SVD Imputation

A wrapper around the pcaMethods::pca(). Impute missing values using singular value decomposition (SVD) imputation. SVD is a matrix factorization technique that factors a matrix into three matrices: U, Σ, and V. SVD is used to find the best lower rank approximation of the original matrix.

Usage

impute_svd(x, by = NULL, ...)

Arguments

x

A glyexp::GlycomicSE(), glyexp::GlycoproteomicSE(), or SummarizedExperiment::SummarizedExperiment() object.

by

Either a column name in sample_info (string) or a factor/vector specifying group assignments for each sample. Used for grouping when imputing missing values.

...

Additional arguments to pass to pcaMethods::pca().

Value

A container of the same class as x, with missing values imputed.