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Imputation by probabilistic principal components analysis

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

Apply imputation to the dataset by probabilistic principal components analysis (Stacklies et al. 2007) .

Usage

impute.pca_prob(
  dataSet,
  reportImputing = FALSE,
  nPcs = NULL,
  maxIterations = 1000,
  seed = 362436069
)

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.

maxIterations

An integer (default = 1000) specifying the maximum number of allowed iterations.

seed

An integer (default = 362436069) specifying the seed used for the random number generator for reproducibility.

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

Stacklies W, Redestig H, Scholz M, Walther D, Selbig J (2007). “pcaMethods–A Bioconductor Package Providing PCA Methods for Incomplete Data.” Bioinformatics, 23(9), 1164–1167. doi:10.1093/bioinformatics/btm069 .