unsv_multipls: Unsupervised multiset partial least squares

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

Unsupervised multiset partial least squares

Description

This function performs Unsupervised multiset partial least squares. In this function, data matrix is automatically scaled to zero mean and unit variance (i.e. autoscaling) for each variables.

Usage

unsv_multipls(X,tau)

Arguments

X	List of data matrix that include variables in each columns.
tau	Matrix for strength parameter of the connection between omics datasets or between omics dataset and group information.

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 coefficients in each column for each dataset

T : A list of matrix with Multiset PLS scores in each column for each dataset

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
tau <- rbind(c(0,1/4,1/4,1/4),c(1/4,0,1/4,1/4),c(1/4,1/4,0,1/4),c(1/4,1/4,1/4,0))

unsvmpls <- unsv_multipls(X,tau)

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