#' @title Run Cluster Summary Metrics
 #' @description Calculates the mean expression of percent of cells that express the
 #' given genes for each cluster
 #'
 #' @param inSCE The single cell experiment to use.
 #' @param useAssay The assay to use.

 #' @param featureNames A string or vector of strings with each gene to aggregate.

 #' @param displayName A string that is the name of the column used for genes.

 #' @param groupNames The name of a colData entry that can be used as groupNames.

 #' @param scale Option to scale the data. Default: \code{FALSE}. Selected assay will not be scaled. 

 #' @return A dataframe with mean expression and percent of cells in cluster that 
 #' express for each cluster.
 #' @examples

 #' data("scExample")

 #' runClusterSummaryMetrics(inSCE=sce, useAssay="counts", featureNames=c("B2M", "MALAT1"), 
 #' displayName="feature_name", groupNames="type")

 #' @export
 

 runClusterSummaryMetrics <- function(inSCE, useAssay="logcounts", featureNames, displayName=NULL, groupNames="cluster", scale = FALSE){
   if(isTRUE(scale)){
     runNormalization(inSCE=inSCE, useAssay=useAssay, scale = TRUE, normalizationMethod = NULL, transformation = NULL, 
                      pseudocountsBeforeNorm = NULL, pseudocountsBeforeTransform = NULL)
   }

   if (!groupNames %in% names(SingleCellExperiment::colData(inSCE))) {
     stop("Specified variable '", groupNames, "' not found in colData(inSCE)")

   }

   if (!is.null(displayName)) {
     dimnames(inSCE)[[1]] <- rowData(inSCE)[[displayName]]
     dimnames(assay(inSCE, i=useAssay, withDimnames=FALSE))[[1]] <- rowData(inSCE)[[displayName]]

     falseGenes <- setdiff(featureNames, rowData(inSCE)[[displayName]])
     featureNames <- intersect(featureNames, rowData(inSCE)[[displayName]])

     warning <- paste0("rowData(inSCE)$", displayName)
   }
   else {

     falseGenes <- setdiff(featureNames, dimnames(inSCE)[[1]])
     featureNames <- intersect(featureNames, dimnames(inSCE)[[1]])

     warning <- "dimnames"
   }
   

   if (length(featureNames) == 0) {

     stop("All genes in '", toString(falseGenes), "' not found in ", warning)
   }

   if (length(falseGenes) > 0) {

     warning("Specified genes '", toString(falseGenes), "' not found in ", warning)

   }

   tempSCE <- inSCE[featureNames, ]
 
   
   if(isTRUE(scale)){

     tempSCE <- runNormalization(inSCE=tempSCE, outAssayName = "scaled", useAssay=useAssay,scale = TRUE, normalizationMethod = NULL, transformation = NULL,

                      pseudocountsBeforeNorm = NULL, pseudocountsBeforeTransform = NULL)

     useAssay <- "scaled"

   }

   avgExpr <- assay(scuttle::aggregateAcrossCells(tempSCE, ids=SingleCellExperiment::colData(inSCE)[,groupNames], 

                                                             statistics="mean", use.assay.type=useAssay, 

                                                  subset.row=NULL))

   percExpr <- assay(scuttle::aggregateAcrossCells(tempSCE, ids=SingleCellExperiment::colData(inSCE)[,groupNames], 

                                                              statistics="prop.detected", use.assay.type=useAssay, 

                                                   subset.row=NULL))

   df <- data.frame(featureNames = featureNames)

   df$avgExpr <- avgExpr
   df$percExpr <- percExpr
   return(df)
 

 }