bpFitCPCA: Contrastive Principal Component Analysis in Parallel
In PhilBoileau/scPCA: Sparse Contrastive Principal Component Analysis

bpFitCPCA

R Documentation

Contrastive Principal Component Analysis in Parallel

Description

Given target and background dataframes or matrices, cPCA will perform contrastive principal component analysis (cPCA) of the target data for a given number of eigenvectors and a vector of real valued contrast parameters. This is identical to the implementation of cPCA method by Abid et al. \insertCiteabid2018exploring;textualscPCA. Analogous to fitCPCA, but replaces all lapply calls by bplapply.

Usage

bpFitCPCA(
  target,
  center,
  scale,
  c_contrasts,
  contrasts,
  n_eigen,
  n_medoids,
  eigdecomp_tol,
  eigdecomp_iter
)

Arguments

`target`	The target (experimental) data set, in a standard format such as a `data.frame` or `matrix`.
`center`	A `logical` indicating whether the target and background data sets should be centered to mean zero.
`scale`	A `logical` indicating whether the target and background data sets should be scaled to unit variance.
`c_contrasts`	A `list` of contrastive covariances.
`contrasts`	A `numeric` vector of the contrastive parameters used to compute the contrastive covariances.
`n_eigen`	A `numeric` indicating the number of eigenvectors to be computed.
`n_medoids`	A `numeric` indicating the number of medoids to consider.
`eigdecomp_tol`	A `numeric` providing the level of precision used by eigendecompositon calculations. Defaults to `1e-10`.
`eigdecomp_iter`	A `numeric` indicating the maximum number of interations performed by eigendecompositon calculations. Defaults to `1000`.

Value

A list of lists containing the cPCA results for each contrastive parameter deemed to be a medoid.

rotation - the list of matrices of variable loadings
x - the list of rotated data, centred and scaled if requested, multiplied by the rotation matrix
contrast - the list of contrastive parameters
penalty - set to zero, since loadings are not penalized in cPCA