LAUSR

MOTIVATION Principal Component Analysis (PCA) is a key tool in the study of population structure in human genetics. As modern datasets become increasingly larger in size, traditional approaches based on loading the entire dataset in the system memory (RAM) become impractical and out-of-core implementations are the only viable alternative. RESULTS We present TeraPCA, a C ++ implementation of the Randomized Subspace Iteration method to perform PCA of large-scale datasets. TeraPCA can be applied both in-core and out-of-core and is able to successfully operate even on commodity hardware with a system memory of just a few gigabytes. Moreover, TeraPCA has minimal dependencies on external libraries and only requires a working installation of the BLAS and LAPACK libraries. When applied to a dataset containing a million individuals genotyped on a million markers, TeraPCA requires less than five hours (in multi-threaded mode) to accurately compute the ten leading principal components. An extensive experimental analysis shows that TeraPCA is both fast and accurate and is competitive with current state-of-the-art software for the same task. AVAILABILITY Source code and documentation are both available at https://github.com/aritra90/TeraPCA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.