東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Primer to analysis of genomic Data u...

Gondro, Cedric.

FindBook

Google Book

Amazon

博客來

Primer to analysis of genomic Data using R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Primer to analysis of genomic Data using R/ by Cedric Gondro.
作者:	Gondro, Cedric.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	xvi, 270 p. :ill. (some col.), digital ;24 cm.
內容註:	R basics -- Simple marker association tests -- Genome wide association studies -- Population and genetic architecture -- Gene expression analysis -- Databases and functional information -- Extending R -- Final comments -- Index -- References.
Contained By:	Springer eBooks
標題:	Genomics - Statistical methods. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-14475-7
ISBN:	9783319144757 (electronic bk.)

Primer to analysis of genomic Data using R
Gondro, Cedric.

Primer to analysis of genomic Data using R[electronic resource] /by Cedric Gondro. - Cham :Springer International Publishing :2015. - xvi, 270 p. :ill. (some col.), digital ;24 cm. - Use R!,2197-5736. - Use R!.

R basics -- Simple marker association tests -- Genome wide association studies -- Population and genetic architecture -- Gene expression analysis -- Databases and functional information -- Extending R -- Final comments -- Index -- References.

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics or for use in lab sessions. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher's website. Chapters show how to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R. A wide range of R packages useful for working with genomic data are illustrated with practical examples. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in the analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. At a time when genomic data is decidedly big, the skills from this book are critical. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection; population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data.

ISBN: 9783319144757 (electronic bk.)

Standard No.: 10.1007/978-3-319-14475-7doiSubjects--Topical Terms:

751074
Genomics
--Statistical methods.

LC Class. No.: QH438.4.S73

Dewey Class. No.: 576.50727

Primer to analysis of genomic Data using R
LDR:03719nmm a2200337 a 4500 001 2006514
003 DE-He213
005 20160105110824.0
006 m d
007 cr nn 008maaau
008 160114s2015 gw s 0 eng d
020 $a 9783319144757 (electronic bk.)
020 $a 9783319144740 (paper)
024 7 $a 10.1007/978-3-319-14475-7 $2 doi
035 $a 978-3-319-14475-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QH438.4.S73
072 7 $a PBT $2 bicssc
072 7 $a MBNS $2 bicssc
072 7 $a MED090000 $2 bisacsh
082 0 4 $a 576.50727 $2 23
090 $a QH438.4.S73 $b G637 2015
100 1 $a Gondro, Cedric. $3 2153478
245 1 0 $a Primer to analysis of genomic Data using R $h [electronic resource] / $c by Cedric Gondro.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xvi, 270 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Use R!, $x 2197-5736
505 0 $a R basics -- Simple marker association tests -- Genome wide association studies -- Population and genetic architecture -- Gene expression analysis -- Databases and functional information -- Extending R -- Final comments -- Index -- References.
520 $a Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics or for use in lab sessions. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher's website. Chapters show how to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R. A wide range of R packages useful for working with genomic data are illustrated with practical examples. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in the analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. At a time when genomic data is decidedly big, the skills from this book are critical. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection; population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data.
650 0 $a Genomics $x Statistical methods. $3 751074
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Statistics. $3 517247
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 891086
650 2 4 $a Statistics and Computing/Statistics Programs. $3 894293
650 2 4 $a Gene Expression. $3 600530
650 2 4 $a Microarrays. $3 1067309
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Use R! $3 939293
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-14475-7
950 $a Mathematics and Statistics (Springer-11649)