Single-omics SmCCA with Binary Phenotype

Compute aggregated (SmCCA) canonical weights for single omics data with quantitative phenotype (subampling enabled).

Usage

getRobustWeightsSingleBinary(
  X1,
  Trait,
  Lambda1,
  s1 = 0.7,
  SubsamplingNum = 1000,
  K = 3
)

Arguments

X1

An \(n\times p_1\) data matrix (e.g. mRNA) with \(p_1\) features and \(n\) subjects.

Trait

An \(n\times 1\) trait (phenotype) data matrix for the same \(n\) subjects.

Lambda1

LASSO penalty parameter for X1. Lambda1 needs to be between 0 and 1.

s1

Proportion of mRNA features to be included, default at s1 = 0.7. s1 needs to be between 0 and 1, default is set to 0.7.

SubsamplingNum

Number of feature subsamples. Default is 1000. Larger number leads to more accurate results, but at a higher computational cost.

K

Number of hidden components for PLSDA, default is set to 3.

Value

A partial least squared weight matrix with \(p_1\) rows. Each column is the canonical correlation weights based on subsampled X1

features. The number of columns is SubsamplingNum.

Examples

X <- matrix(rnorm(600,0,1), nrow = 60)
Y <- rbinom(60,1,0.5)
Ws <- getRobustWeightsSingleBinary(X1 = X, Trait = as.matrix(Y), Lambda1 = 0.8, 
0.7, SubsamplingNum = 10)