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, ...)

# S3 method for class 'glyexp_experiment'
impute_svd(x, by = NULL, ...)

# S3 method for class 'matrix'
impute_svd(x, by = NULL, ...)

# Default S3 method
impute_svd(x, by = NULL, ...)

Arguments

x

Either a glyexp_experiment object or a matrix. If a matrix, rows should be variables and columns should be samples.

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

Returns the same type as the input. If x is a glyexp_experiment, returns a glyexp_experiment with missing values imputed. If x is a matrix, returns a matrix with missing values imputed.