Xia Yinglin, Sun Jun, Chen Ding-Geng Statistical Analysis of Microbiome Data with R 9789811315336

Варианты приобретения

Цена: 20962.00р.
Кол-во:
Наличие: Поставка под заказ. Есть в наличии на складе поставщика.
Склад Америка: Есть
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
При условии наличия книги у поставщика.

Добавить в корзину

в Мои желания

Автор: Xia Yinglin, Sun Jun, Chen Ding-Geng
Название: Statistical Analysis of Microbiome Data with R
ISBN: 9789811315336
Издательство: Springer
Классификация:

Физиология

Вероятность и статистика

Микробиология (немедицинская)

Математическое и статистическое ПО

ISBN-10: 9811315337
Обложка/Формат: Hardcover
Страницы: 505
Вес: 0.92 кг.
Дата издания: 21.10.2018
Серия: Icsa book series in statistics
Язык: English
Издание: 1st ed. 2018
Иллюстрации: 67 illustrations, color; 17 illustrations, black and white; xxiii, 505 p. 84 illus., 67 illus. in color.
Размер: 164 x 243 x 32
Читательская аудитория: General (us: trade)
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: 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 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 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 Covariates
Chapter 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 Fishers 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 Package
Chapter 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&
Дополнительное описание: Chapter 1: Introduction to R, RStudio and ggplot2.- Chapter 2: What are Microbiome Data?.- Chapter 3: Bioinformatic and Statistical Analyses of Microbiome Data.- Chapter 4: Power and Sample Size Calculation in Hypothesis Testing Microbiome Data.- Chapter

Statistical Analysis of Microbiome Data with R, Xia Yinglin, Sun Jun, Chen Ding-Geng