R/regression_selected_pathways_funcs.R

In TaoDFang/gerr: Gene-set Enrichment with Regularized Regression

#' regression_selected_pathways
#'
#' This function allows you to extracte enriched pathways for gene module/list via regressioin (elastic net)
#' based method
#' @param gene_input A vecor of genes to be annotated. It should have same ID types(Ensembl ID, HUGO gene symbol)
#' as the genes in \emph{gene_pathway_matrix}.
#' @param gene_pathway_matrix A binary background matrix whose columns are the pathways/gene sets and
#'whose rows are all the genes from pathways/gene sets . It could be in sparse matrix format ((inherit from class
#' "sparseMatrix" as in package Matrix) to save memory.
#'For gene i and pathway j, the value of matrix(i,j) is 1 is gene i belonging to pathway j otherwise 0.
#'Users could leave it as default value then  it will use pre-collected gene_pathway_matrix from GO Ontology and
#'REACTOME databaase.
#'Otherwise, they could use their own customized gene_pathway_matrix
#' @param alpha The elasticnet mixing parameter, with \eqn{0~\leq~\alpha~\leq~1}. The penalty is defined as
#'\eqn{(1-\alpha)/2||\beta||_2^2+\alpha||\beta||_1}.
#'alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. Default value: 0.5.
#' @param family Response type, currently \code{gaussian} and \code{binomial} are supported and the gaussian family is the default. Future extensions are likely.
#' @param lambda A user supplied lambda sequence, see \code{\link{glmnet}} and use with care.
#' @param verbose If supprese warning messesge from gerr functions. TRUE or FALSE. Default value TRUE, not suppressing warning messages.
#' @param ... Other paramaters passed to the \code{\link{cv.glmnet}} function.
#' @return  A list of following elements:
#' \itemize{
#'   \item selected_pathways_names - Pathways names for selected pathways
#'   \item selected_pathways_coef - Regression coefficients value for selected pathways
#'   \item selected_pathways_fisher_pvalue - Fisher exact pvalue for selected pathways
#'   \item selected_pathways_num_genes - The number of genes for selected pathways in background
#'   \item model - the trained glmnet model
#'   \item x - independent variable
#'   \item y - dependent variable
#' }
#' @importFrom glmnet glmnet cv.glmnet
#' @export
#' @examples
#' rspResults <- regression_selected_pathways(gene_input=c("TRPC4AP","CDC37",
#'   "TNIP1","IKBKB","NKIRAS2", "NFKBIA","TIMM50","RELB","TNFAIP3","NFKBIB",
#'   "HSPA1A","NFKBIE","SPAG9","NFKB2","ERLIN1","REL","TNIP2",
#'   "TUBB6","MAP3K8"),
#'  gene_pathway_matrix=NULL,lambda=NULL,alpha=0.5)
regression_selected_pathways=function(gene_input,gene_pathway_matrix=NULL,alpha=0.5,
                                      family=c("gaussian", "binomial"), lambda=NULL,
                                      verbose=TRUE,
                                      ...){
  family <- match.arg(family)
  if(is.null(gene_pathway_matrix)){
    gene_pathway_matrix <- mydata("gene_pathway_matrix", "gerr")
  }

  all_genes <- rownames(gene_pathway_matrix)
  all_pathways <- colnames(gene_pathway_matrix)
  
  module_labels <- integer(length(all_genes))
  names(module_labels) <- all_genes
  module_common_genes <- intersect(all_genes,gene_input)
  module_labels[module_common_genes] <- 1
  
  if(length(module_common_genes)<=1) {
    if(verbose){
      warning("Not enough genes in the set of genes of interest. NULL is returned.\n")
    }
    return(NULL)
  }
  
  cvfit <- try(cv.glmnet(gene_pathway_matrix,
                  module_labels,alpha =alpha, 
                  family=family, lambda=lambda,
                  lower.limits=0,
                  ...))

  if(class(cvfit)=="try-error") {
    if(verbose){
      warning("cv.glmnet failed. The error and the model input is returned for debugging purposes.\n")
    }
    res <- list(model=cvfit,
                x=gene_pathway_matrix,
                y=module_labels)
    return(res)
  }
  
  passParams <- list(...)
  hasIntercept <- is.null(passParams$intercept) || (!is.null(passParams$intercept) & passParams$intercept)
  coef <- coef(cvfit, s = "lambda.min")
  non0index <- coef@i
  non0coef <- coef@x
  if(hasIntercept) { #remove intercept
    non0index <- non0index[-1]   
    non0coef <- non0coef[-1]
  }
  isPosCoef <- non0coef>0
  selected_index <- non0index[isPosCoef]
  selected_pathways <- all_pathways[selected_index]
  selected_coef <- non0coef[isPosCoef]
  names(selected_coef) <- selected_pathways

  
  if(any(isPosCoef)){
    fisher_exact_test_results <- fisher_exact_test(selected_pathways,module_common_genes,gene_pathway_matrix=gene_pathway_matrix)
    selected_pathways_fisher_pvalue <- fisher_exact_test_results$selected_pathways_fisher_pvalue
    selected_pathways_num_genes <- fisher_exact_test_results$selected_pathways_num_genes
    
    new_order <- order(selected_coef,decreasing = TRUE)
    res <- list(selected_pathways_names=from_id2name(selected_pathways=names(selected_coef[new_order])),
                selected_pathways_coef=selected_coef[new_order],
                selected_pathways_fisher_pvalue=selected_pathways_fisher_pvalue[new_order],
                selected_pathways_num_genes=selected_pathways_num_genes[new_order],
                model=cvfit, 
                x=gene_pathway_matrix,
                y=module_labels)

  } else {
    if(verbose){
      warning("No selected gene-sets with the given parameter set. NULL is returned.\n")
    }
    
    res <- list(model=cvfit, 
                x=gene_pathway_matrix,
                y=module_labels)
  }
  return(res)
}