.3 biom format files 5.1,4 Download from website 5.2 Preprocessing Abundance Data 5.2.1 Subsetting OTUs 5.2.2 Filtering 5.3 Rarefying and Normalizing Microbiome Data 5.3.1 Rarefying 5.3.2 Normalization Chapter 6: Exploratory Analysis of Microbiome Data 6.1 Basic Statistics 6.1.1 Column mean, sum, Print 6.1.2 Convenience access and Abundance access 6.1.3 Interaction with the sample variable 6.1.4 with the taxonomic ranks 6.2 Simple Summary Graphics 6.2.1 Plot Richness 6.2.2 Plot Phylogenetic Tree 6.2.3 Plot Abundance Bar 6.3 Graphics for Inference and Exploration 6.3.1 Clusteri ng, Distance and Ordination 6.3.2 Density plot 6.3.3 Boxplot 6.3.4 Heatmap Chapter 7: Comparisons of Diversities, OTUs and Taxa among Groups 7.1 Estimates of Taxonomic Alpha and Beta Diversity 7.1.1 Alpha and Beta Diversity 7.1.2 Calculating Alpha and Beta Diversity 7.2 Comparisons between Two Groups Using t-test 7.3 Comparisons among more than Two Groups Using ANOVA 7.3.1 Compa