Yinglin Xia – författare & böcker

Applied Microbiome Statistics

Correlation, Association, Interaction and Composition

Inbunden, Engelska, 2024

2 410 kr

Skickas inom 10-15 vardagar

This unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts.Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction, and composition adopted from classical statistics and ecology and specifically designed for microbiome research.Performs step-by-step statistical analysis of correlation, association, interaction, and composition in microbiome data.Discusses the issues of statistical analysis of microbiome data: high dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity.Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data.Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot.Employs the Kruskal–Wallis rank-sum test to perform model selection for further multi-omics data integration.Offers R code and the datasets from the authors’ real microbiome research and publicly available data for the analysis used.Remarks on the advantages and disadvantages of each of the methods used.

Applied Microbiome Statistics

Correlation, Association, Interaction and Composition

AvJun Sun,Yinglin Xia

E-bok

PDF, Engelska, 2024

989 kr

Läs direkt efter köp

This unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts.

Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction, and composition adopted from classical statistics and ecology and specifically designed for microbiome research. Performs step-by-step statistical analysis of correlation, association, interaction, and composition in microbiome data. Discusses the issues of statistical analysis of microbiome data: high dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity. Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data. Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot. Employs the Kruskal–Wallis rank-sum test to perform model selection for further multi-omics data integration. Offers R code and the datasets from the authors’ real microbiome research and publicly available data for the analysis used. Remarks on the advantages and disadvantages of each of the methods used.

Applied Microbiome Statistics

Correlation, Association, Interaction and Composition

AvJun Sun,Yinglin Xia

E-bok

Engelska, 2024

989 kr

Läs direkt efter köp

This unique book officially defines microbiome statistics as a specific new field of statistics and addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research. It also defines the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts.

Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction, and composition adopted from classical statistics and ecology and specifically designed for microbiome research. Performs step-by-step statistical analysis of correlation, association, interaction, and composition in microbiome data. Discusses the issues of statistical analysis of microbiome data: high dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity. Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data. Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot. Employs the Kruskal–Wallis rank-sum test to perform model selection for further multi-omics data integration. Offers R code and the datasets from the authors’ real microbiome research and publicly available data for the analysis used. Remarks on the advantages and disadvantages of each of the methods used.

Machine Learning for Microbiome Statistics

AvJun Sun,Yinglin Xia

E-bok

PDF, Engelska, 2026

989 kr

Läs direkt efter köp

Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field. Presents a thorough overview of machine learning algorithms for microbiome statistics. Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models. Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering. Investigates and applies various cross-validation techniques step-by-step. Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews' correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data. Offers all related R codes and the datasets from the authors' first-hand microbiome research and publicly available data.

Machine Learning for Microbiome Statistics

AvJun Sun,Yinglin Xia

E-bok

Engelska, 2026

989 kr

Läs direkt efter köp

Machine learning fundamentally learns from the past experiences (seen data) to make predictions about future (unseen data). Predictions in nature are often uncertain. Microbiome data have unique characteristics, including high-dimensionality, over-dispersion, sparsity and zero-inflation, and heterogeneity. Thus, machine learning involving microbiome data for predicting the outcome of phenotypes is even more uncertain than learning those data from other fields. Machine Learning for Microbiome Statistics poses many challenges for evaluating the prediction performance using appropriate metrics and independent data validation.This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting. It comprehensively reviews commonly used and newly developed machine learning models for microbiome research. Specifically, this book provides the step-by-step procedures to perform machine learning of microbiome data, including feature engineering, algorithm selection and optimization, performance evaluation and model testing. It comments the benefits and limitations of using machine learning for microbiome statistics and remarks on the advantages and disadvantages of each machine learning algorithm.It will be an excellent reference book for students and academics in the field. Presents a thorough overview of machine learning algorithms for microbiome statistics. Performs step-by-step procedures to perform machine learning of microbiome data, using important supervised learning algorithms, including classical, ensemble learning and tree-based models. Describes important concepts of machine learning, including bias and variance tradeoff, accuracy and precision, overfitting and underfitting, model complexity and interpretability, and feature engineering. Investigates and applies various cross-validation techniques step-by-step. Introduces confusion matrix and its derived measures. Comprehensively describes the properties of F1, Matthews' correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR), as well as discusses their advantages and disadvantages when using them for microbiome data. Offers all related R codes and the datasets from the authors' first-hand microbiome research and publicly available data.

Machine Learning for Microbiome Statistics

AvYinglin Xia,USA) Sun, Jun (Department of Medicine, University of Illinois Chicago

Inbunden, Engelska, 2025

2 410 kr

Skickas inom 10-15 vardagar

This unique book aims to address the challenges of machine learning statistics, emphasize the importance of performance valuation by appropriate metrics and independent data, and describe several important concepts of machine learning statistics, such as feature engineering and overfitting.

Microbiome Statistics Set

Taylor & Francis Ltd

2026

3 429 kr

Skickas inom 10-15 vardagar

Microbiome Statistics Set addresses the statistical analysis of correlation, association, interaction, and composition in microbiome research and talks about the challenges of machine learning statistics with an emphasis on the importance of performance valuation by appropriate metrics and independent data.

Bioinformatic and Statistical Analysis of Microbiome Data

From Raw Sequences to Advanced Modeling with QIIME 2 and R

AvYinglin Xia,Jun Sun

Inbunden, Engelska, 2023

2 149 kr

Skickas inom 10-15 vardagar

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.

Bioinformatic and Statistical Analysis of Microbiome Data

From Raw Sequences to Advanced Modeling with QIIME 2 and R

AvJun Sun,Yinglin Xia

E-bok

Engelska, 2023

2 925 kr

Läs direkt efter köp

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.

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

AvYinglin Xia,Jun Sun

Engelska, 2023

665 kr

Skickas inom 5-8 vardagar

Bioinformatic and Statistical Analysis of Microbiome Data

From Raw Sequences to Advanced Modeling with QIIME 2 and R

AvYinglin Xia,Jun Sun

Häftad, Engelska, 2024

2 149 kr

Skickas inom 10-15 vardagar

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.

Statistical Analysis of Microbiome Data with R

AvYinglin Xia,Jun Sunm. fl.

Inbunden, Engelska, 2018

1 883 kr

Skickas inom 10-15 vardagar

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.

Statistical Analysis of Microbiome Data with R

AvDing-Geng Chen,Jun Sunm. fl.

E-bok

Engelska, 2018

2 359 kr

Läs direkt efter köp

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.

Statistical Analysis of Microbiome Data with R

AvYinglin Xia,Jun Sunm. fl.

Häftad, Engelska, 2018

1 828 kr

Skickas inom 10-15 vardagar

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.

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Yinglin Xia – författare

Applied Microbiome Statistics

Applied Microbiome Statistics

Applied Microbiome Statistics

Machine Learning for Microbiome Statistics

Machine Learning for Microbiome Statistics

Machine Learning for Microbiome Statistics

Microbiome Statistics Set

Bioinformatic and Statistical Analysis of Microbiome Data

Bioinformatic and Statistical Analysis of Microbiome Data

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

Bioinformatic and Statistical Analysis of Microbiome Data

Statistical Analysis of Microbiome Data with R

Statistical Analysis of Microbiome Data with R

Statistical Analysis of Microbiome Data with R

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