R/Specific.R

In iloveless1/EpiXprS: Associates DNA Methylation with Matched gene Expression

#' Function for associating race specific DNA methylation with matched mRNA expression
#'
#' This function allows one to associate DNA methylation with matched mRNA expression
#' in a multivariate context in race specific data.
#' @importFrom utils data
#' @import stats
#' @export Specific
#' @param x Processed DNA methylation with covariates. Race must be the final term
#' @param y Raw expression count vector
#' @param alpha Elastic net mixing penalty. Defaults to 0.5
#' @param nfolds the number of folds to use for cross-validation
#' @param clin a matrix of clinical covariates
#' @return returns list of three elements for summarizing model construction. 
#' \code{Model} is the predicted model for the gene. \code{Predicted.Expression}
#' is the Cross-validated predicted expression. \code{Overall.D2} The prediction
#'  accuracy for the model. 
#' @examples
#' data.whole = cbind(matrix(rnorm(500),nrow=50), rbinom(50,1,0.5))
#' RNA_tmp <- rpois(50,15)
#' Specif(data.whole, RNA_tmp, alpha = 0.5, nfolds = 5, clin = NULL)
#'



Specific <- function(x,y,alpha,nfolds,clin){
    if (!requireNamespace("glmnet", quietly = TRUE)) {
        stop("Package \"glmnet\" needed for this function to work. Please install it.",
             call. = FALSE)
    }
    tryCatch({
        if(ncol(clin) >1){
            mat <- model.matrix(y ~ clin + x -1)
        } else {
            mat <- model.matrix(y ~  x -1)
            
        }
        
        whole.elastic.fit.cv <- glmnet::cv.glmnet(mat,as.matrix(t(y)) , family ='poisson', nfolds = nfolds,alpha=alpha,penalty.factor = c(rep(1, ncol(mat) - ncol(clin)),0,0), parallel = FALSE )
        coef.min = stats::coef(whole.elastic.fit.cv, s = "lambda.min")
        active.min = which(as.numeric(coef.min) != 0)
        Whole.elastic = coef.min[active.min]
        names <- rownames(coef.min)
        a <- data.frame(row.names = names[active.min], Weights = Whole.elastic)
        
        ct <- rep(NA,nfolds)
        b <- NULL
        tmp <- sample(seq(nfolds),nrow(x),replace=TRUE)

        for (i in seq(nfolds)){


            Whole.elastic.fit <- glmnet::cv.glmnet(mat[!tmp %in% i,],t(y[!tmp %in% i]), family='poisson', nfolds = nfolds,alpha = alpha,penalty.factor = c(rep(1, ncol(mat) - ncol(clin)),0,0))
            coef.min = stats::coef(whole.elastic.fit.cv, s = "lambda.min")

            out <- predict(Whole.elastic.fit, mat[tmp %in% i,], s=Whole.elastic.fit$lambda.min, type = 'response', gamma=0.5)
            rownames(out) <- rownames(mat[tmp %in% i,])
            out <- round(out)
            b <- rbind(b,out)

            y_i <- as.numeric(y[tmp %in% i])
            u_i <- out

         
            ct[i] <- D2(y_i,u_i)

        }
        c <- ifelse(stats::median(ct, na.rm = TRUE) < 0,0,stats::median(ct, na.rm = TRUE))
    }, error=function(e){})
    output <- list('Model' = a, 'Predicted.Expression' = b, 'Overall.D2' = c)
    return(output)
}