RunPennCNV: RunPennCNV
In mbertalan/iPsychCNV: iPsychCNV

Description Usage Arguments Details Value Author(s) Source Examples

RunPennCNV: Run the pennCNV algorithm.

RunPennCNV(PathRawData = "~/CNVs/MockData/PKU/Data", MINNumSNPs = 20,
  Pattern = ".*Mock.*\\.tab$", Cores = 20, Skip = 0,
  Normalization = FALSE, PFB = "NO",
  HMM = "/media/NeoScreen/NeSc_home/share/Programs/penncnv/lib/hhall.hmm",
  Path2PennCNV = "/media/NeoScreen/NeSc_home/share/Programs/penncnv/",
  penalty = 60, Quantile = TRUE, QSpline = TRUE, sd = 0.15,
  PennCNVFormat = FALSE, RemoveTmpfiles = TRUE)

`PathRawData:`	The path to the raw data files containing Log R Ratio (LRR) and B Allele Frequency (BAF) values.
`MINNumSNPs:`	Minimum number of SNPs per CNV, default = 20.
`Pattern:`	File pattern in the PathRawData. Example: "*.txt".
`Cores:`	Number of cores used; default = 20.
`Skip:`	Integer, the number of lines of the data file to be skipped before beginning to read the data, default = 0.
`Normalization:`	Unknown, default = FALSE.
`PFB:`	Vector population frequency 0 to 1 for each SNP in the array, default = NO.
`HMM:`	Unknown, default = Unknown.
`Path2PennCNV:`	The path to the pennCNV algorithm.
`Penalty:`	The coefficient of the penalty for degrees of freedom in the GCV criterion. From smooth.spline stats, default = 60.
`Quantile:`	Logical, if quantile normalization should be applied or not, default = TRUE.
`QSpline:`	Logical, if a cubic smoothing spline should be used to normalize the data, default = TRUE.
`Sd:`	numeric, LRR standard deviation (sd) for the quantile nomarlization, default = 0.15.
`PennCNVFormat:`	Unknown, default = FALSE.
`RemoveTmpfiles:`	Unknown, default = TRUE.