--- a
+++ b/R/DIscBIO-generic-Normalizedata.R
@@ -0,0 +1,102 @@
+#' @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)
+  }
+)