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· 분류 : 외국도서 > 과학/수학/생태 > 과학 > 생명과학 > 생명과학 일반
· ISBN : 9783031213908
· 쪽수 : 703쪽
목차
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/Unix
1.2. Features of Linux/Unix
1.3. 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 RStudio
2.1.1 Installing R, RStudio, and R Packages
2.1.2 Set Working Directory in R
2.1.3 Data Analysis through R Studio
2.1.4 Data Import and Export
2.1.5 Basic Data Manipulation
2.1. 6 Simple Summary Statistics
2.1.7 Other useful R functions
2.2. 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 Sequencing
3.2. Bioinformatic Analysis of Next-Generation Sequencing
3.2.1 Sequencing Data Quality Check
3.2.2 Sequencing Data Trimming
3.2.3 Gene Annotation
3.2.4 Sequencing Alignment
3.2.5 Genome Indexing
3.2.6 Remove PCR Duplicates
3.3. Introduction to Genome Browsers
3.3.1 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 Metagenomics
4.2 Amplicon Sequencing
4.2.1 Preprocessing
4.2.2 Generate OTUs
4.2.3 Taxonomic Annotation
4.2.4 Create OUT Table
4.3 Bioinformatcs Tools for Amplicon Sequencing
4.3.1 QIIME 2
4.3.2 mothur
4.3.3 Bioinformatic Analysis of 16S rRNA Sequence Data using QIIME 2 and mothur
4.4 Bioinformatic Analysis of Shortgun Metagenomic Data
4.4.1 Processing of Samples, DNA and Library
4.4.2 Quality Checking
4.4.3 Assembly
4.4.4 Binning
4.4.5 Annotation
4.4.5.1 Genome and Metagenome Functional Annotations
4.4.5.2 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 Diversities
5.1.1 Alpha Diversity
5.1.2 Beta Diversity
5.2 Alpha Diversity Measures and Calculations
5.2.1 Chao 1 Richness Index
5.2.2 Shannon-Wiener Diversity Index
5.2.3 Simpson Diversity Index
5.2.4 Pielou's Evenness Index
5.3 Exploration of Alpha Diversity
5.3.1 Richness
5.3.2 Abundance Bar
5.3.3 Heatmap
5.3.4 Network
5.3.5 Phylogenetic Tree
5.4 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 Test5.4.4 One-way ANOVA
5.5.5 Kruskal-Wallis Test5.5 Multiple Comparisons and Multiple Testing
5.5.1 Pairwise Comparisons
5.5.2 E-value
5.5.3 FWER
5.5.4 FDR
5.6. Power Analysis for Testing Differences in Diversity
5.6.1 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 Calculations
6.1.1 Jaccard Index
6.1.2 Sørensen Index
6.1.3 Bray?Curtis Index
6.2 Exploration of Beta Diversity
6.2.1 Clustering
6.2.1.1 Single Linkage
6.2.1.2 Complete Linkage
6.2.1.3 Average Linkage
6.2.1.4 Ward’s Minimum Variance
6.2.2 Ordination
6.2.2.1 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 Package
6.4.2 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 Analysis
7.1.1 Biological and Technical Variations
7.1.2 Poisson
7.1.3 Negative Binomial (NB)
7.2 NB Model in edgeR7.2.1 Exploration of Differential Abundant Taxa
7.2.1.1 PCoA
7.2.1.2 Heatmap
7.2.1.3 Volcano Plot
7.2.2 Statistical Hypothesis Testing in edgeR
7.2.2.1 The Wald Test
7.2.2.2 The Generalized Linear model (GLM)
7.3. NB Model in DESeq and DESeq2
7.3.1 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 Models
8.1.1 ZIP Model
8.1.2 ZINB Model
8.2 Zero-Hurdle Models
8.2.1 ZHP Model
8.2.2 ZHNB Model
8.3 Comparison of Zero-inflated and Zero-Hurdle Models
8.3.1 Using Likelihood Ratio Test
8.3.2 Using AIC
8.3.3 Using BIC
8.3.4 Using Vuong Test
8.4 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 Test
8.4.2 Implement using metagenomeSeq package
8.5 Marginalized two-part Beta Regression(MTPBR)
8.5.1 Introduction to MTPBR
8.4.2 Implement using NLMIXED Procedure
8.6 Geometric Mean of Pairwise Ratios (GMPR)
8.5.1 Introduction to GMPR
8.4.2 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 Data
9.1.1 Aitchison Simplex
9.1.2 Fundamental Principles
9.1.3 A Family of Log-ratio Transformations
9.1.4 Relative Characteristics of Microbiome Abundance Data
9.2 ANOVA-Like Differential Abundance Analysis for Compositional Data
9.2.1 Exploratory Compositional Data Analysis
9.2.1.1 Compositional Biplot
9.2.1. 2 Compositional Scree Plot
9.2.1. 3 Compositional Cluster Dendrogram
9.2.1. 4 Compositional Barplot
9.2.2 Using ALDEx2 Package
9.3 Analysis of Composition of Microbiomes (ANCOM)
9.3.1 Introduction to ANCOM
9.3.2 Implement using ANCOM Package
9.4 Balances: a Relative Abundances Perspective for Microbiome Analysis
9.4.1 Introduction to Balances
9.4.2 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: ZIBR
10.1.1 Statistical Hypothesis Testing of ZIBR
10.1.2 Implement using ZIBR Package
10.2 Differential Distribution Analysis of Microbiome Data
10.1.1 A General Framework of Statistical Hypothesis Testing based on a ZINB
10.1.2 Implement using MicrobiomeDDA package
10.3 Negative Binomial Mixed Models (NBMMs)
10.3.1 Introduction to NBMMs
10.3.2 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 Studies
11.2 Zero-inflated Beta GAMLSS and Meta-analysis of Microbiome Relative Abundance
11.3 Implement using metamicrobiomeR package
