Primer to Analysis of Genomic Data Using R
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 a
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Cedric Gondro
Primer to Analysis of Genomic Data Using R
Use R! Series Editors: Robert Gentleman Kurt Hornik Giovanni Parmigiani
More information about this series at http://www.springer.com/series/6991
Use R! Kolaczyk / Csárdi: Statistical Analysis of Network Data with R (2014) Nolan / Temple Lang: XML and Web Technologies for Data Sciences with R (2014) Willekens: Multistate Analysis of Life Histories with R (2014) Cortez: Modern Optimization with R (2014) Eddelbuettel: Seamless R and C++ Integration with Rcpp (2013) Bivand / Pebesma / Gómez-Rubio: Applied Spatial Data Analysis with R (2nd ed. 2013) van den Boogaart / Tolosana-Delgado: Analyzing Compositional Data with R (2013) Nagarajan / Scutari / Lèbre: Bayesian Networks in R (2013)
Cedric Gondro
Primer to Analysis of Genomic Data Using R
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Cedric Gondro Ctr. Genetic Analysis and Applications University of New England Armidale, NSW, Australia
. ISSN 2197-5736 Use R! ISBN 978-3-319-14474-0 DOI 10.1007/978-3-319-14475-7
ISSN 2197-5744 (electronic) ISBN 978-3-319-14475-7 (eBook)
Library of Congress Control Number: 2015934220 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
To Placido, Walburga, Simone and Sean
Preface
Overview Just about any text written on the analysis of genomic data will begin by mentioning the rapid pace of changes in the field. How the technology is frantically moving forward and how datasets are getting bigger and bigger. A huge experiment one year is just a tiny proof of concept the following year. Databases are growing exponentially. The literature on even quite specific subjects is overwhelming and we have to decide if we are going to keep up to date or actually get some of the work done. It feels that just a few years ago, a genome scan
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