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Imputation by Bayesian principal components analysis

Source: R/imputations.R
impute.pca_bayes.Rd

Apply imputation to the dataset by Bayesian principal components analysis (Oba et al. 2003) .

Usage

impute.pca_bayes(dataSet, reportImputing = FALSE, nPcs = NULL, maxSteps = 100)

Arguments

dataSet

The 2d dataset of experimental values.

reportImputing

A boolean (default = FALSE) specifying whether to provide a shadow data frame with imputed data labels, where 1 indicates the corresponding entries have been imputed, and 0 indicates otherwise. Alters the return structure.

nPcs

An integer specifying the number of principal components to calculate. The default is set to the minimum between the number of samples and the number of proteins.

maxSteps

An integer (default = 100) specifying the maximum number of estimation steps.

Value

  • If reportImputing = FALSE, the function returns the imputed 2d dataframe.

  • If reportImputing = TRUE, the function returns a list of the imputed 2d dataframe and a shadow matrix showing which proteins by replicate were imputed.

References

Oba S, Sato M, Takemasa I, Monden M, Matsubara K, Ishii S (2003). “A Bayesian Missing Value Estimation Method for Gene Expression Profile Data.” Bioinformatics, 19(16), 2088–2096. doi:10.1093/bioinformatics/btg287 .