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· 제목 : Statistical Analysis of Microbiome Data with R (Paperback)
· 분류 : 외국도서 > 컴퓨터 > 수학/통계 소프트웨어
· ISBN : 9789811346453
· 쪽수 : 505쪽
· 출판일 : 2018-12-16
· 분류 : 외국도서 > 컴퓨터 > 수학/통계 소프트웨어
· ISBN : 9789811346453
· 쪽수 : 505쪽
· 출판일 : 2018-12-16
목차
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
nomes3.1.3 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 Analysis3.4.4 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 Formulas4.2.2 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 Comparison of beta diversity across groups
7.3. 2 Multiple Testing and FDR
7.4 Multivariate Analysis of Variance (MANOVA)
Chapter 8: Community Composition Study
8.1 Analyzing Diversity Using Wilcox Test (KW)
8.1.1 Introduction of Wilcox Test
8.1.2 Example using Wilcox Test
8.1 Hypothesis Testing among Groups using Multivariate Analysis of Variance (NPMANOVA)
8.1.1 In
troduction of NPMANOVA8.1.2 Implementations of NPMANOVA using adonis function in the vegan package
8.2 Hypothesis Tests of Among Group-Differences using Mantel’s Test (MANTEL)
8.2.1 Introduction of Mantel Test
8.2.2 Illustrating Mantel Test using vegan Package
8.3 Hypothesis Tests of Among-Group Differences using ANOSIM
8.3.1 Introduction of Analysis of Similarity (ANOSIM)
8.3.2 Illustrating Analysis of Similarity (ANOSIM) using vegan Package
8.4 Hypothesis Tests of Multi-Response Permutation Procedures (MRPP)
8.4.1 Introduction of MRPP
8.4.2 Illustrating MRPP with Example
8.5 Generalized UniFrac Distance using PERMANOVA
8.5.1 Introduction of Generalized UniFrac Distance Method
8.5. 2 Example using Generalized UniFrac Distance Method
Chapter 9: Modeling Over-dispersed Microbiome Data
9.1 Negative Binomial (NB) Model&
nbsp;9.1.1 Introduction of Negative Binomial
9.1.2 Data Analysis Using Negative Binomial
o Step-by-Step Implementation with DESeq2 Package
o Step-by-Step Implementation with edgeR Package
o DESeq2 vs edgeR Comparisons
9.2 Dirichlet-Multinomial Model
9.2.1 Introduction of Dirichlet-Multinomial Model
9.2. 2 Example using Dirichlet-Multinomial Model
9.3 Analysis of Composition of Microbiomes (ANCOM)
9.3.1 Introduction of ANCOM
9.3.2 Example using ANCOM
Chapter 10: Linear Regression Modeling metadata
10.1 Modeling Two Groups with LIMMA
10.2 Compare between LIMMA and T-Test
10.3 LM-phyloseq Function
10.4 Discuss Why LIMMA IS Preferred Over T-Test
Chapter 11: Modeling Zero-Inflated Microbiome Data
11.1 Fit Zero-inflated Log-Normal Mixture Model for Differential Abundance Testing Using metageno
meSeq11.2 Fit Zero-Inflated Negative Binomial
11.3 Fit Hurdle models
11.4 Fit Zero-inflated Gaussian(ZIG) mixture model Using metagenomeSeq
