Fast Implementation of (Local) Population Stratification Methods

Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) ). Full support for sparse matrices from the R-package 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix without actually computing the similarity matrices.


Reference manual

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1.7 by Georg Hahn, a year ago

Browse source code at

Authors: Georg Hahn [aut,cre] , Sharon M. Lutz [ctb] , Christoph Lange [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, Rdpack, Matrix, RSpectra

Linking to Rcpp, RcppEigen

See at CRAN