runPanelcnMops: Full copy number detection for targeted NGS panel data for...

Description Usage Arguments Value Examples

View source: R/runPanelcnMops.R

Description

This function performs first quality control and runs panelcn.mops for CNV detection on all test samples.

Usage

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runPanelcnMops(XandCB, testiv = c(1), countWindows, selectedGenes = NULL,
  I = c(0.025, 0.57, 1, 1.46, 2), normType = "quant",
  sizeFactor = "quant", qu = 0.25, quSizeFactor = 0.75, norm = 1,
  priorImpact = 1, minMedianRC = 30, maxControls = 25,
  corrThresh = 0.99, sex = "mixed")

Arguments

XandCB

GRanges object of combined read counts of test samples and control samples as returned by countBamListInGRanges

testiv

vector of indices of test samples in XandCB. Default = c(1)

countWindows

data.frame with contents of a BED file as returned by getWindows

selectedGenes

vector of names of genes of interest or NULL if all genes are of interest. Default = NULL

I

vector of positive real values containing 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 targeted NGS panel data the default is c(0.025,0.57,1,1.46,2)

normType

type of the normalization technique. Each samples' read counts are scaled such that the total number of reads are comparable across samples. Options are "mean","median","poisson", "quant", and "mode" Default = "quant"

sizeFactor

parameter for calculating the size factors for normalization. Options are "mean","median", "quant", and "mode". Default = "quant"

qu

Quantile of the normType if normType is set to "quant". Real value between 0 and 1. 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

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.

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

minMedianRC

segments with median read counts over all samples < minMedianRC are excluded from the analysis

maxControls

integer reflecting the maximal numbers of controls to use. If set to 0 all highly correlated controls are used. Default = 25

corrThresh

threshold for selecting highly correlated controls. Default = 0.99

sex

either "mixed", "male", or "female" reflecting the sex of all samples (test and control)

Value

list of instances of "CNVDetectionResult"

Examples

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data(panelcn.mops)
XandCB <- test
elementMetadata(XandCB) <- cbind(elementMetadata(XandCB), 
                                    elementMetadata(control))
resultlist <- runPanelcnMops(XandCB, countWindows = countWindows)

panelcn.mops documentation built on Nov. 8, 2020, 7:56 p.m.