Skip to contents

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

Source: R/SmCCNet-source.R
getRobustWeightsMultiBinary.Rd

SmCCNet algorithm with multi-omics data and binary phenotype. This is a stepwise approach (1) use SmCCA to identify relationship between omics (exlude phenotype), (2) within highly connected omics features selected in step 1, identify relationship between these selected omics features and phenotype of interest with sparse PLS. First, it computes PLSDA by assuming outcome is continuous to extract multiple latent factors, then uses latent factors to fit logistic regression, and weight latent factor by regression parameters. Refer to multi-omics vignette for more detail.

Usage

getRobustWeightsMultiBinary(
  X,
  Y,
  Between_Discriminate_Ratio = c(1, 1),
  SubsamplingPercent = NULL,
  CCcoef = NULL,
  LambdaBetween,
  LambdaPheno = NULL,
  SubsamplingNum = 1000,
  ncomp_pls = 3,
  EvalClassifier = FALSE,
  testData = NULL
)

Arguments

X

A list of omics data each with n subjects.

Y

A vector of binary variable, user needs to set the level of this variable to 0 and 1.

Between_Discriminate_Ratio

A vector with length 2 specifying the relative importance of between-omics relationship and omics-phenotype relationship. For instance a ratio of 1:1 (c(1,1) in the argument) means between-omics relationship and omics-phenotype relationship contribute equally to the canonical weights extraction.

SubsamplingPercent

A vector with length equal to the number of omics data (X), specifying the percentage of omics feature being subsampled at each subsampling iteration.

CCcoef

A vector of scaling factors only for between-omics relationship (exclude omics-phenotype). This coefficient vector follows the column order of combn(T, 2) when there are T omics data.

LambdaBetween

A vector of sparsity penalty value for each omics data to run the between-omics SmCCA, each penalty term should be within the range of 0 and 1.

LambdaPheno

A penalty term when running the sparse PLS with phenotype, penalty term should be within the range of 0 and 1.

SubsamplingNum

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

ncomp_pls

Number of latent components for PLS, default set to 3.

EvalClassifier

If TRUE, the algorithm is at the phase of evaluating classification performance, and the latent factors from SPLSDA will be returned; if FALSE, the algorithm is at the phase of constructing multi-omics network, canonical weight will be returned. Default is set to FALSE.

testData

A list of testing omics data matrix, should have the exact same order as data list X, only used when EvalClassifier is set to TRUE for performing cross-validation, refer to multi-omics vignette for detail.

Value

If EvalClassifier is set to FALSE, a canonical correlation weight matrix is returned with combined omics data. Each column is the canonical correlation weights based on subsampled X features. The number of columns is SubsamplingNum. If EvalClassifier is set to TRUE, then latent factors from training and testing data will be returned for classifier evaluation.

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_binary <- rbinom(60,1,0.5)

Ws <- getRobustWeightsMultiBinary(list(X1,X2), Y_binary, 
      SubsamplingPercent = c(0.8,0.8), CCcoef = NULL,
      LambdaBetween = c(0.5,0.5), LambdaPheno = 0.1, SubsamplingNum = 10)