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

Source: R/MultiOmicsSmCCA.R
getRobustWeightsMulti.Rd

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 \(i\)th 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)