000 | 03422nam a22004935i 4500 | ||
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001 | 978-981-13-1534-3 | ||
003 | DE-He213 | ||
005 | 20200812132037.0 | ||
007 | cr nn 008mamaa | ||
008 | 181006s2018 si | s |||| 0|eng d | ||
020 |
_a9789811315343 _9978-981-13-1534-3 |
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024 | 7 |
_a10.1007/978-981-13-1534-3 _2doi |
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040 | _cCUS | ||
050 | 4 | _aQA276-280 | |
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_aUFM _2bicssc |
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_aCOM077000 _2bisacsh |
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_aUFM _2thema |
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_a519.5 _223 |
100 | 1 |
_aXia, Yinglin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXXIII, 505 p. 84 illus., 67 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aICSA Book Series in Statistics, _x2199-0980 |
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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 |
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912 | _aZDB-2-SXMS | ||
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_c206211 _d206211 |