BPCA Imputation

A wrapper around the pcaMethods::pca(). Impute missing values using Bayesian principal component analysis (BPCA). BPCA combines an EM approach for PCA with a Bayesian model. In standard PCA data far from the training set but close to the principal subspace may have the same reconstruction error. BPCA defines a likelihood function such that the likelihood for data far from the training set is much lower, even if they are close to the principal subspace.

Usage

impute_bpca(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.