cv.PCLasso {PCLassoReg}

Cross-validation for PCLasso

Description

Perform k-fold cross validations for the PCLasso model with grouped covariates over a grid of values for the regularization parameter lambda.

Usage

cv.PCLasso(
  x,
  y,
  group,
  penalty = c("grLasso", "grMCP", "grSCAD"),
  nfolds = 5,
  standardize = TRUE,
  ...
)

Arguments

x

A n x p design matrix of gene/protein expression measurements with n samples and p genes/proteins, as in PCLasso.

y

The time-to-event outcome, as a two-column matrix or Surv object, as in PCLasso. The first column should be time on study (follow up time); the second column should be a binary variable with 1 indicating that the event has occurred and 0 indicating (right) censoring.

group

A list of groups as in PCLasso. The feature (gene/protein) names in group should be consistent with the feature (gene/protein) names in x.

penalty

The penalty to be applied to the model. For group selection, one of grLasso, grMCP, or grSCAD. For bi-level selection, one of gel or cMCP. See grpsurv in the R package grpreg for details.

nfolds

The number of cross-validation folds. Default is 5.

standardize

Logical flag for x standardization, prior to fitting the model. Default is TRUE.

...

Arguments to be passed to cv.grpsurv in the R package grpreg.

Details

The function calls PCLasso nfolds times, each time leaving out 1/nfolds of the data. The cross-validation error is based on the deviance. The numbers for censored samples are balanced across the folds. cv.PCLasso uses the approach of calculating the full Cox partial likelihood using the cross-validated set of linear predictors. See cv.grpsurv in the R package grpreg for details.

Value

An object with S3 class "cv.PCLasso" containing:

cv.fit

An object of class "cv.grpsurv".

complexes.dt

Complexes with features (genes/proteins) not included in x being filtered out.

Author(s)

Wei Liu

References

PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery. Brief Bioinform, 2021.

Park, H., Niida, A., Miyano, S. and Imoto, S. (2015) Sparse overlapping group lasso for integrative multi-omics analysis. Journal of computational biology: a journal of computational molecular cell biology, 22, 73-84.

See Also

predict.cv.PCLasso

Examples

# load data
data(survivalData)
data(PCGroups)

x = survivalData$Exp
y = survivalData$survData

PC.Human <- getPCGroups(Groups = PCGroups, Organism = "Human",
Type = "EntrezID")

# fit model
cv.fit1 <- cv.PCLasso(x, y, group = PC.Human, penalty = "grLasso",
nfolds = 10)