R/cv.PCLasso.R

In weiliu123/PCLassoCox: A protein complex-based group lasso-Cox model for accurate prognosis and risk protein complex discovery

#' Cross-validation for \code{PCLasso}
#'
#' Perform k-fold cross validations for the PCLasso model with grouped
#' covariates over a grid of values for the regularization parameter
#' \code{lambda}.
#'
#' @param x A n x p design matrix of gene expression measurements with n samples
#'   and p genes, as in \code{PCLasso}.
#' @param y The time-to-event outcome, as a two-column matrix or \code{Surv}
#'   object, as in \code{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.
#' @param group A list of groups as in \code{PCLasso}. The feature (gene) names
#'   in \code{group} should be consistent with the feature (gene) names in
#'   \code{x}.
#' @param 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 \code{grpsurv} in the R package \code{grpreg} for details.
#' @param nfolds The number of cross-validation folds. Default is 5.
#' @param standardize Logical flag for \code{x} standardization, prior to
#'   fitting the model. Default is \code{TRUE}.
#' @param ... Arguments to be passed to \code{cv.grpsurv} in the R package
#'   \code{grpreg}.
#'
#' @details
#' The function calls \code{PCLasso} \code{nfolds} times, each time leaving out
#' 1/\code{nfolds} of the data. The cross-validation error is based on the
#' deviance. The numbers for each outcome class are balanced across the folds;
#' i.e., the number of outcomes in which y is equal to 1 is the same for each
#' fold, or possibly off by 1 if the numbers do not divide evenly.
#' \code{cv.PCLasso} uses the approach of calculating the full Cox partial
#' likelihood using the cross-validated set of linear predictors. See
#' \code{cv.grpsurv} in the R package \code{grpreg} for details.
#'
#' @return An object with S3 class "cv.PCLasso" containing:
#' \item{cv.fit }{
#' An object of class "cv.grpsurv".}
#'   \item{group.dt }{
#'   Groups with  features
#'   (genes) not included in \code{x} being filtered out. }
#' @import grpreg
#' @export
#'
#' @examples
#' # load data
#' data(GBM)
#' data(PCGroup)
#'
#' x = GBM$GBM.train$Exp
#' y = GBM$GBM.train$survData
#'
#' # fit model
#' cv.fit1 <- cv.PCLasso(x, y, group = PCGroup, penalty = "grLasso",
#' nfolds = 10)
#' @references
#' PCLasso: a protein complex-based group lasso-Cox model for accurate prognosis
#' and risk protein complex discovery. To be published.
#'
#' 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.
#' @author Wei Liu
#' @seealso \code{\link{predict.cv.PCLasso}}

cv.PCLasso <-
function(x, y, group,
    penalty = c("grLasso", "grMCP", "grSCAD","gel", "cMCP"), nfolds = 5,
    standardize = TRUE,...){
    penalty <- match.arg(penalty)

    if(standardize){
        x <- scale(x, center = TRUE, scale = TRUE)
    }

    # feature set in all groups
    featureSet <- unique(unlist(group))

    # common features in groups and expression matrix x
    commonFeat <- intersect(colnames(x), featureSet)

    # filter undetected genes in expression matrix x
    x <- x[,commonFeat]

    # filter undetected genes in groups
    # Construct groups whose expressions are detected
    group.dt <- vector(mode = "list", length = 0)
    idx <- 0
    for(i in 1:length(group)){
        group.i <- intersect(group[[i]], commonFeat)
        if(length(group.i) > 0){
            idx <- idx + 1
            group.dt[[idx]] <- group.i
            names(group.dt)[idx] <- names(group)[i]
        }
    }

    # Filter duplicate groups (generated due to undetected genes)
    group.dt <- group.dt[!duplicated(group.dt)]

    # extended genes
    commonFeat.ext <- unlist(group.dt)

    # New names of extended genes
    # The new name consists of "group+.+gene name"
    commonFeat.extName <- c()
    for(i in 1:length(group.dt)){
        names.i <- paste0(names(group.dt)[i], ".", group.dt[[i]])
        commonFeat.extName <- c(commonFeat.extName, names.i)
    }

    # group of extended genes
    groupOfFeats <- c()
    for(i in 1:length(group.dt)){
        group.i <- rep(names(group.dt)[i], length = length(group.dt[[i]]))
        groupOfFeats <- c(groupOfFeats, group.i)
    }

    # extended dataset
    x.ext <- x[, commonFeat.ext]
    colnames(x.ext) <- commonFeat.extName

    # grpsurv
    cv.fit <- grpreg::cv.grpsurv(X=x.ext,
                      y=y,
                      group = groupOfFeats,
                      penalty = penalty,
                      nfolds = nfolds,...)

    res <- list(cv.fit = cv.fit, group.dt = group.dt)
    class(res) <- "cv.PCLasso"

    return(res)
}