multipls_rog: Multiset PLS-ROG : Multiset partial least squares with rank...

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

Multiset PLS-ROG : Multiset partial least squares with rank order of groups

Description

This function performs multiset partial least squares with rank order of groups (Multiset PLS-ROG). In this function, data matrix is automatically scaled to zero mean and unit variance (i.e. autoscaling) for each variables.

Usage

multipls_rog(X,Y,tau,D,kappa)

Arguments

X	List of data matrix that include variables in each columns.
Y	Dummy matrix that include group information 0,1 in each columns.
tau	Matrix for strength parameter of the connection between omics datasets or between omics dataset and group information.
D	Differential matrix.
kappa	The smoothing parameter (default : 0.999).

Details

Diagonal elements of matrix tau must be 0.

Value

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

P : A list of matrix with Multiset PLS-ROG coefficients for the explanatory variables in each column for each dataset

T : A list of matrix with Multiset PLS-ROG scores for the explanatory variables in each column for each dataset

Q : A matrix with Multiset PLS-ROG coefficients for the response variable in each column

U : A matrix with Multiset PLS-ROG scores for the response variable in each column

tau : Matrix for strength parameter of the connection between omics datasets or between omics dataset and group information (same as input value).

Author(s)

Hiroyuki Yamamoto

References

Yamamoto H. (2022) Multiset partial least squares with rank order of groups for integrating multi-omics data, bioRxiv.

Examples

data(whhl)
X <- whhl$X
Y <- whhl$Y
D <- whhl$D
tau <- whhl$tau

multiplsrog <- multipls_rog(X,Y,tau,D)
# multiplsrog <- multipls_rog(X,Y,tau,D, kappa=0.999)

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