Package 'genoCN'

Title: genotyping and copy number study tools
Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations
Authors: Wei Sun and ZhengZheng Tang
Maintainer: Wei Sun <[email protected]>
License: GPL (>=2)
Version: 1.57.0
Built: 2024-07-25 05:13:18 UTC
Source: https://github.com/bioc/genoCN

Help Index

code bi-allele genotype to numerical value

Description

code a genotype vector, e.g. ("AA", "AC", ...) to a numerical vector based on the count of minor allele, e.g., (0, 1, ...)

Usage

code.genotype(v)

Arguments

v

character vector of genotypes

Value

a numerical vector of genotype

Author(s)

Wei Sun [email protected]

Copy Number Aberration

Description

extract genotype and copy number calls for copy number aberrations, which are often observed in tumor tissues

Usage

genoCNA(snpNames, chr, pos, LRR, BAF, pBs, sampleID, 
  Para=NULL, fixPara=FALSE, cnv.only=NULL, estimate.pi.r=TRUE, 
  estimate.pi.b=TRUE, estimate.trans.m=TRUE, outputSeg = TRUE, 
  outputSNP=3, outputTag=sampleID, outputViterbi=FALSE, 
  Ds=c(1e10, 1e10, rep(1e8, 7)), pBs.alpha=0.001, contamination=TRUE, 
  normalGtp=NULL, geno.error=0.01, min.tp=1e-4, max.diff=0.1, 
  distThreshold=1e6, transB=c(0.5,.05,.05,0.1,0.1,.05,.05,.05,.05), 
  epsilon=0.005, K=5, maxIt=200, seg.nSNP=3, traceIt=5)

Arguments

snpNames

a vector of SNP names. SNPs must be ordered by chromosme locations

chr

chromosomes of all the SNPs specified in snpNames

pos

positions of all the SNPs specified in snpNames
LRR

Log R Ratio of all the SNPs specified in snpNames
BAF

B Allele Frequency of all the SNPs specified in snpNames
pBs

population frequency of of all the SNPs specified in snpNames

sampleID

symbol/name of the studied sample. Only one sample is studied each time

Para

a list of initial parameters for the HMM. If Para is NULL, The default initial parameters: init.Para.CNA is used

fixPara

if fixPara is TRUE, the parameters in Para are fixed, and are used directly to calculate posterior probabilities. It is not recommended to set fixPara as TRUE for CNA studies.

cnv.only

a vector indicating those CNV-only probes, for which we only consider their Log R ratio. If it is NULL, there is no CNV-only probes

estimate.pi.r

to estimate pi.r (proportion of uniform component for LRR) or not. By default, estimate.pi.r=FALSE, and the initial value of pi.r is used to estimate other parameters

estimate.pi.b

to estimate pi.b (proportion of uniform component for BAF) or not. By default, estimate.pi.b=FALSE, and the initial value of pi.b is used to estimate other parameters

estimate.trans.m

to estimate transition probability matrix or not. By default, estimate.trans.m=FALSE, and the initial value of estimate.trans.m is used to estimate other parameters

outputSeg

wether to output the information of copy number altered segments

outputSNP

if outputSNP is 0, do not output SNP specific information; if outputSNP is 1, output the most likely copy number and genotype state of the SNPs that are within copy number altered regions; if outputSNP is 2, output the most likely copy number and genotype state of all the SNPs (whether it is within CNV regions or not), if outputSNP is 3, output the posterior probability for all the copy number and genotype states for the SNPs.

outputTag

the prefix of the output files, output of copy number altered segments is written into file outputTag\_segment.txt, and output of SNP information is written into file outputTag\_SNP.txt

outputViterbi

whether to output the copy altered regions identified by the viterbi algorithm. see details

Ds

Parameter to for transition probability of the HMM. A vector of length N, where N is the number of states in the HMM

pBs.alpha

pBs.alpha is the lower limit of population B allele frequency, and the upper limit is 1 - pBs.alpha

contamination

whether tissue contamination is considered

normalGtp

normalGtp is specified only if paired tumor-normal SNP array is availalble. It is the normal tissue genotype for all the SNPs specified in snpNames, which can only take four different values: -1, 0, 1, and 2. Values 0, 1, 2 correspond to the number of B alleles, and value -1 indicates the normal genotype is missing. By default, it is NULL, then all the normal genotype are set missing (-1)

geno.error

probability of genotyping error in normal tissue genotypes

min.tp

the minimum of transition probability.

max.diff

Due to normalization procedure, the BAF may not be symmetric. Let's use state (AAA, AAB, ABB, BBB) as an example. Ideally, mean values of normal components AAB and ABB, denoted by mu1 and mu2, respectively, should have the relation mu1 = 1-mu2 if BAF is symmetric. However, this may not be true due to normalization procedures. We restrict the difference of mu1 and (1-mu2) by this parameter max.diff.

distThreshold

If distance between adjacent probes is larger than distThreshold, restart the transition probability by the default values in transB.
transB

The default transition probability.

epsilon

see explanation of K
K

epsilon and K are used to specify the convergence criteria. We say the estimate.para is converged if for K consecutive updates, the maximum change of parameter estimates in every adjacent step is smaller than epsilon

maxIt

the maximum number of iterations of the EM algorithm to estimate parameters

seg.nSNP

the minimum number of SNPs per segment

traceIt

if traceIt is a integer n, then the running time is printed out in every n iterations of the EM algorithm. if traceIt is 0 or negative, no tracing information is printed out.

Value

results are written into output files

Note

Copy number altered regions are identified, by default, based on the SNP level copy number calls. A CNA region boundary is declared simply when the adjacent SNPs have different copy numbers. An alternative approach is to use viterbi algorithm to output the “best path”. Most time the results based on the SNP level copy number calls are the same as the results from viterbi algorithm. For the following up association studies, the SNP level information is more relevant if we examine the association SNP by SNP.

Author(s)

Wei Sun and Zhengzheng Tang

Examples

data(snpData)
data(snpInfo)

dim(snpData)
dim(snpInfo)

snpData[1:2,]
snpInfo[1:2,]

snpInfo[c(1001,1100,10001,10200),]

plotCN(pos=snpInfo$Position, LRR=snpData$LRR, BAF=snpData$BAF, 
main = "simulated data on Chr22")

snpNames = snpInfo$Name
chr = snpInfo$Chr
pos = snpInfo$Position
LRR = snpData$LRR
BAF = snpData$BAF
pBs = snpInfo$PFB
cnv.only=(snpInfo$PFB>1)
sampleID="simu1"

# Note this simulated data is more of CNV rather than CNA. 
# For example, there is no tissue contamination. 
# We just use it to illustrate the usage of genoCNA. 

Theta = genoCNA(snpNames, chr, pos, LRR, BAF, pBs, contamination=TRUE, 
  normalGtp=NULL, sampleID, cnv.only=cnv.only, outputSeg = TRUE, 
            outputSNP = 1, outputTag = "simu1")

Copy Number Variation

Description

extract genotype and copy number calls for copy number variation, which are inheritable DNA polymorphisms and are observed in normal tissues

Usage

genoCNV(snpNames, chr, pos, LRR, BAF, pBs, sampleID, 
  Para=NULL, fixPara=FALSE, cnv.only=NULL, estimate.pi.r=TRUE, 
  estimate.pi.b=FALSE, estimate.trans.m=FALSE, normLRR=TRUE, 
  outputSeg=TRUE, outputSNP=3, outputTag=sampleID, outputViterbi=FALSE, 
  Ds = c(1e6, 1e6, rep(1e5, 4)), 
  pBs.alpha=0.001, loh=FALSE, output.loh=FALSE, 
  min.tp=5e-5, max.diff=0.1, distThreshold=5000, 
  transB = c(0.995, 0.005*c(.01, .09, .8, .09, .01)), 
  epsilon=0.005, K=5, maxIt=200, seg.nSNP=3, traceIt=5)

Arguments

snpNames

a vector of SNP names. SNPs must be ordered by chromosome locations

chr

chromosomes of all the SNPs specified in snpNames

pos

positions of all the SNPs specified in snpNames
LRR

Log R Ratio of all the SNPs specified in snpNames
BAF

B Allele Frequency of all the SNPs specified in snpNames
pBs

population frequency of of all the SNPs specified in snpNames

sampleID

symbol/name of the studied sample. Only one sample is studied each time

Para

a list of initial parameters for the HMM. If Para is NULL, The default initial parameters: init.Para.CNV is used

fixPara

if fixPara is TRUE, the parameters in Para are fixed, and are used directly to calculate posterior probabilities

cnv.only

a vector indicating those CNV-only probes, for which we only consider their Log R ratio. If it is NULL, there is no CNV-only probes

estimate.pi.r

to estimate pi.r (proportion of uniform component for LRR) or not. By default, estimate.pi.r=FALSE, and the initial value of pi.r is used to estimate other parameters

estimate.pi.b

to estimate pi.b (proportion of uniform component for BAF) or not. By default, estimate.pi.b=FALSE, and the initial value of pi.b is used to estimate other parameters

estimate.trans.m

to estimate transition probability matrix or not. By default, estimate.trans.m=FALSE, and the initial value of estimate.trans.m is used to estimate other parameters

normLRR

If normLRR is TRUE, we normalize the LRR data by subtracting the median LRR for those LRR between -2 and 2. This strategy has been used by PennCNV.
outputSeg

wether to output the information of copy number altered segments

outputSNP

if outputSNP is 0, do not output SNP specific information; if outputSNP is 1, output the most likely copy number and genotype state of the SNPs that are within copy number altered regions; if outputSNP is 2, output the most likely copy number and genotype state of all the SNPs (whether it is within CNV regions or not), if outputSNP is 3, output the posterior probability for all the copy number and genotype states for the SNPs.

outputTag

the prefix of the output files, output of copy number altered segments is written into file outputTag\_segment.txt, and output of SNP information is written into file outputTag\_SNP.txt

outputViterbi

whether to output the copy altered regions identified by the viterbi algorithm. see details

Ds

Parameter to for transition probability of the HMM. A vector of length N, where N is the number of states in the HMM

pBs.alpha

pBs.alpha is the lower limit of population B allele frequency, and the upper limit is 1 - pBs.alpha

loh

Whether we use the copy-number-neutral loss of heterozygosity state for CNV studies.

output.loh

Whether we output the loh information.

min.tp

the minimum of transition probability.

max.diff

Due to normalization procedure, the BAF may not be symmetric. Let's use state (AAA, AAB, ABB, BBB) as an example. Ideally, mean values of normal components AAB and ABB, denoted by mu1 and mu2, respectively, should have the relation mu1 = 1-mu2 if BAF is symmetric. However, this may not be true due to normalization procedures. We restrict the difference of mu1 and (1-mu2) by this parameter max.diff.

distThreshold

If distance between adjacent probes is larger than distThreshold, restart the transition probability by the default values in transB.
transB

The default transition probability.

epsilon

see explanation of K

K

epsilon and K are used to specify the convergence criteria. We say the estimate.para is converged if for K consecutive updates, the maximum change of parameter estimates in every adjacent step is smaller than epsilon

maxIt

the maximum number of iterations of the EM algorithm to estimate parameters

seg.nSNP

the minimum number of SNPs per segment

traceIt

if traceIt is a integer n, then the running time is printed out in every n iterations of the EM algorithm. if traceIt is 0 or negative, no tracing information is printed out.

Value

results are written into output files

Note

Copy number altered regions are identified, by default, based on the SNP level copy number calls. A CNV region boundary is declared simply when the adjacent SNPs have different copy numbers. An alternative approach is to use viterbi algorithm to output the “best path”. Most time the results based on the SNP level copy number calls are the same as the results from viterbi algorithm. For the following up association studies, the SNP level information is more relevant if we examine the association SNP by SNP.

Author(s)

Wei Sun and Zhengzheng Tang

Examples

data(snpData)
data(snpInfo)

dim(snpData)
dim(snpInfo)

snpData[1:2,]
snpInfo[1:2,]

snpInfo[c(1001,1100,10001,10200),]

plotCN(pos=snpInfo$Position, LRR=snpData$LRR, BAF=snpData$BAF, 
main = "simulated data on Chr22")

snpNames = snpInfo$Name
chr = snpInfo$Chr
pos = snpInfo$Position
LRR = snpData$LRR
BAF = snpData$BAF
pBs = snpInfo$PFB
cnv.only=(snpInfo$PFB>1)
sampleID="simu1"

Theta = genoCNV(snpNames, chr, pos, LRR, BAF, pBs, 
            sampleID, cnv.only=cnv.only, outputSeg = TRUE, 
            outputSNP = 1, outputTag = "simu1")

Initial parameters for the HMM

Description

a list of initial values for the parameters of genoCNA.

Usage

data(init.Para.CNA)

Format

The format is a list of 16 items

  • pi.r a vector of length N, where N is the number of states. pi.r[j] is the prior probability of the uniform component of log R ratio for state j

  • mu.r a vector of length N, where N is the number of states. mu.r[j] is mean value of the normal component of log R ratio for state j

  • sd.r a vector of length N, where N is the number of states. sd.r[j] is standard deviation of the normal component of log R ratio for state j

  • mu.r.upper, mu.r.lower two vectors of the same size of mu.r, indicating the upper/lower bound of mu.r

  • sd.r.upper, sd.r.lower two vectors of the same size of sd.r, indicating the upper/lower bound of sd.r

  • pi.b a vector of length N, where N is the number of states. pi.b[j] is the prior probability of the uniform component of B allele frequency for state j

  • mu.b a matrix of N*M, where N is the number of states, and M is the maximum number of components of each states. mu.b[i,j] indicates the mean value of the j-th component of the i-th state

  • sd.b a matrix of the same size of mu.b, specifying the standard deviations

  • mu.b.upper, mu.b.lower two matrices of the same size of mu.b, incating the upper/lower bound of mu.b

  • sd.b.upper, sd.b.lower two matrices of the same size of sd.b, indicating the upper/lower bound of sd.b

  • trans.m transition probability matrix of size N*N. The diagonal elements are not used.

  • trans.begin a matrix of size S*N, where S is the number of chromosomes, and N is the number of states. trans.begin[s,] are the state probabilities for the fist probe of the s-th chromosome. By default, we assume there is only one chromosome, therefore it is a matrix of 1*N.

Examples

data(init.Para.CNA)

Initial parameters for the HMM of genoCNV

Description

a list of initial values for the parameters genoCNV.

Usage

data(init.Para.CNV)

Format

The format is a list of 16 items

  • pi.r a vector of length N, where N is the number of states. pi.r[j] is the prior probability of the uniform component of log R ratio for state j

  • mu.r a vector of length N, where N is the number of states. mu.r[j] is mean value of the normal component of log R ratio for state j

  • sd.r a vector of length N, where N is the number of states. sd.r[j] is standard deviation of the normal component of log R ratio for state j

  • mu.r.upper, mu.r.lower two vectors of the same size of mu.r, incating the upper/lower bound of mu.r

  • sd.r.upper, sd.r.lower two vectors of the same size of sd.r, indicating the upper/lower bound of sd.r

  • pi.b a vector of length N, where N is the number of states. pi.b[j] is the prior probability of the uniform component of B allele frequency for state j

  • mu.b a matrix of N*M, where N is the number of states, and M is the maximum number of components of each states. mu.b[i,j] indicates the mean value of the j-th component of the i-th state

  • sd.b a matrix of the same size of mu.b, specifying the standard deviations

  • mu.b.upper, mu.b.lower two matrices of the same size of mu.b, incating the upper/lower bound of mu.b

  • sd.b.upper, sd.b.lower two matrices of the same size of sd.b, indicating the upper/lower bound of sd.b

  • trans.m transition probability matrix of size N*N. The diagonal elements are not used.

  • trans.begin a matrix of size S*N, where S is the number of chromosomes, and N is the number of states. trans.begin[s,] are the state probabilities for the fist probe of the s-th chromosome. By default, we assume there is only one chromosome, therefore it is a matrix of 1*N.

Examples

data(init.Para.CNV)

plot LRR, BAF, and the copy number estimates

Description

plot LRR, BAF, and the copy number estimates of genoCNV and/or PennCNV.

Usage

plotCN(pos, LRR, BAF, chr2plot = NULL, sampleIDs = NULL, fileNames=NULL, 
types = "genoCN", CNA = TRUE, main = "", LRR.ylim=NULL,
cex=0.5, plot.lowess=TRUE)

Arguments

pos

position of all the SNPs

LRR

a vector of the log R ratio, should be one-to-one correspondence of pos

BAF

a vector of the B allele frequency, should be one-to-one correspondence of pos

chr2plot

which chromosome to plot. Only one chromosome can be plotted each time

sampleIDs

sample ID, could be a vector of the same length as fileNames so that different sample IDs are used for different input files.

fileNames

one or more names of the output files of genoCN or PennCNV. If it is NULL, only plot the LRR and BAF.

types

should be the same length as fileNames, indicating the type of output, currently only support "genoCN" and "pennCNV"

CNA

whether this is a copy number aberration study.

main

title of the plot

LRR.ylim

Range of y-axis for LRR plot

cex

the amount by which plotting text and symbols should be magnified relative to the default
plot.lowess

to plot the lowess curve for LRR or not

Author(s)

Wei Sun

See Also

genoCNA, genoCNV

Examples

data(snpData)
data(snpInfo)

dim(snpData)
dim(snpInfo)

snpData[1:2,]
snpInfo[1:2,]

snpInfo[c(1001,1100,10001,10200),]

plotCN(pos=snpInfo$Position, LRR=snpData$LRR, BAF=snpData$BAF, 
main = "simulated data on Chr22")

Simulated LRR and BAF data for 17,348 SNPs on chromosome 22.

Description

Simulated LRR and BAF data for 17,348 SNPs on chromosome 22. Two CNVs are simulated. One is from the 1001-th probe to the 1100-th probe, with copy number 1. The other one is from the 10,001-th probe to the 10,200-th probe, with copy number 3.

Usage

data(snpData)

Format

A data frame with 17,348 observations on the following 3 variables.

Name

a character vector of probe Names

LRR

a numeric vector of LRR values of each probe

BAF

a numeric vector of BAF of each probe

Examples

data(snpData)
data(snpInfo)

dim(snpData)
dim(snpInfo)

snpData[1:2,]
snpInfo[1:2,]

plotCN(pos=snpInfo$Position, LRR=snpData$LRR, BAF=snpData$BAF, 
main = "simulated data on Chr22")

Information of 17,348 SNPs on chromosome 22.

Description

Information of 17,348 SNPs on chromosome 22.

Usage

data(snpInfo)

Format

A data frame with 17348 observations on the following 4 variables.

Name

a character vector of probe Names

Chr

a character vector of chromosomes of each probe

Position

a numeric vector of genomic position of each probe

PFB

a numeric vector of population frequency of B allele for each probe. For copy number only probes, PFB=2.0

Examples

data(snpData)
data(snpInfo)

dim(snpData)
dim(snpInfo)

snpData[1:2,]
snpInfo[1:2,]

plotCN(pos=snpInfo$Position, LRR=snpData$LRR, BAF=snpData$BAF, 
main = "simulated data on Chr22")