Package 'vanquish' reference manual

Package 'vanquish'

Title:	Variant Quality Investigation Helper
Description:	Imports Variant Calling Format file into R. It can detect whether a sample contains contaminant from the same species. In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected.
Authors:	Tao Jiang [aut, cre]
Maintainer:	Tao Jiang <[email protected]>
License:	GPL-2
Version:	1.0.0
Built:	2024-10-27 03:48:51 UTC
Source:	https://github.com/cran/vanquish

Title:

Variant Quality Investigation Helper

Description:

Imports Variant Calling Format file into R. It can detect whether a sample contains contaminant from the same species. In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected.

Authors:

Tao Jiang [aut, cre]

Maintainer:

Tao Jiang <[email protected]>

License:

GPL-2

Version:

1.0.0

Built:

2024-10-27 03:48:51 UTC

Source:

https://github.com/cran/vanquish

Help Index

Default parameters of config.

Description

A dataframe containing default parameters.

Usage

config_df
config_df

Format

A data frame with 12 variables:

threshold: Threshold for allele frequency
skew: Skewness for allele frequency
lower: Lower bound for allele frequency region
upper: Upper bound for allele frequency region
ldpthred: Threshold to determine low depth
hom_mle: Hom MLE of p in Beta-Binomial model
het_mle: Het MLE of p in Beta-Binomial model
Hom_thred: Threshold between hom and high
High_thred: Threshold between high and het
Het_thred: Threshold between het and low
hom_rho: Hom MLE of rho in Beta-Binomial model
het_rho: Het MLE of rho in Beta-Binomial model

Source

Created by Tao Jiang

DEtection of Frequency CONtamination

Description

Detects whether a sample is contaminated another sample of its same species. The input file should be in vcf format.

Usage

defcon(file, rmCNV = FALSE, cnvobj = NULL, config = NULL,
  class_model = NULL, regression_model = NULL)
defcon(file, rmCNV = FALSE, cnvobj = NULL, config = NULL,
  class_model = NULL, regression_model = NULL)

Arguments

`file`	VCF input object
`rmCNV`	Remove CNV regions, default is FALSE
`cnvobj`	CNV object, default is NULL
`config`	config information of parameters. A default set is generated as part of the model and is included in a model object, which contains
`class_model`	An SVM classification model
`regression_model`	An SVM regression model

Value

A list containing (1) stat: a data frame with all statistics for contamination estimation; (2) result: contamination estimation (Class = 0, pure; Class = 1, contaminated)

Examples

data(vcf_example)
result <- defcon(file = vcf_example)
data(vcf_example)
result <- defcon(file = vcf_example)

Feature Generation for Contamination Detection Model

Description

Generates features from each pair of input VCF objects for training contamination detection model.

Usage

generate_feature(file, hom_p = 0.999, het_p = 0.5, hom_rho = 0.005,
  het_rho = 0.1, mixture, homcut = 0.99, highcut = 0.7, hetcut = 0.3)
generate_feature(file, hom_p = 0.999, het_p = 0.5, hom_rho = 0.005,
  het_rho = 0.1, mixture, homcut = 0.99, highcut = 0.7, hetcut = 0.3)

Arguments

`file`	VCF input object
`hom_p`	The initial value for p in Homozygous Beta-Binomial model, default is 0.999
`het_p`	The initial value for p in Heterozygous Beta-Binomial model, default is 0.5
`hom_rho`	The initial value for rho in Homozygous Beta-Binomial model, default is 0.005
`het_rho`	The initial value for rho in Heterozygous Beta-Binomial model, default is 0.1
`mixture`	A vector of whether the sample is contaminated: 0 for pure; 1 for contaminated
`homcut`	Cutoff allele frequency value between hom and high, default is 0.99
`highcut`	Cutoff allele frequency value between high and het, default is 0.7
`hetcut`	Cutoff allele frequency value between het and low, default is 0.3

Value

A data frame with all features for training model of contamination detection

Second alternative allele percentage

Description

Second alternative allele percentage

Usage

getAlt2(f)
getAlt2(f)

Arguments

`f`	Input raw file

Value

Percent of the second alternative allele

Annotation rate

Description

Annotation rate

Usage

getAnnoRate(f)
getAnnoRate(f)

Arguments

`f`	Input raw file

Value

Percentage of annotation locus

Calculate average log-likelihood

Description

Calculate average log-likelihood

Usage

getAvgLL(df, hom_mle, het_mle, hom_rho, het_rho)
getAvgLL(df, hom_mle, het_mle, hom_rho, het_rho)

Arguments

`df`	Input modified file
`hom_mle`	Hom MLE of p in Beta-Binomial model, default is 0.9981416 from NA12878_1_L5
`het_mle`	Het MLE of p in Beta-Binomial model, default is 0.4737897 from NA12878_1_L5
`hom_rho`	Hom MLE of rho in Beta-Binomial model, default is 0.04570275 from NA12878_1_L5
`het_rho`	Het MLE of rho in Beta-Binomial model, default is 0.02224098 from NA12878_1_L5

Value

meanLL

Low depth percentage

Description

Low depth percentage

Usage

getLowDepth(f, ldpthred)
getLowDepth(f, ldpthred)

Arguments

`f`	Input raw file
`ldpthred`	Threshold to determine low depth, default is 20

Value

Percentage of low depth

Get the ratio of allele frequencies with a region

Description

Get the ratio of allele frequencies with a region

Usage

getRatio(subdf, lower, upper)
getRatio(subdf, lower, upper)

Arguments

`subdf`	Dataframe with calculated statistics
`lower`	Lower bound for allele frequency region
`upper`	Upper bound for allele frequency region

Value

Ratio of allele frequencies with a region

Get absolute value of skewness

Description

Get absolute value of skewness

Usage

getSkewness(subdf)
getSkewness(subdf)

Arguments

subdf

Input dataframe

Value

Absolute value of skewness

SNV percentage

Description

SNV percentage

Usage

getSNVRate(df)
getSNVRate(df)

Arguments

`df`	Input raw file

Value

Percentage of SNV

Calculate zygosity variable

Description

Calculate zygosity variable

Usage

getVar(df, state, hom_mle, het_mle)
getVar(df, state, hom_mle, het_mle)

Arguments

`df`	Input modified file
`state`	Zygosity state
`hom_mle`	MLE in hom model
`het_mle`	MLE in het model

Value

Zygosity variable

Check input filename

Description

Check input filename

Usage

locateFile(fn, extension)
locateFile(fn, extension)

Arguments

`fn`	Exact full file name of input file, including directory
`extension`	Expected input file extension: vcf & txt

Value

Valid directory

Negative Log Likelihood

Description

Calculates negative log likelihood for beta binomial distribution.

Usage

negll(x, size, prob, rho)
negll(x, size, prob, rho)

Arguments

`x`	Depth of alternative allele
`size`	Total depth
`prob`	Theoretical probability for heterozygous is 0.5, for homozygous is 0.999
`rho`	Rho parameter of Beta-Binomial distribution of alternative allele

VCF Data Input

Description

Reads a file in vcf or vcf.gz file and creates a list containing Content, Meta, VCF and file_sample_name

Usage

read_vcf(fn, vcffor, dbOnly = FALSE, depCut = FALSE, thred = 20,
  metaline = 200, extnum = 10, keepall = TRUE, filter = FALSE)
read_vcf(fn, vcffor, dbOnly = FALSE, depCut = FALSE, thred = 20,
  metaline = 200, extnum = 10, keepall = TRUE, filter = FALSE)

Arguments

`fn`	Input vcf file name
`vcffor`	Input vcf data format: 1) GATK; 2) VarPROWL; 3) VarDict; 4) strelka2
`dbOnly`	Use dbSNP as filter, default is FALSE
`depCut`	Use a threshold for min depth , default is False
`thred`	Threshold for min depth, default is 20
`metaline`	Number of head lines to read in (better to be large enough), the lines will be checked if they contain meta information, default is 200
`extnum`	The column number to be extracted from vcf, default is 10; 0 for not extracting any column; extnum should be between 10 and total column number
`keepall`	Keep unextracted column in output, default is TRUE
`filter`	Whether to select "PASS" variants for analyses if they contain unfiltered variants, default is FALSE

Value

A list containing (1) Content: a vector showing what is contained; (2) Meta: a data frame containing meta-information of the file; (3) VCF: a data frame, the main part of VCF file; (4) file_sample_name: the file name and sample name, in case when multiple samples exist in one file, file and sample names might be different

Examples

file.name <- system.file("extdata", "example.vcf.gz", package = "vanquish")
example <- read_vcf(fn=file.name, vcffor="VarPROWL")
file.name <- system.file("extdata", "example.vcf.gz", package = "vanquish")
example <- read_vcf(fn=file.name, vcffor="VarPROWL")

Read in input vcf data in GATK format for Contamination detection

Description

Read in input vcf data in GATK format for Contamination detection

Usage

readGATK(dr, dbOnly, depCut, thred, content, extnum, keepall)
readGATK(dr, dbOnly, depCut, thred, content, extnum, keepall)

Arguments

`dr`	A valid input object
`dbOnly`	Use dbSNP as filter, default is FALSE, passed from read_vcf
`depCut`	Use a threshold for min depth , default is False
`thred`	Threshold for min depth, default is 20
`content`	Column names in VCF files
`extnum`	The column number or numbers to be extracted from vcf, default is 10; 0 for not extracting any columns
`keepall`	Keep unextracted column in output, default is TRUE, passed from read_vcf

Value

Dataframe from VCF file

Read in input vcf data in strelka2 format for Contamination detection

Description

Read in input vcf data in strelka2 format for Contamination detection

Usage

readStrelka(dr, dbOnly, depCut, thred, content, extnum, keepall)
readStrelka(dr, dbOnly, depCut, thred, content, extnum, keepall)

Arguments

`dr`	A valid input object
`dbOnly`	Use dbSNP as filter, default is FALSE, passed from read_vcf
`depCut`	Use a threshold for min depth , default is False
`thred`	Threshold for min depth, default is 20
`content`	Column names in VCF files
`extnum`	The column number or numbers to be extracted from vcf, default is 10; 0 for not extracting any columns
`keepall`	Keep unextracted column in output, default is TRUE, passed from read_vcf

Value

Dataframe from VCF file

Read in input vcf data in VarDict format for Contamination detection

Description

Read in input vcf data in VarDict format for Contamination detection

Usage

readVarDict(dr, dbOnly, depCut, thred, content, extnum, keepall)
readVarDict(dr, dbOnly, depCut, thred, content, extnum, keepall)

Arguments

`dr`	A valid input object
`dbOnly`	Use dbSNP as filter, default is FALSE, passed from read_vcf
`depCut`	Use a threshold for min depth , default is False
`thred`	Threshold for min depth, default is 20
`content`	Column names in VCF files
`extnum`	The column number to be extracted from vcf, default is 10; 0 for not extracting any column
`keepall`	Keep unextracted column in output, default is TRUE, passed from read_vcf

Value

Dataframe from VCF file

Read in input vcf data in VarPROWL format

Description

Read in input vcf data in VarPROWL format

Usage

readVarPROWL(dr, dbOnly, depCut, thred, content, extnum, keepall)
readVarPROWL(dr, dbOnly, depCut, thred, content, extnum, keepall)

Arguments

`dr`	A valid input object
`dbOnly`	Use dbSNP as filter, default is FALSE, passed from read_vcf
`depCut`	Use a threshold for min depth , default is False
`thred`	Threshold for min depth, default is 20
`content`	Column names in VCF files
`extnum`	The column number or numbers to be extracted from vcf, default is 10; 0 for not extracting any columns
`keepall`	Keep unextracted column in output, default is TRUE, passed from read_vcf

Value

vcf Dataframe from VCF file

Estimate Rho for Alternative Allele Frequency

Description

Estimates Rho parameter in beta binomial distribution for alternative allele frequency

Usage

rho_est(vl)
rho_est(vl)

Arguments

`vl`	A list of vcf objects from read_vcf function.

Value

A list containing (1) het_rho: Rho parameter of heterozygous location; (2) hom_rho: Rho parameter homozygous location;

Examples

data("vcf_example")
vcf_list <- list()
vcf_list[[1]] <- vcf_example$VCF
res <- rho_est(vl = vcf_list)
res$het_rho[[1]]$par
res$hom_rho[[1]]$par
data("vcf_example")
vcf_list <- list()
vcf_list[[1]] <- vcf_example$VCF
res <- rho_est(vl = vcf_list)
res$het_rho[[1]]$par
res$hom_rho[[1]]$par

Remove CNV regions within VCF files by change point method

Description

Remove CNV regions within VCF files by change point method

Usage

rmChangePoint(vcf, threshold, skew, lower, upper)
rmChangePoint(vcf, threshold, skew, lower, upper)

Arguments

`vcf`	Input VCF files
`threshold`	Threshold for allele frequency
`skew`	Skewness for allele frequency
`lower`	Lower bound for allele frequency region
`upper`	Upper bound for allele frequency region

Value

VCF object without change point region

Remove CNV regions within VCF files given cnv file

Description

Remove CNV regions within VCF files given cnv file

Usage

rmCNVinVCF(vcf, cnvobj)
rmCNVinVCF(vcf, cnvobj)

Arguments

`vcf`	Input VCF files
`cnvobj`	cnv object

Value

VCF object without change point region

VCF Data Summary

Description

Summarizes allele frequency information in scatter and density plots

Usage

summary_vcf(vcf, ZG = NULL, CHR = NULL)
summary_vcf(vcf, ZG = NULL, CHR = NULL)

Arguments

`vcf`	VCF object from read_vcf function
`ZG`	zygosity: (1) null, for both het and hom, default; (2) het; (3) hom
`CHR`	chromosome number: (1) null, all chromosome, default; (2) any specific number

Value

A list containing (1) scatter: allele frequency scatter plot; (2) density: allele frequency density plot

Examples

data("vcf_example")
tmp <- summary_vcf(vcf = vcf_example, ZG = 'het', CHR = c(1,2))
plot(tmp$scatter)
plot(tmp$density)
data("vcf_example")
tmp <- summary_vcf(vcf = vcf_example, ZG = 'het', CHR = c(1,2))
plot(tmp$scatter)
plot(tmp$density)

Default svm classification model.

Description

An svm object containing default svm classification model.

Usage

svm_class_model
svm_class_model

Format

An svm object:

Source

Created by Tao Jiang

Default svm regression model.

Description

An svm object containing default svm regression model.

Usage

svm_regression_model
svm_regression_model

Format

An svm object:

Source

Created by Tao Jiang

Train Contamination Detection Model

Description

Trains two SVM models (classification and regression) to detects whether a sample is contaminated another sample of its same species.

Usage

train_ct(feature)
train_ct(feature)

Arguments

feature

Feature list objects from generate_feature()

Value

A list contains two trained svm models: regression & classification

Remove CNV regions within VCF files

Description

Remove CNV regions within VCF files

Usage

update_vcf(rmCNV = FALSE, vcf, cnvobj = NULL, threshold = 0.1,
  skew = 0.5, lower = 0.45, upper = 0.55)
update_vcf(rmCNV = FALSE, vcf, cnvobj = NULL, threshold = 0.1,
  skew = 0.5, lower = 0.45, upper = 0.55)

Arguments

`rmCNV`	Remove CNV regions, default is FALSE
`vcf`	Input VCF files
`cnvobj`	cnv object, default is NULL
`threshold`	Threshold for allele frequency, default is 0.1
`skew`	Skewness for allele frequency, default is 0.5
`lower`	Lower bound for allele frequency region, default is 0.45
`upper`	Upper bound for allele frequency region, default is 0.55

Value

VCF file without CNV region

VCF example file.

Description

An example containing a list of 4 data frames.

Usage

vcf_example
vcf_example

Format

A list of 4 data frames:

Source

Created by Tao Jiang