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Imputation by probabilistic principal components analysis

impute.pca_prob.Rd

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

Usage

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

Arguments

dataSet

A data frame containing the data signals.

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

The imputed data.

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 .