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001 978-981-13-1534-3
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020 _a9789811315343
_9978-981-13-1534-3
024 7 _a10.1007/978-981-13-1534-3
_2doi
040 _cCUS
050 4 _aQA276-280
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
072 7 _aUFM
_2thema
082 0 4 _a519.5
_223
100 1 _aXia, Yinglin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aStatistical Analysis of Microbiome Data with R
_h[electronic resource] /
_cby Yinglin Xia, Jun Sun, Ding-Geng Chen.
250 _a1st ed. 2018.
264 1 _aSingapore :
_bSpringer Singapore :
_bImprint: Springer,
_c2018.
300 _aXXIII, 505 p. 84 illus., 67 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aICSA Book Series in Statistics,
_x2199-0980
505 0 _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.
520 _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.
650 0 _aStatistics .
650 0 _aBig data.
650 1 4 _aStatistics and Computing/Statistics Programs.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S12008
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S17030
650 2 4 _aBig Data.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I29120
700 1 _aSun, Jun.
700 1 _aChen, Ding-Geng.
830 0 _aICSA Book Series in Statistics,
_x2199-0980
856 4 0 _uhttps://doi.org/10.1007/978-981-13-1534-3
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
942 _cEBK
999 _c206211
_d206211