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PCA for gen_tibble objects by partial SVD

gt_pca_partialSVD.Rd

This function performs Principal Component Analysis on a gen_tibble, by partial SVD through the eigen decomposition of the covariance. It works well if the number of individuals is much smaller than the number of loci; otherwise, gt_pca_randomSVD() is a better option. This function is a wrapper for bigstatsr::big_SVD().

Usage

gt_pca_partialSVD(x, k = 10, fun_scaling = bigsnpr::snp_scaleBinom())

Arguments

x

a gen_tbl object

k

Number of singular vectors/values to compute. Default is 10. This algorithm should be used to compute a few singular vectors/values.

fun_scaling

Usually this can be left unset, as it defaults to bigsnpr::snp_scaleBinom(), which is the appropriate function for biallelic SNPs. Alternatively it is possible to use custom function (see bigsnpr::snp_autoSVD() for details.

Value

a gt_pca object, which is a subclass of bigSVD; this is an S3 list with elements: A named list (an S3 class "big_SVD") of

  • d, the eigenvalues (singular values, i.e. as variances),

  • u, the scores for each sample on each component (the left singular vectors)

  • v, the loadings (the right singular vectors)

  • center, the centering vector,

  • scale, the scaling vector,

  • method, a string defining the method (in this case 'partialSVD'),

  • call, the call that generated the object.

Note: rather than accessing these elements directly, it is better to use tidy and augment. See gt_pca_tidiers.