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Statistical Analysis of Microbiome Data with R
Statistical Analysis of Microbiome Data with R
Knygos.lt klubas Knygos.lt nariams
382,15 €
-15%
Įprastai
449,59 €
  • Išsiųsime per 12–18 d.d.
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…
  • Leidėjas:
  • ISBN-10: 9811315337
  • ISBN-13: 9789811315336
  • Formatas: 15.6 x 23.4 x 2.9 cm, kieti viršeliai
  • Kalba: Anglų

Statistical Analysis of Microbiome Data with R (el. knyga) (skaityta knyga) | knygos.lt

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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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  • Autorius: Yinglin Xia
  • Leidėjas:
  • ISBN-10: 9811315337
  • ISBN-13: 9789811315336
  • Formatas: 15.6 x 23.4 x 2.9 cm, kieti viršeliai
  • Kalba: Anglų

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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