loadings (version 0.5.1)

multipls_rog: 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)

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).

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).

Author

Hiroyuki Yamamoto

Details

Diagonal elements of matrix tau must be 0.

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)