語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Statistical analysis of microbiome d...
~
Chen, Ding-Geng.
Statistical analysis of microbiome data with R
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Statistical analysis of microbiome data with Rby Yinglin Xia, Jun Sun, Ding-Geng Chen.
作者:
Xia, Yinglin.
其他作者:
Sun, Jun.
出版者:
Singapore :Springer Singapore :2018.
面頁冊數:
xxiii, 505 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
MicrobiologyResearch.
電子資源:
http://dx.doi.org/10.1007/978-981-13-1534-3
ISBN:
9789811315343$q(electronic bk.)
Statistical analysis of microbiome data with R
Xia, Yinglin.
Statistical analysis of microbiome data with R
[electronic resource] /by Yinglin Xia, Jun Sun, Ding-Geng Chen. - Singapore :Springer Singapore :2018. - xxiii, 505 p. :ill., digital ;24 cm. - ICSA book series in statistics,2199-0980. - ICSA book series in statistics..
Chapter 1: Introduction to R, RStudio and ggplot2 -- Chapter 2: What are Microbiome Data? -- Chapter 3: Bioinformatic and Statistical Analyses of Microbiome Data -- Chapter 4: Power and Sample Size Calculation in Hypothesis Testing Microbiome Data -- Chapter 5: Microbiome Data Management -- Chapter 6: Exploratory Analysis of Microbiome Data -- Chapter 7: Comparisons of Diversities, OTUs and Taxa among Groups -- Chapter 8: Community Composition Study -- Chapter 9: Modeling Over-dispersed Microbiome Data -- Chapter 10: Linear Regression Modeling metadata -- Chapter 11: Modeling Zero-Inflated Microbiome Data.
This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors' research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.
ISBN: 9789811315343$q(electronic bk.)
Standard No.: 10.1007/978-981-13-1534-3doiSubjects--Topical Terms:
275232
Microbiology
--Research.
LC Class. No.: QR62 / .X539 2018
Dewey Class. No.: 579
Statistical analysis of microbiome data with R
LDR
:02505nmm a2200325 a 4500
001
544733
003
DE-He213
005
20190314150250.0
006
m d
007
cr nn 008maaau
008
190508s2018 si s 0 eng d
020
$a
9789811315343$q(electronic bk.)
020
$a
9789811315336$q(paper)
024
7
$a
10.1007/978-981-13-1534-3
$2
doi
035
$a
978-981-13-1534-3
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QR62
$b
.X539 2018
072
7
$a
UFM
$2
bicssc
072
7
$a
COM077000
$2
bisacsh
082
0 4
$a
579
$2
23
090
$a
QR62
$b
.X6 2018
100
1
$a
Xia, Yinglin.
$3
823416
245
1 0
$a
Statistical analysis of microbiome data with R
$h
[electronic resource] /
$c
by Yinglin Xia, Jun Sun, Ding-Geng Chen.
260
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2018.
300
$a
xxiii, 505 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
ICSA book series in statistics,
$x
2199-0980
505
0
$a
Chapter 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
$a
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.
650
0
$a
Microbiology
$x
Research.
$3
275232
650
0
$a
Microbiology
$x
Analysis.
$3
823417
650
0
$a
Microbiology
$3
253095
650
0
$a
Microbiology
$x
Data processing.
$3
823418
650
1 4
$a
Statistics.
$3
182057
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
275710
650
2 4
$a
Statistics for Life Sciences, Medicine, Health Sciences.
$3
274067
650
2 4
$a
Big Data.
$3
760530
700
1
$a
Sun, Jun.
$3
680301
700
1
$a
Chen, Ding-Geng.
$3
487343
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
ICSA book series in statistics.
$3
725077
856
4 0
$u
http://dx.doi.org/10.1007/978-981-13-1534-3
950
$a
Mathematics and Statistics (Springer-11649)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000162177
電子館藏
1圖書
電子書
EB QR62 .X6 2018 2018
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://dx.doi.org/10.1007/978-981-13-1534-3
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入