#' Class SconeExperiment

 #' 
 #' @description Objects of this class store, at minimum, a gene expression 
 #'   matrix and a set of covariates (sample metadata) useful for running 
 #'   \code{\link{scone}}. These include, the quality control (QC) metrics,
 #'   batch information, and biological classes of interest (if available).
 #'   
 #' @description The typical way of creating \code{SconeExperiment} objects is 

 #'   via a call to the \code{\link{SconeExperiment}} function or to the 

 #'   \code{\link{scone}} function. If the object is a result to a 
 #'   \code{\link{scone}} call, it will contain the results, e.g., the 
 #'   performance metrics, scores, and normalization workflow comparisons. (See 

 #'   Slots for a full list).

 #'   
 #' @description This object extends the 

 #'   \code{\linkS4class{SummarizedExperiment}} class.

 #'   
 #' @details The QC matrix, biological class, and batch information are 
 #'   stored as elements of the `colData` of the object.
 #' @details The positive and negative control genes are stored as 
 #'   elements of the `rowData` of the object.
 #'   

 #' @import methods
 #' @import SummarizedExperiment
 #' @importClassesFrom SummarizedExperiment SummarizedExperiment

 #'   

 #' @name SconeExperiment-class
 #' @import methods
 #' @aliases SconeExperiment

 #'   

 #' @export

 #' 
 #' @slot which_qc integer. Index of columns of `colData` that contain the
 #'   QC metrics.
 #' @slot which_bio integer. Index of the column of `colData` that contains
 #'   the biological classes information (it must be a factor).
 #' @slot which_batch integer. Index of the column of `colData`
 #'   that contains the batch information (it must be a factor).
 #' @slot which_negconruv integer. Index of the column of `rowData` that

 #'   contains a logical vector indicating which genes to use as negative

 #'   controls to infer the factors of unwanted variation in RUV.
 #' @slot which_negconeval integer. Index of the column of `rowData` that 
 #'   contains a logical vector indicating which genes to use as negative 
 #'   controls to evaluate the performance of the normalizations.
 #' @slot which_poscon integer. Index of the column of `rowData` that
 #'   contains a logical vector indicating which genes to use as positive

 #'   controls to evaluate the performance of the normalizations.

 #' @slot hdf5_pointer character. A string specifying to which 
 #'   file to write / read the normalized data.
 #' @slot imputation_fn list of functions used by scone for 
 #'   the imputation step.

 #' @slot scaling_fn list of functions used by scone for the scaling step.

 #' @slot scone_metrics matrix. Matrix containing the "raw" 
 #'   performance metrics. See \code{\link{scone}} for a 
 #'   description of each metric.
 #' @slot scone_scores matrix. Matrix containing the performance scores 
 #'   (transformed metrics). See \code{\link{scone}} for a discussion on the 

 #'   difference between scores and metrics.

 #' @slot scone_params data.frame. A data frame containing
 #'   the normalization schemes applied to the data and compared.
 #' @slot scone_run character. Whether \code{\link{scone}} was 
 #'   run and in which mode ("no", "in_memory", "hdf5").

 #' @slot is_log logical. Are the expression data in log scale?

 #' @slot nested logical. Is batch nested within bio? 
 #'   (Automatically set by \code{\link{scone}}).
 #' @slot rezero logical. TRUE if \code{\link{scone}} was run with 

 #'   \code{zero="preadjust"} or \code{zero="strong"}.

 #' @slot fixzero logical. TRUE if \code{\link{scone}} was run with 

 #'   \code{zero="postadjust"} or \code{zero="strong"}.

 #' @slot impute_args list. Arguments passed to all imputation functions.

 #' 
 #' @seealso \code{\link{get_normalized}}, \code{\link{get_params}},
 #' \code{\link{get_batch}}, \code{\link{get_bio}}, \code{\link{get_design}},
 #' \code{\link{get_negconeval}}, \code{\link{get_negconruv}},
 #' \code{\link{get_poscon}}, \code{\link{get_qc}}, 
 #' \code{\link{get_scores}}, and \code{\link{get_score_ranks}} 
 #' to access internal fields, \code{\link{select_methods}} for subsetting
 #' by method, and \code{\link{scone}} for running scone workflows.

 #'   

 setClass(
   Class = "SconeExperiment",
   contains = "SummarizedExperiment",
   slots = list(
     which_qc = "integer",
     which_bio = "integer",
     which_batch = "integer",
     which_negconruv = "integer",
     which_negconeval = "integer",
     which_poscon = "integer",
     hdf5_pointer = "character",

     imputation_fn = "list",
     scaling_fn = "list",

     scone_metrics = "matrix",
     scone_scores = "matrix",
     scone_params = "data.frame",

     scone_run = "character",

     is_log = "logical",

     nested = "logical",

     rezero = "logical",

     fixzero = "logical",

     impute_args = "list"

   )
 )
 
 setValidity("SconeExperiment", function(object) {
 
   ## check that the indeces are not out of bounds

   if(!all(object@which_qc %in% seq_len(NCOL(colData(object))))) {

     return("`which_qc` index out of bounds.")
   }
 

   if(!all(object@which_bio %in% seq_len(NCOL(colData(object))))) {

     return("`which_bio` index out of bounds.")
   }
 

   if(!all(object@which_batch %in% seq_len(NCOL(colData(object))))) {

     return("`which_batch` index out of bounds.")
   }
 

   if(!all(object@which_negconruv %in% seq_len(NCOL(rowData(object))))) {

     return("`which_negconruv` index out of bounds.")
   }
 

   if(!all(object@which_negconeval %in% seq_len(NCOL(rowData(object))))) {

     return("`which_negconeval` index out of bounds.")
   }
 

   if(!all(object@which_poscon %in% seq_len(NCOL(rowData(object))))) {

     return("`which_poscon` index out of bounds.")
   }
 
   ## check that which_bio and which_batch are of length 1

   if(length(object@which_bio) > 1) {

     return("Only one `bio` variable can be specified.")
   }

   if(length(object@which_batch) > 1) {

     return("Only one `batch` variable can be specified.")
   }
 
   ## check that which_poscon and which_negcon are of length 1

   if(length(object@which_negconruv) > 1) {

     return("Only one set of negative controls for RUV can be specified.")
   }

   if(length(object@which_negconeval) > 1) {

     return(paste0("Only one set of negative controls ",
                   "for evaluation can be specified."))

   }

   if(length(object@which_poscon) > 1) {

     return("Only one set of positive controls can be specified.")
   }
 
   ## check that all QC columns are numeric

   if(length(object@which_qc) > 0) {

     if(any(lapply(get_qc(object), class) != "numeric")) {

       return("Only numeric QC metrics are allowed.")
     }

   }
 
   ## check that bio is a factor

   if(length(object@which_bio) > 0) {

     if(!is.factor(get_bio(object))) {

       return("`bio` must be a factor.")
     }

   }
 
   ## check that batch is a factor

   if(length(object@which_batch) > 0) {

     if(!is.factor(get_batch(object))) {

       return("`batch` must be a factor.")
     }

   }
 
   ## check that poscon and negcon are logical

   if(length(object@which_negconruv) > 0) {

     if(!is.logical(get_negconruv(object))) {

       return("`negconruv` must be a logical vector.")
     }

   }

   if(length(object@which_negconeval) > 0) {

     if(!is.logical(get_negconeval(object))) {

       return("`negconeval` must be a logical vector.")
     }

   }

   if(length(object@which_poscon) > 0) {

     if(!is.logical(get_poscon(object))) {

       return("`poscon` must be a logical vector.")
     }

   }
 
   ## check if hdf5 file exist (depends on scone_run)

   if(length(object@hdf5_pointer) > 0) {
     if(object@scone_run == "hdf5" && !file.exists(object@hdf5_pointer)) {
       return(paste0("File ", object@hdf5_pointer, " not found."))
     }
     if(object@scone_run == "no" && file.exists(object@hdf5_pointer)) {

       return(paste0("File ", object@hdf5_pointer,
                     " exists. Please specify a new file."))

     }

   }

 
   return(TRUE)
 

 })

 
 ## Constructor
 
 #' @rdname SconeExperiment-class

 #'   

 #' @description The constructor \code{SconeExperiment} creates an object of the

 #'   class \code{SconeExperiment}.

 #'   
 #' @param object Either a matrix or a \code{\link{SummarizedExperiment}} 

 #'   containing the raw gene expression.

 #' @param ... see specific S4 methods for additional arguments.

 #' @export

 #' 

 #' @examples

 #' set.seed(42)

 #' nrows <- 200
 #' ncols <- 6
 #' counts <- matrix(rpois(nrows * ncols, lambda=10), nrows)
 #' rowdata <- data.frame(poscon=c(rep(TRUE, 10), rep(FALSE, nrows-10)))
 #' coldata <- data.frame(bio=gl(2, 3))
 #' se <- SummarizedExperiment(assays=SimpleList(counts=counts),
 #'                           rowData=rowdata, colData=coldata)

 #' 

 #' scone1 <- SconeExperiment(assay(se), bio=coldata$bio, poscon=rowdata$poscon)

 #' 

 #' scone2 <- SconeExperiment(se, which_bio=1L, which_poscon=1L)

 #' 
 #' 

 setGeneric(

   name = "SconeExperiment",

   def = function(object, ...) {

     standardGeneric("SconeExperiment")

   }
 )
 
 #' @rdname SconeExperiment-class

 #'   
 #' @param which_qc index that specifies which columns of `colData` 
 #'   correspond to QC measures.
 #' @param which_bio index that specifies which column of `colData`
 #'   corresponds to `bio`.
 #' @param which_batch index that specifies which column of `colData`
 #'   corresponds to `batch`.
 #' @param which_negconruv index that specifies which column of `rowData`
 #'   has information on negative controls for RUV.
 #' @param which_negconeval index that specifies which column of `rowData`
 #'   has information on negative controls for evaluation.
 #' @param which_poscon index that specifies which column of `rowData` has 

 #'   information on positive controls.

 #' @param is_log are the expression data in log scale?

 #' @export

 #' 

 setMethod(

   f = "SconeExperiment",

   signature = signature("SummarizedExperiment"),
   definition = function(object, which_qc=integer(), which_bio=integer(),
                         which_batch=integer(),
                         which_negconruv=integer(), which_negconeval=integer(),

                         which_poscon=integer(), is_log=FALSE) {

 
     out <- new("SconeExperiment",
                object,
                which_qc = which_qc,
                which_bio = which_bio,
                which_batch = which_batch,
                which_negconruv = which_negconruv,
                which_negconeval = which_negconeval,
                which_poscon = which_poscon,
                hdf5_pointer = character(),

                imputation_fn = list(),
                scaling_fn = list(),

                scone_metrics = matrix(),
                scone_scores = matrix(),
                scone_params = data.frame(),

                scone_run = "no",

                is_log = is_log,

                nested = FALSE,

                rezero = FALSE,

                fixzero = FALSE,

                impute_args = list()

                )
 
     validObject(out)
     return(out)
   }
 )
 
 
 #' @rdname SconeExperiment-class

 #'   

 #' @param qc numeric matrix with the QC measures.
 #' @param bio factor with the biological class of interest.
 #' @param batch factor with the batch information.

 #' @param negcon_ruv a logical vector indicating which genes to use as negative
 #'   controls for RUV.
 #' @param negcon_eval a logical vector indicating which genes to use as 
 #'   negative controls for evaluation.
 #' @param poscon a logical vector indicating which genes to use as positive
 #'   controls.
 #'   

 #' @export

 #' 

 #' @return A \code{\link{SconeExperiment}} object.

 #'   

 setMethod(

   f = "SconeExperiment",

   signature = signature("matrix"),
   definition = function(object, qc, bio, batch,
                         negcon_ruv=NULL, negcon_eval=negcon_ruv,

                         poscon=NULL, is_log=FALSE) {

 
     which_qc <- which_bio <- which_batch <- integer()
     which_negconruv <- which_negconeval <- which_poscon <- integer()
 
     coldata <- as.data.frame(matrix(nrow=NCOL(object), ncol=0))
 
     if(!missing(qc)) {
       coldata <- cbind(coldata, qc)
       which_qc <- seq_len(NCOL(coldata))
     }
 
     if(!missing(bio)) {
       coldata <- cbind(coldata, bio)
       which_bio <- NCOL(coldata)
     }
 
     if(!missing(batch)) {
       coldata <- cbind(coldata, batch)
       which_batch <- NCOL(coldata)
     }
 
     rowdata <- as.data.frame(matrix(nrow=NROW(object), ncol=0))
 
     if(!is.null(negcon_ruv)) {
       rowdata <- cbind(rowdata, negcon_ruv)
       which_negconruv <- NCOL(rowdata)
     }
 
     if(!is.null(negcon_eval)) {
       rowdata <- cbind(rowdata, negcon_eval)
       which_negconeval <- NCOL(rowdata)
     }
 
     if(!is.null(poscon)) {
       rowdata <- cbind(rowdata, poscon)
       which_poscon <- NCOL(rowdata)
     }
 
     se <- SummarizedExperiment(object, rowData=rowdata, colData=coldata)

     SconeExperiment(se,  which_qc, which_bio, which_batch,

                     which_negconruv, which_negconeval, which_poscon, is_log)

   }
 )