PlotCNVsFromDataFrame: PlotCNVsFromDataFrame

Description Usage Arguments Details Value Author(s) Source Examples

View source: R/PlotCNVsFromDataFrame.R

Description

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

Usage

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PlotCNVsFromDataFrame(DF, PathRawData = ".", Cores = 1, Skip = 0,
  PlotPosition = 1, Pattern = "*", recursive = TRUE, dpi = 300,
  Files = NA, SNPList = NULL, key = NA, OutFolder = ".",
  XAxisDefine = NULL, Window = NA)

Arguments

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.

XAxisDefine:

Position of a specific region to be plotted, in the form chr21:1050000-1350000, default = NULL.

OutFolder:

Path for saving outputfiles, default is the current folder.

Details

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

Value

One BAF- and LRR-plot for each CNV.

Author(s)

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

Source

http://biopsych.dk/iPsychCNV

Examples

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# 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.
PlotCNVsFromDataFrame(DF=CNVs.Good[1,], PathRawData=".", Cores=1, Skip=0, Pattern="^MockSamples*", key=NA, OutFolder="../", XAxisDefine = NULL)

mbertalan/iPsychCNV documentation built on June 30, 2017, 2:02 a.m.