Overview
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
Full Product Details
Author: Juan R. González ,
Alejandro Cáceres
Publisher: Taylor & Francis Ltd
Imprint: Chapman & Hall/CRC
Weight: 0.720kg
ISBN: 9780367728106
ISBN 10: 0367728109
Pages: 376
Publication Date: 18 December 2020
Audience:
College/higher education
,
General/trade
,
Tertiary & Higher Education
,
General
Format: Paperback
Publisher's Status: Active
Availability: In Print
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Reviews
"""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 ""Omic Association Studies with R and Bioconductor is an excellent tool book for those looking to have hands-on guidance to analyze multiomics datasets using established packages. The authors provide comprehensive examples of using genomic, transcriptomic, epigenomic, and exposomic data, as well as their integration, to generate biological hypotheses and explore individual heterogeneity."" – Biometrics"
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
Author Information
Juan R. González 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. González 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. Cáceres 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.
