iPsychCNV is an R package software capable of finding copy number variation in amplified DNA from dried blood spots on Illumina SNP array. It can handle large variation on Log R ratio, and use B allele frequency to improve CNV calls. The software is open source, and we welcome suggestions, and that people code new functions and/or improve existing ones. The source code is available at Github.

 

 


Contact

Xabier Calle Sanchez
Research Institute of Biological Psychiatry, Psychiatric Center Sct. Hans.
Roskilde, Denmark.
Boserupvej 2, 4000.
Tel: +45 38 64 20 00
Email: xabier.calle.sanchez@regionh.dk


How to install

INSTALL IPSYCHCNV PACKAGE

# To run iPsychCNV you need R.
# To download and install R, see R-Project

# In R:# Install packages: 1 = CRAN , 2 = BioCsoft, 8 = http://R-Forge.R-project.org (for Gada).
# See setRepositories(ind=1:10), getOption("repos").

setRepositories(ind=1:8)
library(devtools)
install_github("mbertalan/iPsychCNV")

# Download Master file from GitHub.
iPsychCNV R-package
setRepositories(ind=1:8)
library(devtools)
install_url(iPsychCNV-master)

## Common errors ##

1-) Error in library(devtools) : there is no package called "devtools"
To fix the error you must install devtools first.install.packages("devtools")

2-) Installation of devtools fails. 
This may be solved by installing the OS package libcurlmultiple threads can be found on this issue. 
http://stackoverflow.com/questions/26445815/error-when-installing-devtools-package-for-r-in-ubuntu

3-) A number of dependent R-packages fail as the installer cannot find -lquadmath. 
This issue may be resolved by installing r-base-dev. 
See http://stackoverflow.com/questions/6302209/building-r-package-getting-error-ld-cannot-find-lgfortran.

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TUTORIAL

# In R:
# Load iPsychCNV package
library(iPsychCNV)

RUN IPSYCHCNV PACKAGE

## Creates a mock data that simulates Infinium PsychArray BeadChip from Illumina.
## Mock data uses same SNP position as PsychArray chip.
MockCNVs <- MockData(N=1, Type="Blood", Cores=1)

# Running iPsychCNV on mock data.
CNVs <- iPsychCNV(PathRawData=".", Cores=1, Pattern="^MockSample*", Skip=0)




# PathRawData: Data path.
# Cores = 1, number of CPUs to be used.
# Pattern = "^MockSample*". Scan for the pattern in a given folder.

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EVALUATE PERFORMANCE

# Test and evaluate three methods using long mock data.
CNV.Prediction <- RunLongMock(Name="All", Method="All",
HMM="/media/NeoScreen/NeSc_home/share/Programs/penncnv/lib/hhall.hmm",
Path2PennCNV="/media/NeoScreen/NeSc_home/share/Programs/penncnv/")

 


iPsychCNV prediction.

 


Gada prediction.


PennCNV prediction PFB 0.5 for each SNP (PennCNV NO).


PennCNV prediction PFB calculated for each SNP (PennCNV NA).

 


ROC curve.

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HOTSPOTS

# Creating a mock data.
MockCNVs <- MockData(N=10, Type="Blood", Cores=10)

# MockCNVs data.frame format (detail information about CNVs in each sample):
str(MockCNVs)
'data.frame':  5480 obs. of  15 variables:
$ Start     : int  752566 6530391 7912975 14210730 16847832 20645925 21009279 27210667 29763684 35824753 ...
$ Stop      : int  6530391 7912975 14210730 16847832 20645925 21009279 27210667 29763684 35824753 36557619 ...
$ StartIndx : num  1 2000 2400 4000 4800 6000 6150 8000 8600 10000 ...
$ StopIndx  : num  2000 2400 4000 4800 6000 6150 8000 8600 10000 10200 ...
$ NumSNPs   : num  1999 400 1600 800 1200 ...
$ Chr       : int  1 1 1 1 1 1 1 1 1 1 ...
$ CNVmean   : num  0 0.35 0 -0.35 0 -0.4 0 0.5 0 -0.35 ...
$ CN        : num  2 4 2 1 2 0 2 3 2 1 ...
$ sd        : num  0.2 0.15 0.2 0.15 0.2 0.15 0.2 0.15 0.2 0.15 ...
$ ID        : chr  "MockSample_1.tab" "MockSample_1.tab" "MockSample_1.tab" "MockSample_1.tab" ...
$ NumCNVs   : num  25 25 25 25 25 25 25 25 25 25 ...
$ ChrMean   : num  0.0456 0.0456 0.0456 0.0456 0.0456 ...
$ Length    : int  5777825 1382584 6297755 2637102 3798093 363354 6201388 2553017 6061069 732866 ...
$ CNVID     : int  1 1 2 2 3 3 4 4 5 5 ...
$ PositionID: chr  "1_2000" "2000_2400" "2400_4000" "4000_4800" ...

# Predicting CNVs using iPsychCNV
CNVs <- iPsychCNV(PathRawData=".", Pattern="^MockSample*", Skip=0)
CNVs.Good <- subset(CNVs, CN != 2) # keep only CNVs with CN = 0, 1, 3, 4.

# iPsychCNV results data.frame format:
str(CNVs.Good)
'data.frame':  2579 obs. of  56 variables:
$ Start           : int  95947234 6530372 14209336 20645874 27190196 35824706 43672394 53164220 62253546 74174778 ...
$ Stop            : int  98808760 7912975 16847832 21009279 29763684 36557619 46094037 54546449 62676947 74737345 ...
$ CNVMean         : num  -0.279 0.259 -0.245 -0.283 0.426 ...
$ StartIndx       : num  21999 1999 3999 5999 7999 ...
$ StopIndx        : num  22400 2400 4800 6150 8600 ...
$ Length          : int  2861526 1382603 2638496 363405 2573488 732913 2421643 1382229 423401 562567 ...
$ NumSNPs         : num  401 401 801 151 601 201 801 401 151 101 ...
$ Chr             : int  1 1 1 1 1 1 1 1 1 1 ...
$ ID              : chr  "MockSample_1.tab" "MockSample_1.tab" "MockSample_1.tab" "MockSample_1.tab" ...
$ CN              : num  0 4 1 0 3 1 4 4 1 3 ...
$ CNVID           : chr  "1" "2" "3" "4" ...
$ AAAA            : num  12.9 37.8 48 13.2 42.7 49 35 37.1 55.9 41.2 ...
$ AAAB            : num  19.2 0 0.1 19.7 0 0 0 0 0 0 ...
$ AAB             : num  16.2 8.5 0 10.5 9.6 0 7.9 7.5 0 8.8 ...
$ AB              : num  14.7 8.2 0 19.7 0 0 9.6 13.4 0 0 ...
$ ABB             : num  13.4 8.2 0 10.5 8.1 0 8.9 9 0 14.7 ...
$ ABBB            : num  14.9 0 0 18.4 0 0 0 0 0 0 ...
$ BBBB            : num  8.7 37.3 51.9 7.9 39.5 51 38.7 33.1 44.1 35.3 ...
$ UsedBAF         : num  100 100 100 100 100 100 100 100 100 100 ...
$ CNVmean         : num  -0.285 0.259 -0.245 -0.283 0.426 ...
$ HighMean        : num  0.00403 0.01017 0.00521 0.00845 -0.00302 ...
$ LowMean         : num  0.010138 0.001656 0.01133 0.011527 -0.000432 ...
$ CNVmeanOrig     : num  -0.373 0.374 -0.325 -0.371 0.524 ...
$ SDCNV           : num  0.029 0.0256 0.0229 0.0386 0.0285 ...
$ SDMeansCNV      : num  0.00742 0.0061 0.00549 0.01332 0.00549 ...
$ SDHigh          : num  0.111 0.112 0.112 0.115 0.114 ...
$ SDLow           : num  0.109 0.114 0.111 0.11 0.114 ...
$ VarianceCNV     : num  0.00084 0.000657 0.000525 0.001488 0.000812 ...
$ DiffHigh        : num  0.289 0.249 0.251 0.291 0.429 ...
$ DiffLow         : num  0.295 0.257 0.257 0.294 0.426 ...
$ Wave.Length.Mean: num  3.04 2.91 2.96 3.13 2.9 ...
$ Wave.Length.SD  : num  1.084 0.956 1.098 1.169 0.924 ...
$ Amplitude.Mean  : num  0.001397 -0.001268 0.000632 0.004513 -0.001248 ...
$ Amplitude.SD    : num  0.0475 0.0431 0.041 0.0573 0.0465 ...
$ CNV2HighPvalue  : num  4.77e-188 1.29e-158 3.63e-318 3.65e-71 0.00 ...
$ CNV2LowPvalue   : num  5.93e-194 3.97e-161 0.00 4.25e-75 0.00 ...
$ BAlleleFreq     : chr  "Undefined" "Undefined" "1" "Undefined" ...
$ LogRRatio       : num  1 3 1 1 3 1 3 3 1 3 ...
$ SumPeaks        : int  1 3 2 3 2 2 3 3 2 2 ...
$ MyBAF           : num  0 4 1 0 3 1 4 4 1 3 ...
$ SDChr           : num  0.196 0.196 0.196 0.196 0.196 ...
$ MeanChr         : num  0.0188 0.0188 0.0188 0.0188 0.0188 ...
$ Source          : chr  "iPsychCNV" "iPsychCNV" "iPsychCNV" "iPsychCNV" ...

# Searching for hotspots
CNVs.Hotspots <- HotspotsCNV(df=CNVs.Good, Freq=1, OverlapCutoff=0.7, Cores=22)

# Hotspots data.frame format:
str(CNVs.Hotspots)
'data.frame':  260 obs. of  18 variables:
$ Chr      : num  1 1 1 1 1 1 1 1 1 1 ...
$ Start    : num  1.10e+08 1.17e+08 1.42e+07 1.51e+08 1.55e+08 ...
$ Stop     : num  1.12e+08 1.18e+08 1.68e+07 1.52e+08 1.57e+08 ...
$ Length   : num  2658481 1794999 2638496 1307606 1952779 ...
$ Count    : num  10 8 9 10 10 10 8 10 9 8 ...
$ Source   : chr  "iPsychCNV" "iPsychCNV" "iPsychCNV" "iPsychCNV" ...
$ NewName  : chr  "1_109650542_112309023_2658481" "1_116691120_118486119_1794999"...
$ CHR      : chr  "chr1" "chr1" "chr1" "chr1" ...
$ CNV_Start: num  1.10e+08 1.17e+08 1.42e+07 1.51e+08 1.55e+08 ...
$ CNV_Stop : num  1.12e+08 1.18e+08 1.68e+07 1.52e+08 1.57e+08 ...
$ locus    : chr  " 1p13.3-p13.2" " 1p13.1-p12" " 1p36.21-p36.13" " 1q21.3" ...
$ ID       : chr  "ROI" "ROI" "ROI" "ROI" ...
$ Class    : chr  "ROI" "ROI" "ROI" "ROI" ...
$ CN0      : num  4 2 4 2 1 4 3 4 4 0 ...
$ CN1      : num  1 3 6 1 4 2 3 3 2 3 ...
$ CN3      : num  2 1 0 1 3 3 3 1 2 4 ...
$ CN4      : num  3 4 0 6 2 1 0 2 1 2 ...
$ CNTotal  : num  10 10 10 10 10 10 9 10 9 9 ...

# Ploting CNVs and Hotspots
PlotAllCNVs(CNVs.Good, Name="CNVs.Hotspots.png", Roi=CNVs.Hotspots)


CNV Prediction from iPsychCNV and hotspots.

 

# Plotting individual CNVs from a data.frame
PlotCNVsFromDataFrame(DF=CNVs.Good[1:4,], PathRawData=".", Cores=1, Skip=0, Pattern="^MockSamples*")


Example of mock CNV with CN = 0.


Example of mock CNV with CN = 1.


Example of mock CNV with CN = 3.


Example of mock CNV with CN = 4.

 

Hotspot visual inspection.

Example of true hotspot duplication (CN=3) on real data. Plot data from multiple CNVs in a hotspots.

 

PennCNV and iPsychCNV on DBS data: Hotspots and telomere regions.

DBS data from chromosome 16 from PennCNV and iPsychCNV. Example of hotspots and false positive at telomere region.

 

PennCNV on mock data: Telomere regions.

Mock data example of false positive prediction on telomere regions by PennCNV

 

 

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LONG MOCK

# Long mock has only one chromosome, and doesn't use PsychArray chip information.
# It is created to test combination of LRR and BAF found in DBS data.
# Input for long mock. More variables can be included.
# For example: Size=c(100, 300, 600, 2000)

CNVDistance=1000                   # Distance among CNVs.
Type=c(0,1,2,3,4)                  # CNV copy number.
Mean=c(-0.6, -0.3, 0.3, 0.6)       # CNV LRR mean.
Size=c(300, 600)                   # CNV number of SNPs.
Method = "iPsychCNV"               # Method used
Name="LongMock"                    #
CNVMean=0.3                        # If method has no CNV mean output
Name <- paste(Name,"_",Method, "_LongMockResult.png", sep="", collapse="") # png plot name.

# Running long mock step by step
LongRoi <- MakeLongMockSample(CNVDistance, Type, Mean, Size)

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

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

# Plotting LRR and BAF from
PlotLRRAndCNVs(PredictedCNV, Sample, CNVMean, Name=Name, Roi=LongRoi)

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

# Print ROC curve.
plot.roc(CNVs.Eval$CNV.Predicted, CNVs.Eval$CNV.Present, percent=TRUE, col="#1c61b6", print.auc=TRUE)

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IPSYCHCNV STEP BY STEP

# Function to read sample.
Mock.chr22 <- ReadSample(RawFile="MockSample_1.tab", skip=0, LCR=FALSE, chr=22)

# Function to find CNVs.
Mock.chr22.CNVs <- FindCNV.V4(ID="Test", MinNumSNPs=10, CNV=Mock.chr22)

# Function to validate CNVs.
Mock.chr22.CNVs.validation <- FilterCNVs.V4(CNVs=Mock.chr22.CNVs, MinNumSNPs=10, CNV=Mock.chr22, ID="Test")

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MERGE CNVS

# Using low penvalue to break CNV prediction.
Mock.chr22.CNVs <- FindCNV.V4(ID="Test", MinNumSNPs=10, CNV=Mock.chr22, CPTmethod="meanvar", penvalue=10)

# Second and Third CNV should be merged. Gap of only 11 SNPs or 47171 bp.
str(Mock.chr22.CNVs)
'data.frame':  5 obs. of  10 variables:
$ Start    : int  24583365 32009612 33430734 38864287 45309851
$ Stop     : int  25938025 33383563 35786182 39385595 46854398
$ CNVMean  : num  -0.454 0.387 0.391 0.428 0.379
$ StartIndx: num  1999 4006 4424 5999 7999
$ StopIndx : num  2400 4413 4800 6139 8600
$ Length   : int  1354660 1373951 2355448 521308 1544547
$ NumSNPs  : num  401 407 376 140 601
$ Chr      : int  22 22 22 22 22
$ ID       : chr  "Test" "Test" "Test" "Test" ...
$ CN       : num  1 3 3 3 3

Mock.chr22.CNVs.merged <- MergeCNVs(Mock.chr22.CNVs, MaxNumSNPs=50)
str(Mock.chr22.CNVs.merged)
'data.frame':  4 obs. of  10 variables:
$ Start    : int  24583365 32009612 38864287 45309851
$ Stop     : int  25938025 35786182 39385595 46854398
$ StartIndx: num  1999 4006 5999 7999
$ StopIndx : num  2400 4800 6139 8600
$ Length   : int  1354660 3776570 521308 1544547
$ NumSNPs  : num  401 794 140 601
$ CNVMean  : num  -0.454 0.387 0.428 0.379
$ Chr      : int  22 22 22 22
$ ID       : chr  "Test" "Test" "Test" "Test"
$ CN       : num  1 3 3 3

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FILTER CNVS FROM OTHER METHODS

# It is possible to validate CNVs using prediction from other methods.
# For example, if you have PennCNV results it is possible to run the filter to collect CNV variables
#(that are required to run support vector machine) and get a preliminary evaluation.
LongRoi <- MakeLongMockSample(Mean=c(-0.6, -0.3, 0.3, 0.6), Size=c(200, 400, 600))

# GADA
Sample <- read.table("LongMockSample.tab", sep="\t", header=TRUE, stringsAsFactors=F)
Gada <- RunGada(Sample)
Gada$ID <- "LongMockSample.tab"
PlotLRRAndCNVs(Gada, tmp=Sample, Name="GadaLong.png", CNVMean=0.3, Roi=LongRoi)


Gada prediction on long mock.

Gada.filter <- FilterFromCNVs(CNVs=Gada, PathRawData=".", MinNumSNPs=10, Source="Gada", Skip=0, Cores=1)
PlotLRRAndCNVs(Gada.filter, tmp=Sample, Name="Gada.filter.Long.png", CNVMean=0.3, Roi=LongRoi)

 


Gada filtered prediction on long mock.

# PennCNV
LongRoi <- MakeLongMockSample(Mean=c(-0.6, -0.3, 0.3, 0.6), Size=c(200, 400, 600))
Sample <- read.table("LongMockSample.tab", sep="\t", header=TRUE, stringsAsFactors=F)
CNVs <- RunPennCNV(PathRawData=".", Pattern="^LongMockSample.tab$", Skip=0,
Path2PennCNV="/home/programs/penncnv.tardis/penncnv/",
HMM="/home/programs/penncnv.tardis/penncnv/lib/hhall.hmm")
PlotLRRAndCNVs(CNV=CNVs, Sample=Sample, Name="PennCNVLong.png", Roi=LongRoi)


PennCNV prediction on long mock.

PennCNV.filter <- FilterFromCNVs(CNVs=CNVs, PathRawData=".", MinNumSNPs=10, Source="PennCNV", Skip=0, Cores=1)
PlotLRRAndCNVs(CNV=PennCNV.filter, Sample=Sample, Name="PennCNV.Long.filter.png", Roi=LongRoi)

 


PennCNV filtered prediction on long mock.

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