Omic Association Studies with R and Bioconductor
1st Edition
Published on 14. June 2019
390 pages
978-0-429-80336-9 (ISBN)
Description
After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data.
Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data
Uses up-to-date methods to exploit omic data
Presents methods through specific examples and computing sessions
Supplemented by a website, including code, datasets, and solutions
Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data
Uses up-to-date methods to exploit omic data
Presents methods through specific examples and computing sessions
Supplemented by a website, including code, datasets, and solutions
Reviews / Votes
"This book is a good tool for self-learning analytical strategies for omics data. It requires previous knowledge of R and focuses on getting things done...I think the book would be a good reference for masters or PhD students that have to perform their analysis and need a starting point. Also, for the practicing statistician working with omics data."- Victor Moreno, ISCB News, July 2020
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
Illustrations
5 Tables, black and white; 100 Illustrations, black and white
ISBN-13
978-0-429-80336-9 (9780429803369)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Juan R. Gonzalez | Alejandro Caceres
Omic Association Studies with R and Bioconductor
Book
12/2020
1st Edition
Chapman & Hall/CRC
€64.36
Shipment within 15-20 days
Juan R. Gonzalez | Alejandro Caceres
Omic Association Studies with R and Bioconductor
Book
06/2019
1st Edition
CRC Press
€172.00
Shipment within 10-20 days
Persons
Juan R. Gonzalez is an Associate Research Professor leading the Bioinformatics Research Group in Epidemiology at Barcelona Institute for Global Health. He has published extensively on methods and bioinformatics tools to detect structural variants from genomic data and to perform different types of omic association studies. Dr. Gonzalez is the author of a large number of R and Bioconductor packages including state-of-the-art libraries such as SNPassoc or MAD that have been used to discover new susceptibility genetic factor for complex diseases.
Alejandro Caceres is a Senior Statistician in the Bioinformatics Research Group in Epidemiology at Barcelona Institute for Global Health. He has large experience in developing new statistical methods to exploit genomic, transcriptomic and epigenomic data obtained from public repositories. Dr. Caceres is the author of several R and Bioconductor packages that have been used, for instance, to study the role of polymorphic genomic inversions in complex diseases or to investigate how the downregulation of chromosome Y may affect age-related diseases.
Alejandro Caceres is a Senior Statistician in the Bioinformatics Research Group in Epidemiology at Barcelona Institute for Global Health. He has large experience in developing new statistical methods to exploit genomic, transcriptomic and epigenomic data obtained from public repositories. Dr. Caceres is the author of several R and Bioconductor packages that have been used, for instance, to study the role of polymorphic genomic inversions in complex diseases or to investigate how the downregulation of chromosome Y may affect age-related diseases.
Content
1 Introduction 2 Case examples 3 Dealing with omic data in Bioconductor 4 Genetic association studies 5 Genomic variant studies 6 Adressing batch effects 7 Transcriptomic studies 8 Epigenomic studies 9 Exposomic analysis 10 Enrichment analysis 11 Multiomic data analysis
