Single-omics SmCCA with Quantitative Phenotype — getRobustWeightsSingle • SmCCNet

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

Usage

getRobustWeightsSingle(
  X1,
  Trait,
  Lambda1,
  s1 = 0.7,
  SubsamplingNum = 1000,
  trace = FALSE
)

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 features in X1 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.
trace: Whether to display the CCA algorithm trace, default is set to FALSE.

Value

A canonical correlation 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



## For illustration, we only subsample 5 times.
set.seed(123)

# Single Omics SmCCA
W1 <- getRobustWeightsSingle(X1, Trait = Y, Lambda1 = 0.05,
  s1 = 0.7, 
  SubsamplingNum = 5, trace = FALSE)