Fast Implementation of (Local) Population Stratification Methods
Computation of the k leading eigenvectors of the genomic relationship ...
Computation of the k leading eigenvectors of the Jaccard similarity ma...
Computation of the k leading eigenvectors of the s-matrix (the weighte...
C++ implementation to compute the covariance matrix for a (sparse) inp...
Computation of the k leading eigenvectors of the covariance matrix for...
Computation of the k leading eigenvectors of the covariance matrix dir...
Computation of the k leading eigenvectors of the genomic relationship ...
Computation of the k leading eigenvectors of the Jaccard similarity ma...
Computation of the k leading eigenvectors of the s-matrix (the weighte...
A full scan of the input data m using a collection of windows given ...
C++ implementation to compute the genomic relationship matrix (grm) fo...
C++ implementation to compute the Jaccard similarity matrix for a (spa...
Auxiliary function to generate a two-column matrix of windows to be us...
C++ implementation of the power method (von Mises iteration) to comput...
Auxiliary function to invert minor alleles and to select those variant...
C++ implementation to compute the s-matrix (the weighted Jaccard simil...
Simulated test data.
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) <doi:10.1002/gepi.22356>). 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 via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from 'bed'+'bim'+'fam' files.