TeraPCA: a fast and scalable software package to study genetic variation in tera-scale genotypes

Abstract

Motivation Principal Component Analysis 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 (Random Access Memory) 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 Principal Component Analysis 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 <5 h (in multi-threaded mode) to accurately compute the 10 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 and implementation Source code and documentation are both available at https://github.com/aritra90/TeraPCA. Supplementary information Supplementary data are available at Bioinformatics online.

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

“…There are also many focuses of recent PCA algorithms (Additional file 19). The randomized subspace iteration algorithm, which is a hybrid of Krylov and Rand methodologies, was developed based on randomized SVD [133,134]. In pass-efficient or one-pass randomized SVD, some tricks to reduce the number of passes have been considered [135,136].…”

Efficient toolkit implementing best practices for principal component analysis of population genetic data

“…Recently, the advent of large population-scale genetic datasets, such as the UK biobank data, has prompted research on developing scalable algorithms to compute PCA on very large data (Bycroft et al 2018). It is now possible to efficiently approximate PCA on very large datasets thanks to software such as FastPCA (fast mode of EIGENSOFT), FlashPCA2, PLINK 2.0 (approx mode), bigstatsr/bigsnpr, TeraPCA and ProPCA (Galinsky et al 2016; Abraham et al 2017; Chang et al 2015; Privé et al 2018; Bose et al 2019; Agrawal et al 2019).…”

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

“…There are also many focuses of recent PCA algorithms (Additional file 23). The randomized subspace iteration algorithm, which is a hybrid of Krylov and Rand methodologies, was developed based on randomized SVD [133,134]. In pass-efficient or one-pass randomized SVD, some tricks to reduce the number of passes have been considered [135,136].…”