PlotCNVs: PlotCNVs
In mbertalan/iPsychCNV: iPsychCNV

Description Usage Arguments Details Value Author(s) Source Examples

PlotCNVs: Function to plot Log R Ratio (LRR) and B Allele Frequency (BAF) of CNVs from a data frame (DF).

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PlotCNVs(DF, PathRawData = ".", Cores = 1, Skip = 10, PlotPosition = 1,
  Pattern = "", recursive = TRUE, dpi = 300, Files = NA, Start = NA,
  Stop = NA, SNPList = NULL, OutFolder = ".", Window = 35, key = NA)

`DF:`	Data frame with predicted CNVs for each sample, default = Unknown.
`PathRawData:`	The path to the raw data files containing LRR and BAF values.
`Cores:`	Number of cores used, default = 1.
`Skip:`	Integer, the number of lines of the data file to be skipped before beginning to read the data, default = 0.
`PlotPosition:`	Unknown, default = 1.
`Pattern:`	File pattern in the raw data, default = "*".
`Recursive:`	Logical, Unknown, default = TRUE.
`Dpi:`	Dots per inch, default = 300.
`Files:`	Unknown, default = NA.
`SNPList:`	Getting chromosome (Chr) and position from another source than the RawFile - input should be the full path of the SNPList with columns: Name, Chr, and Position. Any positions from the RawFile will be erased. A PFB-column is also allowed but will be overwritten by the PFB-parameter or exchanged with 0.5, default = NULL.
`Key:`	Exchange the ID printed on the plot and in the name of file with a deidentified ID - requires that the DF contains a column called ID_deidentified, default = NA.
`Start:`	Start position of plot
`Stop:`	Stop position of plot
`OutFolder:`	Path for saving outputfiles, default is the current folder.

Specifically designed to handle noisy data from amplified DNA on phenylketonuria (PKU) cards. The function is a pipeline using many subfunctions.

One BAF- and LRR-plot for each CNV.

Marcelo Bertalan, Ida Elken Sønderby, Louise K. Hoeffding.

http://biopsych.dk/iPsychCNV

# Creating CNVs from MockData & plotting
MockCNVs <- MockData(N=2, Type="Blood", Cores=10)
CNVs <- iPsychCNV(PathRawData=".", Pattern="^MockSample*", Skip=0)
CNVs.Good <- subset(CNVs, CN != 2) # keep only CNVs with CN = 0, 1, 3, 4.
PlotCNVs(DF=CNVs.Good[1,], PathRawData=".", Cores=1, Skip=0, Pattern="^MockSamples*", key=NA, OutFolder="../", XAxisDefine = NULL)