singlecn.mops: Copy number detection in NGS data with in a setting in which...
In cn.mops: cn.mops - Mixture of Poissons for CNV detection in NGS data

Description Usage Arguments Value Author(s) Examples

This function performs the an alternative version of the cn.mops algorithm adapted to a setting of a single sample.

singlecn.mops(x, 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, norm = 1,
  normType = "poisson", sizeFactor = "mean", normQu = 0.25,
  quSizeFactor = 0.75, upperThreshold = 0.5, lowerThreshold = -0.9,
  minWidth = 3, segAlgorithm = "fast", minReadCount = 1,
  returnPosterior = FALSE, ...)

`x`	Either an instance of "GRanges" or a raw data matrix with one column or a vector of read counts. An entry is the read count of the sample in the genomic region.
`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(0.025,0.5,1,1.5,2,2.5,3,3.5,4).
`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").
`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.
`cyc`	Positive integer that sets the number of cycles for the algorithm. Usually after less than 15 cycles convergence is reached. Default = 20.
`parallel`	How many cores are used for the computation. If set to zero than no parallelization is applied. Default = 0.
`norm`	The normalization strategy to be used. If set to 0 the read counts are not normalized and cn.mops does not model different coverages. If set to 1 the read counts are normalized. If set to 2 the read counts are not normalized and cn.mops models different coverages. (Default=1).
`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".
`sizeFactor`	By this parameter one can decide to how the size factors are calculated. Possible choices are the the mean, median or mode coverage ("mean", "median", "mode") or any quantile ("quant").
`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.
`quSizeFactor`	Quantile of the sizeFactor if sizeFactor is set to "quant". 0.75 corresponds to "upper quartile normalization". Real value between 0 and 1. Default = 0.75.
`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.
`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.
`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 = 3.
`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".
`minReadCount`	If all samples are below this value the algorithm will return the prior knowledge. This prevents that the algorithm from being applied to segments with very low coverage. Default=1.
`returnPosterior`	Flag that decides whether the posterior probabilities should be returned. The posterior probabilities have a dimension of samples times copy number states times genomic regions and therefore consume a lot of memory. Default=FALSE.
`...`	Additional parameters will be passed to the "DNAcopy" or the standard segmentation algorithm.