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Bioinformatic and Statistical Analysis of Microbiome Data Yinglin Xia

Bioinformatic and Statistical Analysis of Microbiome Data By Yinglin Xia

Bioinformatic and Statistical Analysis of Microbiome Data by Yinglin Xia


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Bioinformatic and Statistical Analysis of Microbiome Data Summary

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

This unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing. It includes real-world data from the authors' research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 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.

Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis.

About Yinglin Xia

Dr. Yinglin Xia is a Research Professor in the Department of Medicine at the University of Illinois Chicago (UIC). He was a Research Assistant Professor in the Department of Biostatistics and Computational Biology at the University of Rochester (Rochester, NY) and Clinical Statistician in AbbVie (North Chicago, IL) before joining UIC as a Research Associate Professor in 2015. Dr. Xia has published more than 140 statistical methodology and research papers in peer-reviewed journals. He serves on the editorial board for several scientific journals including as an Associate Editor of Gut Microbes and has served as a reviewer for over 100 scientific journals. He is the lead authors of Statistical Analysis of Microbiome Data with R (Springer Nature, 2018), which was the first statistics book in microbiome study, Statistical Data Analysis of Microbiomes and Metabolomics(American Chemical Society, 2022) and An Integrated Analysis of Microbiomes and Metabolomics (American Chemical Society, 2022).

Dr. Jun Sun is a tenured Professor of Medicine at the University of Illinois Chicago. She is an elected fellow of the American Gastroenterological Association (AGA) and American Physiological Society (APS). She chairs the AGA Microbiome and Microbial Therapy section.

She is an internationally recognized expert on microbiome and human diseases, such as vitamin D receptor in inflammation, dysbiosis and intestinal dysfunction in amyotrophic lateral sclerosis (ALS). Her lab is the first to discover chronic effects and molecular mechanisms of Salmonella infection and development of colon cancer. Dr. Sun has published over 210 scientific articles in peer-reviewed journals and 8 books on microbiome. She is on the editorial boards of more than 10 peer-reviewed international scientific journals, including a Deputy Editor for American Journal of Physiology-GIL, an Associate Editor for Gut Microbes. She serves on the study sections for the national and international research foundations.


Table of Contents

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 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 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 Sorensen 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 edgeR

7.2.1 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

Additional information

NPB9783031213908
9783031213908
3031213904
Bioinformatic and Statistical Analysis of Microbiome Data: From Raw Sequences to Advanced Modeling with QIIME 2 and R by Yinglin Xia
New
Hardback
Springer International Publishing AG
2023-05-16
703
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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