bpFitCPCA {scPCA} | R Documentation |
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. Abid A, Zhang MJ, Bagaria VK, Zou J (2018).
“Exploring patterns enriched in a dataset with contrastive principal
component analysis.”
Nature communications, 9(1), 2134.. Analogous
to fitCPCA
, but replaces all lapply
calls by
bplapply
.
bpFitCPCA(target, center, scale, c_contrasts, contrasts, n_eigen, n_medoids)
target |
The target (experimental) data set, in a standard format such
as a |
center |
A |
scale |
A |
c_contrasts |
A |
contrasts |
A |
n_eigen |
A |
n_medoids |
A |
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 the loadings are not penalized in cPCA