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https://hdl.handle.net/1959.11/14595| Title: | R for Genome-Wide Association Studies | Contributor(s): | Gondro, Cedric (author) ; Porto-Neto, Laercio R (author); Lee, S H (author) |
Publication Date: | 2013 | DOI: | 10.1007/978-1-62703-447-0_1 | Handle Link: | https://hdl.handle.net/1959.11/14595 | Abstract: | In recent years R has become de facto statistical programming language of choice for statisticians and it is also arguably the most widely used generic environment for analysis of high-throughput genomic data. In this chapter we discuss some approaches to improve performance of R when working with large SNP datasets. | Publication Type: | Book Chapter | Source of Publication: | Genome-Wide Association Studies and Genomic Predictions, p. 1-17 | Publisher: | Humana Press | Place of Publication: | New York, United States of America | ISBN: | 9781627034470 9781627034463 |
Fields of Research (FoR) 2008: | 060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics) | Fields of Research (FoR) 2020: | 310506 Gene mapping | Socio-Economic Objective (SEO) 2008: | 970106 Expanding Knowledge in the Biological Sciences | Socio-Economic Objective (SEO) 2020: | 280102 Expanding knowledge in the biological sciences | HERDC Category Description: | B1 Chapter in a Scholarly Book | Publisher/associated links: | http://trove.nla.gov.au/version/198468706 | Series Name: | Methods in Molecular Biology | Series Number : | 1019 | Editor: | Editor(s): Cedric Gondro, Julius van der Werf, Ben Hayes |
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| Appears in Collections: | Book Chapter |
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