#' dea_pipeline
 #' 
 #' The `PipelineDefinition` for bulk RNAseq differential expression analysis 
 #' (DEA).
 #' 
 #' @return A `PipelineDefinition` object to be used with `runPipeline`.
 #' 
 #' @export
 #' @examples
 #' pip <- dea_pipeline()
 #' pip
 dea_pipeline <- function(){
   # description for each step
   desc <- list( 
     filtering=
 paste("Takes a SE object, passes it through the function `filt` (with param",
 " `minCount`), and outputs a filtered SE object."),
     sva=
 paste("Takes a SE object, passes it through the function `sva.method`, and",
       "returns a SE object."),
     dea=
 paste("Takes a SE object, passes it through the function `dea.method`, and",
       "returns a DEA data.frame.")
   )
   
   # functions list
   f <- list(
     filtering=function(x, filt, minCount=10) get(filt)(x, minCount=minCount),
     sva=function(x, sva.method, k=1){ 
       get(sva.method)(x, k=k)
     },
     dea=function(x, dea.method){
       mm <- pipeComp:::.getMM(x)
       x2 <- pipeComp:::.homogenizeDEA(get(dea.method)(x,mm))
       metadata(x2)$truth <- metadata(x)$truth
       metadata(x2)$mm <- mm
       x2
     }
   )
   
   agg <- eva <- lapply(f, FUN=function(x) NULL)
   eva$dea <- evaluateDEA

   agg$dea <- function(res){
     res <- defaultStepAggregation(res)
     lapply( res, FUN=function(x){
       x$sva.method <- gsub("^sva\\.","",x$sva.method)
       x$dea.method <- gsub("^dea\\.","",x$dea.method)
       x
     })
   }

 
   # default arguments
   def <- list(minCount=10, k=1)
 
   # initiation function
   initf <- function(x){
     if(is.character(x) && length(x)==1) x <- readRDS(x)
     metadata(x)$truth <- rowData(x)[,c("expected.beta","isDE")]
     cn <- grep("^SV[[:digit:]]+$", colnames(colData(x)), invert=TRUE)
     colData(x) <- colData(x)[,cn,drop=FALSE]
     x
   }
 
   PipelineDefinition(functions=f, descriptions=desc, evaluation=eva,
                      aggregation=agg, initiation=initf, 
                      defaultArguments=def, verbose=FALSE)
 }
 
 #' @importFrom S4Vectors DataFrame
 .homogenizeDEA <- function(x, g=NULL){
   colnames(x)[which(colnames(x) %in% c("FDR","padj","adj.P.Val"))] <- "FDR"
   colnames(x)[which(colnames(x) %in% c("P.Value","pvalue","PValue"))] <- "PValue"
   colnames(x)[which(colnames(x) %in% c("log2FoldChange","logFC"))] <- "logFC"
   if(is.null(g)) g <- row.names(x)
   x <- x[g,c("logFC","PValue","FDR")]
   row.names(x) <- g
   if(!is(x,"DataFrame")) x <- DataFrame(x)
   x
 }
 
 #' @importFrom stats model.matrix
 .getMM <- function(se){
   v <- c( grep("^SV[[:digit:]]+$", colnames(colData(se)), value=TRUE), 
           "condition" )
   f <- as.formula(paste0("~",paste(v,collapse="+")))
   model.matrix(f, data=as.data.frame(colData(se)))
 }