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

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.