RT Journal Article SR Electronic T1 Fast Principal Component Analysis of Large-Scale Genome-Wide Data JF bioRxiv FD Cold Spring Harbor Laboratory SP 002238 DO 10.1101/002238 A1 Gad Abraham A1 Michael Inouye YR 2014 UL http://biorxiv.org/content/early/2014/01/30/002238.abstract AB 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 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.