عنوان

Statistical analysis of microbiome data with R /

(Number (ISBN

9789811315343

(Number (ISBN

9789811315350

(Number (ISBN

9811315345

(Number (ISBN

9811315353

Erroneous ISBN

9789811315336

Erroneous ISBN

9811315337

Title Proper

Statistical analysis of microbiome data with R /

General Material Designation

[Book]

First Statement of Responsibility

Yinglin Xia, Jun Sun, Ding-Geng Chen.

Place of Publication, Distribution, etc.

Singapore :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2018.

Specific Material Designation and Extent of Item

1 online resource

Series Title

ICSA book series in statistics

Text of Note

Intro; Preface; Acknowledgements; Contents; About the Authors; 1 Bioinformatic Analysis of Microbiome Data; 1.1 Introduction to Microbiome Study; 1.1.1 What Is the Human Microbiome?; 1.1.2 Microbiome Research and DNA Sequencing; 1.2 Introduction to Phylogenetics; 1.3 16S rRNA Sequencing Approach; 1.3.1 The Advantages of 16S rRNA Sequencing; 1.3.2 Bioinformatic Analysis of 16S rRNA Sequencing Data; 1.3.2.1 Processing of Samples, DNA and Library; 1.3.2.2 DNA Sequencing and Quality Checking; 1.3.2.3 Cluster 16S rRNA Sequences into OTUs; 1.3.2.4 Limitations of 16S rRNA Sequencing Approach

Text of Note

1.4 Shotgun Metagenomic Sequencing Approach1.4.1 Definition of Metagenomics; 1.4.2 Advantages of Shotgun Metagenomic Sequencing; 1.4.3 Bioinformatic Analysis of Shotgun Metagenomic Data; 1.4.3.1 Processing of Samples, DNA and Library; 1.4.3.2 Quality Checking; 1.4.3.3 Assembly; 1.4.3.4 Binning; 1.4.3.5 Annotation; Genome and Metagenome Functional Annotations; Gene Prediction and Functional Annotation; 1.4.3.6 Challenges of Analyzing Shotgun Metagenomic Data; 1.5 Bioinformatics Data Analysis Tools; 1.5.1 QIIME; 1.5.2 mothur; 1.5.3 Analyzing 16S rRNA Sequence Data Using QIIME and Mothur

Text of Note

1.6 SummaryReferences; 2 What Are Microbiome Data?; 2.1 Microbiome Data; 2.2 Microbiome Data Structure; 2.2.1 Microbiome Data Are Structured as a Phylogenetic Tree; 2.2.2 Feature-by-Sample Contingency Table; 2.2.3 OTU Table; 2.2.4 Taxa Count Table; 2.2.5 Taxa Percent Table; 2.3 Features of Microbiome Data; 2.3.1 Microbiome Data Are Compositional; 2.3.2 Microbiome Data Are High Dimensional and Underdetermined; 2.3.3 Microbiome Data Are Over-Dispersed; 2.3.4 Microbiome Data Are Often Sparse with Many Zeros; 2.4 An Example of Over-Dispersed and Zero-Inflated Microbiome Data

Text of Note

2.5 Challenges of Modeling Microbiome Data2.6 Summary; References; 3 Introductory Overview of Statistical Analysis of Microbiome Data; 3.1 Research Themes and Statistical Hypotheses in Human Microbiome Studies; 3.2 Classic Statistical Methods and Models in Microbiome Studies; 3.2.1 Classic Statistical Tests; 3.2.2 Multivariate Statistical Tools; 3.2.3 Over-Dispersed and Zero-Inflated Models; 3.3 Newly Developed Multivariate Statistical Methods; 3.3.1 Dirichlet-Multinomial Model; 3.3.2 UniFrac Distance Metric Family; 3.3.3 Multivariate Bayesian Models; 3.3.4 Phylogenetic LASSO and Microbiome

Text of Note

3.4 Compositional Analysis of Microbiome Data3.5 Longitudinal Data Analysis and Causal Inference in Microbiome Studies; 3.5.1 Standard Longitudinal Models; 3.5.2 Newly Developed Over-Dispersed and Zero-Inflated Longitudinal Models; 3.5.3 Regression-Based Time Series Models; 3.5.4 Detecting Causality: Causal Inference and Mediation Analysis of Microbiome Data; 3.5.5 Meta-analysis of Microbiome Data; 3.6 Introduction of Statistical Packages; 3.7 Limitations of Existing Statistical Methods and Future Development; References; 4 Introduction to R, RStudio and ggplot2

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Text of Note

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.--

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9789811315343

Title

Statistical analysis of microbiome data with R.

International Standard Book Number

9789811315336

Microbiology-- Analysis.

Microbiology-- Data processing.

Microbiology-- Research.

Microbiology, Statistics.

Databases.

Mathematical & statistical software.

Microbiology-- Data processing.

Microbiology-- Research.

Microbiology.

Probability & statistics.

SCIENCE-- Life Sciences-- Biology.

SCIENCE-- Life Sciences-- Microbiology.

SCI-- 008000

SCI-- 045000

UFM

UFM

Number

Edition

Class number

Xia, Yinglin

Chen, Ding-Geng

Sun, Jun

Date of Transaction

20200823224956.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y