LEADER 03299nam  22005535  450 
001 9910300110903321
005 20230810231725.0
010   $a981-13-1534-5
024 7 $a10.1007/978-981-13-1534-3
035   $a(CKB)4100000006999490
035   $a(MiAaPQ)EBC5543530
035   $a(DE-He213)978-981-13-1534-3
035   $a(PPN)231458630
035   $a(EXLCZ)994100000006999490
100   $a20181006d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aStatistical Analysis of Microbiome Data with R /$fby Yinglin Xia, Jun Sun, Ding-Geng Chen
205   $a1st ed. 2018.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2018.
215   $a1 online resource (518 pages)
225 1 $aICSA Book Series in Statistics,$x2199-0999
311   $a981-13-1533-7 
327   $aChapter 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 5: Microbiome Data Management -- Chapter 6: Exploratory Analysis of Microbiome Data -- Chapter 7: Comparisons of Diversities, OTUs and Taxa among Groups -- Chapter 8: Community Composition Study -- Chapter 9: Modeling Over-dispersed Microbiome Data -- Chapter 10: Linear Regression Modeling metadata -- Chapter 11: Modeling Zero-Inflated Microbiome Data.
330   $aThis unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors? research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.
410  0$aICSA Book Series in Statistics,$x2199-0999
606   $aMathematical statistics$xData processing
606   $aBiometry
606   $aBig data
606   $aStatistics and Computing
606   $aBiostatistics
606   $aBig Data
615  0$aMathematical statistics$xData processing.
615  0$aBiometry.
615  0$aBig data.
615 14$aStatistics and Computing.
615 24$aBiostatistics.
615 24$aBig Data.
676   $a579.16
700   $aXia$b Yinglin$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767992
702   $aSun$b Jun$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aChen$b Ding-Geng$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910300110903321
996   $aStatistical Analysis of Microbiome Data with R$92182078
997   $aUNINA