#' @title Normalizing and filtering

#' @description This function allows filtering of genes and cells to be used in

#'   the downstream analysis.

#' @param object \code{DISCBIO} class object.

#' @param mintotal minimum total transcript number required. Cells with less

#'   than \code{mintotal} transcripts are filtered out. Default is 1000.

#' @param minexpr minimum required transcript count of a gene in at least

#'   \code{minnumber} cells. All other genes are filtered out. Default is 0.

#' @param minnumber minimum number of cells that are expressing each gene at

#'   minexpr transcripts. Default is 0.

#' @param maxexpr maximum allowed transcript count of a gene in at least a

#'   single cell after normalization or downsampling. All other genes are

#'   filtered out. Default is Inf.

#' @param downsample A logical vector. Default is FALSE. If downsample is set to

#'   TRUE, then transcript counts are downsampled to mintotal transcripts per

#'   cell, instead of the normalization. Downsampled versions of the transcript

#'   count data are averaged across dsn samples

#' @param dsn A numeric value of the number of samples to be used to average the

#'   downsampled versions of the transcript count data. Default is 1 which means

#'   that sampling noise should be comparable across cells. For high numbers of

#'   dsn the data will become similar to the median normalization.

#' @param rseed Random integer to enforce reproducible clustering.

#'   results

#' @include DIscBIO-classes.R

#' @return The DISCBIO-class object input with the ndata and fdata slots filled.

#' @examples

#' sc <- DISCBIO(valuesG1msTest) # changes signature of data

#'

#' # In this case this function is used to normalize the reads

#' sc_normal <- Normalizedata(

#'   sc,

#'   mintotal = 1000, minexpr = 0, minnumber = 0, maxexpr = Inf,

#'   downsample = FALSE, dsn = 1, rseed = 17000

#' )

#' summary(sc_normal@fdata)

#'

setGeneric(

  "Normalizedata",

  function(object, mintotal = 1000, minexpr = 0, minnumber = 0, maxexpr = Inf,

           downsample = FALSE, dsn = 1, rseed = NULL) {

    standardGeneric("Normalizedata")

  }

)

#' @export

#' @rdname Normalizedata

setMethod(

  "Normalizedata",

  signature = "DISCBIO",

  definition = function(

    object, mintotal, minexpr, minnumber, maxexpr, downsample, dsn, rseed

  ) {

    # Validation

    if (!is.numeric(mintotal)) {

      stop("mintotal has to be a positive number")

    } else if (mintotal <= 0) {

      stop("mintotal has to be a positive number")

    }

    if (!is.numeric(minexpr)) {

      stop("minexpr has to be a non-negative number")

    } else if (minexpr < 0) {

      stop("minexpr has to be a non-negative number")

    }

    if (!is.numeric(minnumber)) {

      stop("minnumber has to be a non-negative integer number")

    } else if (round(minnumber) != minnumber | minnumber < 0) {

      stop("minnumber has to be a non-negative integer number")

    }

    if (!(is.numeric(downsample) | is.logical(downsample))) {

      stop("downsample has to be logical (TRUE or FALSE)")

    }

    if (!is.numeric(dsn)) {

      stop("dsn has to be a positive integer number")

    } else if (round(dsn) != dsn | dsn <= 0) {

      stop("dsn has to be a positive integer number")

    }

    object@filterpar <- list(

      mintotal = mintotal,

      minexpr = minexpr,

      minnumber = minnumber,

      maxexpr = maxexpr,

      downsample = downsample,

      dsn = dsn

    )

    cols <- apply(object@expdata, 2, sum, na.rm = TRUE) >= mintotal

    object@ndata <- object@expdata[, cols]

    if (downsample) {

      set.seed(rseed)

      object@ndata <- downsample(object@expdata, n = mintotal, dsn = dsn)

    } else {

      x <- object@ndata

      object@ndata <- as.data.frame(t(t(x) / apply(x, 2, sum)) *

        median(apply(x, 2, sum, na.rm = TRUE)) + .1)

    }

    x <- object@ndata

    object@fdata <-

      x[apply(x >= minexpr, 1, sum, na.rm = TRUE) >= minnumber, ]

    x <- object@fdata

    object@fdata <- x[apply(x, 1, max, na.rm = TRUE) < maxexpr, ]

    return(object)

  }

)