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Author: Somnath Datta Publisher: Springer Nature ISBN: 3030733513 Category : Medical Languages : en Pages : 349
Book Description
Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
Author: Somnath Datta Publisher: Springer Nature ISBN: 3030733513 Category : Medical Languages : en Pages : 349
Book Description
Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
Author: Yinglin Xia Publisher: Springer ISBN: 9811315345 Category : Computers Languages : en Pages : 518
Book Description
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.
Author: Jacques Izard Publisher: Academic Press ISBN: 0124105084 Category : Science Languages : en Pages : 188
Book Description
Concisely discussing the application of high throughput analysis to move forward our understanding of microbial principles, Metagenomics for Microbiology provides a solid base for the design and analysis of omics studies for the characterization of microbial consortia. The intended audience includes clinical and environmental microbiologists, molecular biologists, infectious disease experts, statisticians, biostatisticians, and public health scientists. This book focuses on the technological underpinnings of metagenomic approaches and their conceptual and practical applications. With the next-generation genomic sequencing revolution increasingly permitting researchers to decipher the coding information of the microbes living with us, we now have a unique capacity to compare multiple sites within individuals and at higher resolution and greater throughput than hitherto possible. The recent articulation of this paradigm points to unique possibilities for investigation of our dynamic relationship with these cellular communities, and excitingly the probing of their therapeutic potential in disease prevention or treatment of the future. - Expertly describes the latest metagenomic methodologies and best-practices, from sample collection to data analysis for taxonomic, whole shotgun metagenomic, and metatranscriptomic studies - Includes clear-headed pointers and quick starts to direct research efforts and increase study efficacy, eschewing ponderous prose - Presented topics include sample collection and preparation, data generation and quality control, third generation sequencing, advances in computational analyses of shotgun metagenomic sequence data, taxonomic profiling of shotgun data, hypothesis testing, and mathematical and computational analysis of longitudinal data and time series. Past-examples and prospects are provided to contextualize the applications.
Author: Brian J. Reich Publisher: CRC Press ISBN: 0429510918 Category : Mathematics Languages : en Pages : 288
Book Description
Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.
Author: Otso Ovaskainen Publisher: Cambridge University Press ISBN: 1108492460 Category : Nature Languages : en Pages : 389
Book Description
A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.
Author: David J. Balding Publisher: John Wiley & Sons ISBN: 1119429250 Category : Science Languages : en Pages : 1740
Book Description
A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.
Author: Richard A. Levine Publisher: John Wiley & Sons ISBN: 1119561086 Category : Mathematics Languages : de Pages : 672
Book Description
Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.
Author: Kim-Anh Lê Cao Publisher: CRC Press ISBN: 1000472191 Category : Computers Languages : en Pages : 316
Book Description
Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. There is a real need for good training in statistics, from data exploration through to analysis and interpretation. This book provides an overview of statistical and dimension reduction methods for high-throughput biological data, with a specific focus on data integration. It starts with some biological background, key concepts underlying the multivariate methods, and then covers an array of methods implemented using the mixOmics package in R. Features: Provides a broad and accessible overview of methods for multi-omics data integration Covers a wide range of multivariate methods, each designed to answer specific biological questions Includes comprehensive visualisation techniques to aid in data interpretation Includes many worked examples and case studies using real data Includes reproducible R code for each multivariate method, using the mixOmics package The book is suitable for researchers from a wide range of scientific disciplines wishing to apply these methods to obtain new and deeper insights into biological mechanisms and biomedical problems. The suite of tools introduced in this book will enable students and scientists to work at the interface between, and provide critical collaborative expertise to, biologists, bioinformaticians, statisticians and clinicians.
Author: Dale P. Mood Publisher: Taylor & Francis ISBN: 1351818821 Category : Computers Languages : en Pages : 406
Book Description
"Our goal is to give readers the knowledge and skill to use statistics effectively in their professional lives and feel comfortable doing so."--From the Preface This new textbook, by two renowned authors with many years of teaching experience, provides: A sound overview of statistical procedures and introduction to the basics of statistical analyses An informal perspective that enables students to read, interpret, and use statistics directly related to their chosen careers in the kinesiology field (e.g., exercise physiology, physical therapy, medicine, personal training, nurse practitioner, physician’s assistant, and more) Relevant examples, review questions, practice problems, and SPSS activities, which help to make the material understandable and interesting A student website with videos, interactive concept reviews, image bank, and PowerPoint slides offers students the tools they need to understand the statistical concepts and learn at their own pace
Author: Somnath Datta
Author: Somnath Datta
Author: Yinglin Xia
Author: Robert G. Beiko
Author: Jacques Izard
Author: Brian J. Reich
Author: Otso Ovaskainen
Author: David J. Balding
Author: Richard A. Levine
Author: Kim-Anh Lê Cao
Author: Dale P. Mood