Statistical Analysis of Microbiome Data with R (eBook)

Yinglin Xia, Jun Sun, Ding-Geng Chen (Autoren)

eBook Download: PDF

2018 | 1st ed. 2018
XXIII, 505 Seiten
Springer Singapore (Verlag)
978-981-13-1534-3 (ISBN)

Infos

This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors' research and from the public domain, and discusses the implementation of R for data analysis step by step. The data 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.

The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.

Inhalt

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 Table2.2 3 Response Variables and Covariates2.3 Some Specific Features of Microbiome DataChapter 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 Metagenomes3.1.3 Introduction to Bioinformatic toolso QIIME o Mothuro 16S rRNA Gene Sequence Data Analysis using QIIME and Mothuro Other Biostatistics Tools3.2   Statistical Analysis of Microbiome Community Composition  3.2.1   Alpha Diversity Analysis and Statistical Measurements  3.2.2   Beta Diversity Analysis and Statistical Measurements3.3   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 Covariates3.4.3 Mediational and Longitudinal Microbiome Data Analysis3.4.4 Host Interactions and Interventions3.4.5 Mediation Analysis and Longitudinal Analysis    3.5 Multiple Comparisons and Testing Correlation   3.6 Correlation Analysis of Microbiome Community and Environmental CovariatesChapter 4: Power and Sample Size Calculation in Hypothesis Testing Microbiome Data4.1 Statistical Hypothesis Testing and Power Analysis 4.1.1 Hypothesis Testing 4.1.2 Power Analysis and Sample Size Calculation4.2 Comparing Diversity or a Taxon of Interest between Two Groups 4.2.1 Hypotheses and Basic Power and Sample Size Formulas4.2.2 Diversity Data for Vitamin D and Vitamin D Receptor Study4.2.3 Theory of Power for a   Test for Comparing Proportions4.2.4 Power of Fisher's Exact Test for Comparing Proportions4.2.5 R Function power.t.test4.3 Comparing Diversity across More than Two Groups 4.3.1 Hypotheses and Theory of Power for One-Way ANOVA4.3.2 Examples4.3.2 R Function pwr.avova.test4.4 Comparing the Frequency of all Taxa across Groups4.4.1 Hypotheses Testing and Power and Sample Size Calculations for Comparing all Taxa4.4.2 Dirichlet-multinomial model in Power and Sample Size Analyses4.4.3 Power and Size Calculations using HMP Package4.5 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 Package4.6 Power Calculations using ANOSIM PackageChapter 5: Microbiome Data Management5.1 Data Importing and Merging datasets or components   5.1.1 Importing the Output from QIIME   5.1.2 Importing the Output from mothur   5.1.3 biom format files   5.1,4 Download from website5.2 Preprocessing Abundance Data   5.2.1 Subsetting OTUs   5.2.2 Filtering5.3 Rarefying and Normalizing Microbiome Data   5.3.1 Rarefying    5.3.2 Normalization Chapter 6: Exploratory Analysis of Microbiome Data6.1 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 ranks6.2 Simple Summary Graphics   6.2.1 Plot Richness   6.2.2 Plot Phylogenetic Tree   6.2.3 Plot Abundance Bar6.3 Graphics for Inference and Exploration   6.3.1 Clustering, Distance and Ordination   6.3.2 Density plot   6.3.3 Boxplot   6.3.4 HeatmapChapter 7: Comparisons of Diversities, OTUs and Taxa among Groups 7.1 Estimates of Taxonomic Alpha and Beta Diversity7.1.1 Alpha and Beta Diversity7.1.2 Calculating Alpha and Beta Diversity7.2 Comparisons between Two Groups Using t-test7.3 Comparisons among more than Two Groups Using ANOVA7.3.1 Comparison of beta diversity across groups7.3. 2 Multiple Testing and FDR7.4 Multivariate Analysis of Variance (MANOVA)Chapter 8: Community Composition Study8.1 Analyzing Diversity Using Wilcox Test (KW)8.1.1 Introduction of Wilcox Test8.1.2 Example using Wilcox Test8.1 Hypothesis Testing among Groups using Multivariate Analysis of Variance (NPMANOVA)8.1.1 Introduction of NPMANOVA8.1.2 Implementations of NPMANOVA using adonis function in the vegan package8.2 Hypothesis Tests of Among Group-Differences using Mantel’s Test (MANTEL)8.2.1 Introduction of Mantel Test8.2.2 Illustrating Mantel Test using vegan Package8.3 Hypothesis Tests of Among-Group Differences using ANOSIM8.3.1 Introduction of Analysis of Similarity (ANOSIM)8.3.2 Illustrating Analysis of Similarity (ANOSIM) using vegan Package8.4 Hypothesis Tests of Multi-Response Permutation Procedures (MRPP)8.4.1 Introduction of MRPP8.4.2 Illustrating MRPP with Example8.5 Generalized UniFrac Distance using PERMANOVA8.5.1 Introduction of Generalized UniFrac Distance Method8.5. 2 Example using Generalized UniFrac Distance MethodChapter 9: Modeling Over-dispersed Microbiome Data 9.1 Negative Binomial (NB) Model 9.1.1 Introduction of Negative Binomial9.1.2 Data Analysis Using Negative Binomialo Step-by-Step Implementation with DESeq2 Packageo Step-by-Step Implementation with edgeR Packageo DESeq2 vs edgeR Comparisons9.2 Dirichlet-Multinomial Model9.2.1 Introduction of  Dirichlet-Multinomial Model9.2. 2 Example using Dirichlet-Multinomial Model9.3 Analysis of Composition of Microbiomes (ANCOM)9.3.1 Introduction of ANCOM9.3.2 Example using ANCOMChapter 10: Linear Regression Modeling metadata10.1 Modeling Two Groups with LIMMA10.2 Compare between LIMMA and T-Test10.3 LM-phyloseq Function10.4 Discuss Why LIMMA IS Preferred Over T-TestChapter 11: Modeling Zero-Inflated Microbiome Data11.1 Fit Zero-inflated Log-Normal Mixture Model for Differential Abundance Testing Using metagenomeSeq11.2 Fit Zero-Inflated Negative Binomial11.3 Fit Hurdle models 11.4 Fit Zero-inflated  Gaussian(ZIG)  mixture  model Using metagenomeSeq

Produkt-Details

• Erscheint lt. Verlag: 6.10.2018
• Reihe/Serie: ICSA Book Series in Statistics
• Reihe/Serie: ICSA Book Series in Statistics
• Zusatzinfo: XXIII, 505 p. 84 illus., 67 illus. in color.
• Verlagsort: Singapore
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Medizin / Pharmazie ► Allgemeines / Lexika
• Themenwelt: Studium ► 1. Studienabschnitt (Vorklinik) ► Physiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Mikrobiologie / Immunologie
• Schlagworte: Metagenomics • Microbiome Data Analysis • Multivariate Data Analysis • R software • Statistical Models • Statistics
• ISBN-10: 981-13-1534-5 / 9811315345
• ISBN-13: 978-981-13-1534-3 / 9789811315343