\name{cn.mops}
\alias{cn.mops}
\title{Performs the cn.mops algorithm for copy number detection in
NGS data.}
\usage{
  cn.mops(input,
    I = c(0.025, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4),
    classes = c("CN0", "CN1", "CN2", "CN3", "CN4", "CN5", "CN6", "CN7", "CN8"),
    priorImpact = 1, cyc = 20, parallel = 0,
    normType = "poisson", normQu = 0.25, norm = TRUE,
    upperThreshold = 0.5, lowerThreshold = -0.9,
    minWidth = 3, segAlgorithm = "fast", ...)
}
\arguments{
  \item{input}{Either an instance of "GRanges" or a raw
  data matrix, where columns are interpreted as samples and
  rows as genomic regions. An entry is the read count of a
  sample in the genomic region.}

  \item{I}{Vector positive real values that contain the
  expected fold change of the copy number classes.  Length
  of this vector must be equal to the length of the
  "classes" parameter vector. For human copy number
  polymorphisms we suggest to use the default I =
  c(0.05,0.5,1,1.5,2,2.5,3,3.5,4).}

  \item{classes}{Vector of characters of the same length as
  the parameter vector "I". One vector element must be
  named "CN2". The names reflect the labels of the copy
  number classes. Default =
  c("CN0","CN1","CN2","CN3","CN4","CN5","CN6","CN7","CN8").}

  \item{priorImpact}{Positive real value that reflects how
  strong the prior assumption affects the result. The
  higher the value the more samples will be assumed to have
  copy number 2. Default = 1.}

  \item{cyc}{Positive integer that sets the number of
  cycles for the algorithm. Usually after less than 15
  cycles convergence is reached. Default = 20.}

  \item{parallel}{How many cores are used for the
  computation. If set to zero than no parallelization is
  applied. The package "snow" has to be installed for this
  option. Default = 0.}

  \item{normType}{Mode of the normalization technique.
  Possible values are "mean","min","median","quant",
  "poisson" and "mode". Read counts will be scaled
  sample-wise. Default = "poisson".}

  \item{normQu}{Real value between 0 and 1. If the
  "normType" parameter is set to "quant" then this
  parameter sets the quantile that is used for the
  normalization. Default = 0.25.}

  \item{norm}{Logical that indicates whether normalization
  should be applied or not. Default = TRUE.}

  \item{upperThreshold}{Positive real value that sets the
  cut-off for copy number gains. All CNV calling values
  above this value will be called as "gain". The value
  should be set close to the log2 of the expected
  foldchange for copy number 3 or 4. Default = 0.5.}

  \item{lowerThreshold}{Negative real value that sets the
  cut-off for copy number losses. All CNV calling values
  below this value will be called as "loss". The value
  should be set close to the log2 of the expected
  foldchange for copy number 1 or 0. Default = -0.9.}

  \item{minWidth}{Positive integer that is exactly the
  parameter "min.width" of the "segment" function of
  "DNAcopy". minWidth is the minimum number of segments a
  CNV should span. Default = 4.}

  \item{segAlgorithm}{Which segmentation algorithm should
  be used. If set to "DNAcopy" circular binary segmentation
  is performed. Any other value will initiate the use of
  our fast segmentation algorithm. Default = "fast".}

  \item{...}{Additional parameters will be passed to the
  "DNAcopy" or the standard segmentation algorithm.}
}
\value{
  An instance of "CNVDetectionResult".
}
\description{
  Performs the cn.mops algorithm for copy number detection
  in NGS data.
}
\examples{
data(cn.mops)
cn.mops(XRanges)
}
\author{
  Guenter Klambauer \email{klambauer@bioinf.jku.at}
}