os_pca: Orthogonal smoothed principal component analysis

In loadings: Loadings for Principal Component Analysis and Partial Least Squares

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)

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).

Details

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

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

Author(s)

Hiroyuki Yamamoto

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)

loadings documentation built on May 29, 2024, 8:01 a.m.