Bioinformatic and Statistical Analysis of Microbiome Data : From Raw Sequences to Advanced Modeling with QIIME 2 and R

목차 (Table of Contents)

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• Chapter 1:  Introduction to Linux and Unix(This chapter will introduce some important bioinformatics tools and basics of Linux/Unix system and basic operations with Linux/Unix.) 1.1. Bioinformatics tools and Linux/Unix1.2. Features of Linux/Unix1.3. Interact with Linux/Unix Chapter 2: Introduction to R, RStudio(This chapter will introduce the environment of microbiome data analysis: R, RStudio, and some important R functions and data manipulation skills. All these skills will provide a foundation of bioinformatic and biostatistical analyses of microbiome data.) 2.1. Introduction to R and RStudio2.1.1 Installing R, RStudio, and R Packages2.1.2 Set Working Directory in R2.1.3 Data Analysis through R Studio     2.1.4 Data Import and Export     2.1.5 Basic Data Manipulation2.1. 6 Simple Summary Statistics2.1.7 Other useful R functions2.2. Useful R Packages for Data Management Chapter 3: Bioinformatic Analysis of Next-Generation Sequencing(This chapter will cover next-generation sequencing (NGS) and bioinformatic analysis of NGS data, such as sequencing data quality check, trimming, gene annotation, sequencing alignment, and genome indexing.) 3.1. Introduction to Next-Generation Sequencing3.2. Bioinformatic Analysis of Next-Generation Sequencing3.2.1 Sequencing Data Quality Check3.2.2 Sequencing Data Trimming   3.2.3 Gene Annotation3.2.4 Sequencing Alignment3.2.5 Genome Indexing3.2.6 Remove PCR Duplicates3.3. Introduction to Genome Browsers3.3.1 IGV (Integrative Genome Brower)3.3.2 UCSC Chapter 4: Bioinformatic Analysis of Metagenomics(This chapter will cover bioinformatic analysis of NGS and metagenomics data step by step. The steps will focus on bioinformatic analysis of amplicon sequencing, such as generate OTUs, taxonomic annotation and create OUT table. ) 4.1 Definition of Metagenomics4.2 Amplicon Sequencing4.2.1 Preprocessing4.2.2 Generate OTUs4.2.3 Taxonomic Annotation4.2.4 Create OUT Table4.3 Bioinformatcs Tools for Amplicon Sequencing4.3.1 QIIME 24.3.2 mothur   4.3.3 Bioinformatic Analysis of 16S rRNA Sequence Data using QIIME 2 and mothur4.4 Bioinformatic Analysis of Shortgun Metagenomic Data   4.4.1 Processing of Samples, DNA and Library4.4.2 Quality Checking4.4.3 Assembly4.4.4 Binning4.4.5 Annotation4.4.5.1 Genome and Metagenome Functional Annotations4.4.5.2 Gene Prediction and Functional Annotation Chapter 5: Alpha Diversity(This chapter will introduce biostatistical analysis of alpha diversity of microbiome data. The contents will cover alpha diversity measures and calculations, exploration, statistical hypothesis testing, and power analysis.) 5.1 Introduction to Community Diversities5.1.1 Alpha Diversity5.1.2 Beta Diversity5.2 Alpha Diversity Measures and Calculations5.2.1 Chao 1 Richness Index5.2.2 Shannon-Wiener Diversity Index5.2.3 Simpson Diversity Index5.2.4 Pielou's Evenness Index5.3 Exploration of Alpha Diversity5.3.1 Richness5.3.2 Abundance Bar5.3.3 Heatmap5.3.4 Network   5.3.5 Phylogenetic Tree5.4 Statistical Hypothesis Testing of Alpha Diversity    5.4.1 Two-sample Welch's t-test    5.4.2 Wilcoxon Rank Sum Test     5.4.3 Chi-square Test             5.4.4 One-way ANOVA   5.5.5 Kruskal-Wallis Test 5.5  Multiple Comparisons and Multiple Testing       5.5.1 Pairwise Comparisons       5.5.2 E-value       5.5.3 FWER       5.5.4  FDR5.6. Power Analysis for Testing Differences in Diversity5.6.1 Using power.t.test()5.6.2 Using pwr.avova.test()5.6.3 Using power.prop.test()   5.6.4 Using pwr.chisq.test()   5.6.5 Using  power.fisher.test()   5.6.6 Using  power.exact.test() Chapter 6: Beta Diversity(This chapter will introduce biostatistical analysis of beta diversity of microbiome data. The contents will cover beta diversity measures and calculations, exploration, ordination, statistical hypothesis testing.) 6.1 Beta Diversity Measures and Calculations6.1.1 Jaccard Index6.1.2  Sørensen Index6.1.3 Bray?Curtis Index6.2 Exploration of Beta Diversity6.2.1 Clustering6.2.1.1 Single Linkage6.2.1.2 Complete Linkage6.2.1.3 Average Linkage   6.2.1.4 Ward’s Minimum Variance6.2.2 Ordination6.2.2.1 Principal Component Analysis (PCA)6.2.2.2 Principal Coordinate Analysis (PCoA) 6.2.2.3 Non-metric multidimensional scaling (NMDS)6.4 Statistical Hypothesis Testing of Beta Diversity   6.4.1 Permutational Multivariate Analysis of Variance (PERMANOVA)   6.4.1.1 Implement PERMANOVA using vegan Package   6.4.1.2 Implement Pairwise Permutational MANOVA using RVAideMemoire Package6.4.2 Analysis of Similarity (ANOSIM) 6.4.2.1  Implement ANOSIM using vegan Package   6.4.3  Compare Microbiome Communities        6.4.3.1 UniFrac, Weighted UniFrac and Generalized UniFrac Distance Metrics     6.4.3.2  Implement Comparison using GUniFrac Package Chapter 7: Differential Abundance Analysis(This chapter will cover two models for count-based differential abundance analysis of microbiome data: negative binomial (NB) models in edgeR and in DESeq2.)  7.1. Count-based Differential Abundance Analysis7.1.1 Biological and Technical Variations7.1.2 Poisson 7.1.3 Negative Binomial (NB)7.2 NB Model in edgeR7.2.1 Exploration of Differential Abundant Taxa7.2.1.1 PCoA7.2.1.2 Heatmap7.2.1.3 Volcano Plot7.2.2 Statistical Hypothesis Testing in edgeR7.2.2.1 The Wald Test7.2.2.2 The Generalized Linear model (GLM)7.3. NB Model in DESeq and DESeq27.3.1 Statistical Hypothesis Testing in DESeq2      7.3.2 Implement DESeq2 Chapter 8: Analyzing Zero-Inflated Microbiome Data(This chapter will introduce both classic and newly developed statistical models for analyzing zero-inflated count microbiome data and show how to use different tests to compare these models. ) 8.1 Zero-inflated Models8.1.1 ZIP Model8.1.2 ZINB Model8.2 Zero-Hurdle Models8.2.1 ZHP Model8.2.2 ZHNB Model8.3 Comparison of Zero-inflated and Zero-Hurdle Models8.3.1 Using Likelihood Ratio Test8.3.2 Using AIC8.3.3 Using BIC8.3.4 Using Vuong Test8.4 Zero-inflated Gaussian (ZIG)8.4.1 Statistical Hypothesis Testing     8.4.1.1 Non-parametric Permutation Test on t-statistics     8.4.1.2 Non-parametric Kruskal-Wallis Test8.4.2 Implement using metagenomeSeq package8.5 Marginalized two-part Beta Regression(MTPBR)8.5.1 Introduction to MTPBR8.4.2 Implement using NLMIXED Procedure8.6 Geometric Mean of Pairwise Ratios (GMPR)8.5.1 Introduction to GMPR8.4.2 Implement using GMPR Package Chapter 9: Compositional Analysis of Microbiome Data(This chapter will summarize the issues of compositional data analysis and introduce the newly developed statistical models and methods for compositional data analysis in microbiome research.) 9.1 Introduction to Compositional Data9.1.1 Aitchison Simplex9.1.2 Fundamental Principles9.1.3 A Family of Log-ratio Transformations   9.1.4 Relative Characteristics of Microbiome Abundance Data9.2 ANOVA-Like Differential Abundance Analysis for Compositional Data9.2.1 Exploratory Compositional Data Analysis9.2.1.1 Compositional Biplot9.2.1. 2 Compositional Scree Plot9.2.1. 3 Compositional Cluster Dendrogram        9.2.1. 4 Compositional Barplot   9.2.2 Using ALDEx2 Package9.3 Analysis of Composition of Microbiomes (ANCOM)9.3.1 Introduction to ANCOM   9.3.2 Implement using ANCOM Package9.4 Balances: a Relative Abundances Perspective for Microbiome Analysis9.4.1  Introduction to  Balances9.4.2  Implementing Selection of Balances Using selbal Package Chapter 10: Longitudinal Data Analysis of Microbiome(This chapter will introduce several newly developed statistical models and methods for longitudinal data analysis of microbiome.) 10.1 Zero-inflated Beta Regression Model with Random Effects: ZIBR10.1.1 Statistical Hypothesis Testing of ZIBR10.1.2 Implement using ZIBR Package10.2 Differential Distribution Analysis of Microbiome Data10.1.1 A General Framework of Statistical Hypothesis Testing based on a ZINB10.1.2 Implement using MicrobiomeDDA package10.3 Negative Binomial Mixed Models (NBMMs)10.3.1 Introduction to NBMMs10.3.2 Implement using NBZIMMpackage Chapter 11: Meta-analysis of Microbiome Data (optional)(This chapter will summarize current approaches of meta-analysis of microbiome data and discuss the issues of current approaches. The zero-inflated Beta GAMLSS of meta-analysis of microbiome data will be introduced.) 11.1 Introduction to Meta-analysis in Microbiome Studies11.2 Zero-inflated Beta GAMLSS and Meta-analysis of Microbiome Relative Abundance11.3 Implement using metamicrobiomeR package