bed_fastSMatrixEVs: Computation of the k leading eigenvectors of the s-matrix...

In locStra: Fast Implementation of (Local) Population Stratification Methods

Computation of the k leading eigenvectors of the s-matrix (the weighted Jaccard similarity matrix) directly from a bed+bim+fam file. Note that in contrast to the parameters of the function sMatrix, the choice phased=FALSE cannot be modified for the fast eigenvector computation. Moreover, inverting the minor allele is not possible when reading directly from external files.

Description

Computation of the k leading eigenvectors of the s-matrix (the weighted Jaccard similarity matrix) directly from a bed+bim+fam file. Note that in contrast to the parameters of the function sMatrix, the choice phased=FALSE cannot be modified for the fast eigenvector computation. Moreover, inverting the minor allele is not possible when reading directly from external files.

Usage

bed_fastSMatrixEVs(f, k, Djac = FALSE, q = 2)

Arguments

f	The filename of the bed file (including its extension). The bim and fam files need to be in the same folder and have the same base filename.
k	The number of leading eigenvectors.
Djac	Flag to switch between the unweighted (Djac=TRUE) or weighted (Djac=FALSE) version. Default is Djac=FALSE.
q	The number of power iteration steps (default is q=2).

Value

The k leading eigenvectors of the s-matrix of m as a column matrix.

References

Daniel Schlauch (2016). Implementation of the stego algorithm - Similarity Test for Estimating Genetic Outliers. https://github.com/dschlauch/stego

N. Halko, P.G. Martinsson, and J.A. Tropp (2011). Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. SIAM Review: 53(2), pp. 217–288.

F. Prive, M. Blum, H. Aschard, B.J. Vilhjalmsson (2022). bigsnpr: Analysis of Massive SNP Arrays. https://cran.r-project.org/package=bigsnpr

Examples

require(locStra)

locStra documentation built on April 13, 2022, 1:07 a.m.