Fast principal component analysis of large-scale genome-wide data.

Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time cons...

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Autores principales:	Gad Abraham, Michael Inouye
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7c52eb15209244efa849360d96256343
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spelling	oai:doaj.org-article:7c52eb15209244efa849360d962563432021-11-18T08:24:13ZFast principal component analysis of large-scale genome-wide data.1932-620310.1371/journal.pone.0093766https://doaj.org/article/7c52eb15209244efa849360d962563432014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24718290/?tool=EBIhttps://doaj.org/toc/1932-6203Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.Gad AbrahamMichael InouyePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e93766 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gad Abraham Michael Inouye Fast principal component analysis of large-scale genome-wide data.
description	Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.
format	article
author	Gad Abraham Michael Inouye
author_facet	Gad Abraham Michael Inouye
author_sort	Gad Abraham
title	Fast principal component analysis of large-scale genome-wide data.
title_short	Fast principal component analysis of large-scale genome-wide data.
title_full	Fast principal component analysis of large-scale genome-wide data.
title_fullStr	Fast principal component analysis of large-scale genome-wide data.
title_full_unstemmed	Fast principal component analysis of large-scale genome-wide data.
title_sort	fast principal component analysis of large-scale genome-wide data.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/7c52eb15209244efa849360d96256343
work_keys_str_mv	AT gadabraham fastprincipalcomponentanalysisoflargescalegenomewidedata AT michaelinouye fastprincipalcomponentanalysisoflargescalegenomewidedata
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Fast principal component analysis of large-scale genome-wide data.

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