loadings (version 0.5.1)

os_pca: Orthogonal smoothed principal component analysis

Description

This function performs orthogonal smoothed principal component analysis (OS-PCA). In this function, data matrix is automatically scaled to zero mean and unit variance (i.e. autoscaling) for each variables.

Usage

os_pca(X,D,kappa,M)

Value

The return value is a list object that contains the following elements:

P : A matrix with OS-PC loading in each column

T : A matrix with OS-PC score in each column

MT : A matrix with averaging OS-PC score for repeated data in each column (If not for repeated data, the matrix MT equals to the matrix T)

Q : A matrix with OS-PC loading for auxiliary variable in each column

U : A matrix with OS-PC score for auxiliary variable in each column

Arguments

X

Data matrix that include variables in each columns.

D

Differential matrix.

kappa

The smoothing parameter (default : 0.999).

M

Averaging matrix for repeated data (default : Identity matrix).

Author

Hiroyuki Yamamoto

Details

The kappa represents the degree of smoothing. The value of kappa increases, the strength of the smoothing increases.

References

Yamamoto H., Nakayama Y., Tsugawa H. (2021) OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data, Metabolites, 11(3):149.

Examples

# metabolic turnover data
data(turnover)

X <- turnover$X
D <- turnover$D

ospca <- os_pca(X,D,0.999)

# metabolome data
data(greentea)

X <- greentea$X
D <- greentea$D
M <- greentea$M

ospca <- os_pca(X,D,0.1,M)