Method for copy number variation detection for dried blood spots.
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DESCRIPTION
NAMESPACE
README.md
iPsychCNV.Rproj

README.md

iPsychCNV

##In R: ### To install setRepositories(ind=1:8) # 1 = CRAN , 2 = BioCsoft, 8 = http://R-Forge.R-project.org. library(devtools) install_github("mbertalan/iPsychCNV") ### Load the package library(iPsychCNV)

## Testing iPsychCNV # Creating a long mock file (one chromosome). LongRoi <- MakeLongMockSample(CNVDistance=1000, Type=c(0,1,2,3,4), Mean=c(-0.6, -0.3, 0.3, 0.6), Size=c(300, 600))

# Running iPsychCNV on long mock data.
CNVs <- iPsychCNV(PathRawData=".", Pattern="^LongMockSample.tab$", Skip=0)

# Reading long mock to an object in R.
Sample <- read.table("LongMockSample.tab", sep="\t", header=TRUE, stringsAsFactors=F)

# Plotting LRR and BAF from 
PlotLRRAndCNVs(CNVs, Sample, CNVMean=0.3, Name="LRR_BAF_Test_Plot.png", Roi=LongRoi)

# Evaluating CNVs
CNVs.Eval <- EvaluateMockResults(LongRoi, CNVs)

# Print ROC curve.
LongROC <- plot.roc(CNVs.Eval$CNV.Predicted, CNVs.Eval$CNV.Present, percent=TRUE, print.auc=TRUE)

Creating mock data.

# Simulates Infinium PsychArray BeadChip (Illumina).
#  Creates a Mockfile on local folder and returns the CNV position on the mock sample.
MockCNVs <- MockData(N=1, Type="Blood", Cores=1)

# Predicting CNVs
CNVs <- iPsychCNV(PathRawData=".", Cores=1, Pattern="*.tab", MINNumSNPs=20, LCR=FALSE, MinLength=10, Skip=0)

# Subset all CNVs with copy number (CN) different from 2.
CNVs.Good <- subset(CNVs, CN != 2)

# Creating ROI for Mock Data.
# ROI: Regions of interest (CNV position on the sample). 
MockCNVs.Roi <- subset(MockCNVs, CN != 2)
MockCNVs.Roi$Class <- rep("ROI", nrow(MockCNVs.Roi))

# Ploting CNVs. 
# It create a file (test.png) on your local folder.
PlotAllCNVs(CNVs.Good, Name="test.png", Roi=MockCNVs.Roi)

Evaluating CNVs

CNVs.Eval <- EvaluateMockResults(MockCNVs, CNVs.Good)

# Ploting evaluation using ROC curve.  
rocobj <- plot.roc(CNVs.Eval$CNV.Predicted, CNVs.Eval$CNV.Present, percent=TRUE,  print.auc=TRUE)