iPsychCNV: iPsychCNV

Description Usage Arguments Details Value Author(s) Source Examples

View source: R/iPsychCNV.R

Description

iPsychCNV: Find Copy Number Variation (CNV) from SNP genotyping arrays.

Usage

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iPsychCNV(PathRawData = "/media/NeoScreen/NeSc_home/ILMN/iPSYCH/",
  MINNumSNPs = 20, Cores = 1, hg = "hg19", NumFiles = "All",
  Pattern = "22q11_*", MinLength = 10, SelectedFiles = NA, Skip = 10,
  LCR = FALSE, PFB = NULL, chr = NA, penalty = 60, Quantile = FALSE,
  QSpline = FALSE, sd = 0.18, recursive = FALSE, CPTmethod = "meanvar",
  CNVSignal = 0.1, penvalue = 16, OutputPath = NA,
  OutputFileName = "Test", OnlyCNVs = TRUE, SNPList = NULL)

Arguments

LCR

list: Low copy repeat region, list of SNPs that should be removed.

PFB

vector: Population frequency 0 to 1 for each SNP in the array.

PathRawData:

The path to the raw data files contining Log R Ratio (LRR) and B Allele Frequency (BAF) values.

MINNumSNPs:

Minimum number of SNPs per CNV, default = 20.

Cores:

Number of cores used, default = 1.

Hg:

Human genome version, default = hg19.

NumFiles:

Number of files to be analyzed from PathRawData.

Pattern:

File pattern in the PathRawData. Example: "*.txt".

MinLength:

Minimum CNV length, default = Unknown.

SelectedFiles:

List of file names that should be analyzed from PathRawData.

Skip:

Integer, the number of lines of the data file to skip before beginning to read data.

Chr:

Character, select a specific chromosome to be analyzed.

Penalty:

The coefficient of the penalty for degrees of freedom in the GCV criterion. From smooth.spline stats.

Quantile:

Logical, if quantile normalization should be applied or not, default = FALSE.

QSpline:

Logical, if a cubic smoothing spline should be used to normalize the data, default = FALSE.

Sd:

Numeric, LRR standard deviation for the quantile nomarlization, default = 0.18.

Recursive:

Logical, should the listing recurse into directories? From list.files base.

CPTmethod:

Character, method to find change points from changepoint package by Rebecca Killick. Default "meanvar", or "mean".

CNVSignal:

Numeric, minumum CNV signal to be consider a CNV in absolute value, default = 0.1, any CNV with mean LRR in the CNV region with abs(X) < 0.1 is ignored.

Penvalue:

Same as pen.value from function cpt.mean at changepoint R package by Rebecca Killick, default = 10. "The theoretical type I error e.g.0.05 when using the Asymptotic penalty. A vector of length 2 (min,max) if using the CROPS penalty. The value of the penalty when using the Manual penalty option - this can be a numeric value or text giving the formula to use. Available variables are, n=length of original data, null=null likelihood, alt=alternative likelihood, tau=proposed changepoint, diffparam=difference in number of alternatve and null parameters".

OutputPath:

Character, path for output.

OutputFileName:

Character, output file name.

OnlyCNVs:

Logical, if TRUE only CNVs with copy number state 0,1,3,4 will be returned. If FALSE will return also changepoint regions with CN = 2.

SNPList:

Getting Chr. and Position from another source than the RawFile - input should be the full path of the SNPList with columns: Name, Chr, amd 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

Details

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

Value

Data frame with predicted CNVs.

Author(s)

Marcelo Bertalan, Louise K. Hoeffding.

Source

http://biopsych.dk/iPsychCNV

Examples

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mockCNV <- MockData(N=5, Type="Blood", Cores=1)
cnvs <- iPsychCNV(PathRawData=".", Cores=1, Pattern="^MockSample*", Skip=0)

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