getRobustWeightsMulti: Run Sparse multiple Canonical Correlation Analysis and Obtain...

In KechrisLab/SmCCNet: Sparse Multiple Canonical Correlation Network Analysis Tool

Run Sparse multiple Canonical Correlation Analysis and Obtain Canonical Weights (with Subsampling)

Description

SmCCNet algorithm with multi-omics data and quantitative phenotype. Calculate the canonical weights for SmCCA.

Usage

getRobustWeightsMulti(
  X,
  Trait,
  Lambda,
  s = NULL,
  NoTrait = FALSE,
  SubsamplingNum = 1000,
  CCcoef = NULL,
  trace = FALSE,
  TraitWeight = FALSE
)

Arguments

X	A list of omics data each with n subjects.
Trait	An n\times 1 trait (phenotype) data matrix for the same n subjects.
Lambda	Lasso penalty vector with length equals to the number of omics data (X). Lambda needs to be between 0 and 1.
s	A vector with length equals to the number of omics data (X), specifying the percentage of omics feature being subsampled at each subsampling iteration.
NoTrait	Logical, default is FALSE. Whether trait information is provided.
SubsamplingNum	Number of feature subsamples. Default is 1000. Larger number leads to more accurate results, but at a higher computational cost.
CCcoef	Optional scaling factors for the SmCCA pairwise canonical correlations. If CCcoef = NULL (default), then the objective function is the total sum of all pairwise canonical correlations. This coefficient vector follows the column order of combn(T+1, 2) assuming there are T omics data and a phenotype data.
trace	Whether to display the CCA algorithm trace, default is set to FALSE.
TraitWeight	Whether to return canonical weight for trait (phenotype), default is set to FALSE.

Value

A canonical correlation weight matrix with p = \sum_{i} p_i rows, where p_i is the number of features for the ith omics. Each column is the canonical correlation weights based on subsampled features. The number of columns is SubsamplingNum.

Examples

## For illustration, we only subsample 5 times.
set.seed(123)
X1 <- matrix(rnorm(600,0,1), nrow = 60)
X2 <- matrix(rnorm(600,0,1), nrow = 60)
Y <- matrix(rnorm(60,0,1), nrow = 60)
# Unweighted SmCCA
result <- getRobustWeightsMulti(X = list(X1, X2), Trait = Y, NoTrait = FALSE,
Lambda = c(0.5, 0.5),s = c(0.7, 0.7), SubsamplingNum = 20)
  

KechrisLab/SmCCNet documentation built on April 18, 2024, 9:46 p.m.