Package 'GBScleanR'

#' Build a GbsrScheme object

Description

GBScleanR uses breeding scheme information to set the expected number of cross overs in a chromosome which is a required parameter for the genotype error correction with the Hidden Markov model implemented in the estGeno() function. This function build the object storing type crosses performed at each generation of breeding and population sizes.

Usage

addScheme(object, crosstype, mating, ...)

## S4 method for signature 'GbsrGenotypeData'
addScheme(object, crosstype, mating)

## S4 method for signature 'GbsrScheme'
addScheme(object, crosstype, mating)

Arguments

Details

A scheme object is just a data.frame indicating a population size and a type of cross applied to each generation of the breeding process to generate the population which you are going to subject to the estGeno() function. The crosstype can take either of "selfing", "sibling", "pairing", and "random". When you set crosstype = "random", you need to specify pop_size to indicate how many individuals were crossed in the random mating. You also need to specify a matrix indicating combinations of mating, in which each column shows a pair of member IDs indicating parental samples of the cross. Member IDs are serial numbers starts from 1 and automatically assigned by initScheme() and addScheme(). To check the member IDs, run showScheme(). Please see the examples section for more details of specifying a mating matrix. The created GbsrScheme object is set in the scheme slot of the GbsrGenotypeData object.

Value

A GbsrGenotypeData object storing a GbsrScheme object in the "scheme" slot.

See Also

addScheme() and showScheme()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Biparental F2 population.
gds <- setParents(gds, parents = c("Founder1", "Founder2"))

# setParents gave member ID 1 and 2 to Founder1 and Founder2, respectively.
gds <- initScheme(gds, mating = cbind(c(1:2)))

# Now the progenies of the cross above have member ID 3.
# If `crosstype = "selfing"` or `"sibling"`, you can omit a `mating` matrix.
gds <- addScheme(gds, crosstype = "self")

############################################################################
# Now you can execute `estGeno()` which requires a [GbsrScheme] object.

# Close the connection to the GDS file
closeGDS(gds)

Description

GBScleanR uses breeding scheme information to set the expected number of cross overs in a chromosome which is a required parameter for the genotype error correction with the Hidden Markov model implemented in the estGeno() function. This function assign member IDs to indicate which samples were derived from which pedigree that recorded in the GbsrScheme object.

Usage

assignScheme(object, id, ...)

## S4 method for signature 'GbsrGenotypeData'
assignScheme(object, id)

## S4 method for signature 'GbsrScheme'
assignScheme(object, id)

Arguments

Details

Member IDs can be shown by showScheme(). Only the member IDs assigned to progenies (not parents) are available to assign to samples. If the last generation recorded in the GbsrScheme object has only one member ID that should be assigned to all samples in your population, you can omit assigning IDs by assignScheme(). In that case, estGeno() automatically assign the only one member ID to all samples.

Value

A GbsrGenotypeData object storing a GbsrScheme object in the "scheme" slot.

See Also

addScheme() and showScheme()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Biparental F2 population.
gds <- setParents(gds, parents = c("Founder1", "Founder2"))

# setParents gave member ID 1 and 2 to Founder1 and Founder2, respectively.
gds <- initScheme(gds, mating = cbind(c(1:2)))

# Now the progeny of the cross above have member ID 3.
# If `crosstype = "selfing"` or `"sibling"`, you can omit a `mating` matrix.
gds <- addScheme(gds, crosstype = "self")

# The progeny of the selfing above has member ID 4.
# To execute genotype estimation for your samples, you need to assign a member
# ID to each of the samples.

# Check IDs of samples to be assigned member IDs if necessary.
getSamID(gds)

# The assignScheme() assign member IDs `id` to the samples in order.
# Please confirm the order of the member IDs in `id` and the order of the
# sample IDs shown by getSamID(gds).
gds <- assignScheme(gds, rep(4, nsam(gds)))

# If your population has samples all of which belong to only one pedigree,
# you can omit assignScheme() and let estGeno() automatically assign the
# last member ID to all samples.

############################################################################
# Now you can execute `estGeno()` which requires a [GbsrScheme] object.

# Close the connection to the GDS file
closeGDS(gds)

Description

Draw boxplots of specified statistics

Usage

boxplotGBSR(
  x,
  stats = "missing",
  target = c("marker", "sample"),
  color = c(Marker = "darkblue", Sample = "darkblue"),
  fill = c(Marker = "skyblue", Sample = "skyblue")
)

Arguments

Details

You can draw boxplots of several summary statistics of genotype counts and read counts per sample and per marker. The "stats" argument can take the following values:

missing

Proportion of missing genotype calls.

het

Proportion of heterozygote calls.

raf

Reference allele frequency.

dp

Total read counts.

ad_ref

Reference allele read counts.

ad_alt

Alternative allele read counts.

rrf

Reference allele read frequency.

mean_ref

Mean of reference allele read counts.

sd_ref

Standard deviation of reference allele read counts.

median_ref

Quantile of reference allele read counts.

mean_alt

Mean of alternative allele read counts.

sd_alt

Standard deviation of alternative allele read counts.

median_alt

Quantile of alternative allele read counts.

mq

Mapping quality.

fs

Phred-scaled p-value (strand bias)

qd

Variant Quality by Depth

sor

Symmetric Odds Ratio (strand bias)

mqranksum

Alt vs. Ref read mapping qualities

readposranksum

Alt vs. Ref read position bias

baseqranksum

Alt Vs. Ref base qualities

To draw boxplots for "missing", "het", "raf", you need to run countGenotype() first to obtain statistics. Similary, "dp", "ad_ref", "ad_alt", "rrf" requires values obtained via countRead(). "mq", "fs", "qd", "sor", "mqranksum", "readposranksum", and "baseqranksum" only work with target = "marker", if your data contains those values supplied via SNP calling tools like GATK.

Value

A ggplot object.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize genotype count information to be used in `boxplotGBSR()`
gds <- countGenotype(gds)

boxplotGBSR(gds, stats = "missing")

# Close the connection to the GDS file
closeGDS(gds)

Description

Close the connection to the GDS file linked to the given GbsrGenotypeData object.

Usage

closeGDS(object, save_filter = FALSE, verbose = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
closeGDS(object, save_filter, verbose)

Arguments

Value

NULL.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Close the connection to the GDS file
closeGDS(gds)

Description

This function calculates several summary statistics of genotype calls and alleles per marker and per sample. Those values will be stored in the SnpAnnotaionDataFrame slot and the sample slot and obtained via getter functions, e.g.s getCountGenoRef(), getCountAlleleRef(), and getMAF().

Usage

countGenotype(object, target = "both", node = "raw", ...)

## S4 method for signature 'GbsrGenotypeData'
countGenotype(object, target, node)

Arguments

Details

#' Genotype call data can be obtained from the "genotype" node, the "filt.genotype" node, or the "corrected.genotype" node of the GDS file with node = "raw", node = "filt", or node = "raw", respectively. The setCallFilter() function generate filtered genotype call data in the "filt.genotype" node which can be accessed as mentioned above. On the other hand, the "corrected.genotype" node can be generated via the estGeno() function.

Value

A GbsrGenotypeData object with genotype count information.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

# Get the proportion of missing genotype per sample.
sample_missing_rate <- getCountGenoMissing(gds,
                                           target = "sample",
                                           prop = TRUE)

# Get the minor allele frequency per marker.
marker_minor_allele_freq <- getMAF(gds, target = "marker")

# Draw histograms of the missing rate per sample and marker.
histGBSR(gds, stats = "missing")

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function calculates several summary statistics of read counts per marker and per sample. Those values will be stored in the SnpAnnotaionDataFrame slot and the sample slot and obtained via getter functions, e.g. getCountReadRef() and getCountReadAlt(). This function first calculates normalized allele read counts by dividing allele read counts at each marker in each sample by the total allele read of the sample followed by multiplication by 10^6. In other words, it calculates reads per million (rpm). Then, the function calculates mean, standard deviation, quantile values of rpm per marker and per sample. The results will be stored in the SnpAnnotaionDataFrame slot and the sample slot and obtained via getter functions, e.g. getMeanReadRef() and getMedianReadAlt().

Usage

countRead(object, target = "both", node = "raw", ...)

## S4 method for signature 'GbsrGenotypeData'
countRead(object, target, node)

Arguments

Details

Read count data can be obtained from the "annotation/format/AD/data" node or the "annotation/format/AD/filt.data" node of the GDS file with node = "raw" or node = "filt", respectively. The setCallFilter() function generate filtered read count data in the "annotation/format/AD/filt.data" node which can be accessed as mentioned above.

Value

A GbsrGenotypeData object with read count information.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the read count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countRead(gds)

# Get the total read counts per marker
read_depth_per_marker <- getCountRead(gds, target = "marker")

# Get the proportion of reference allele rads per marker.
reference_read_freq <- getCountReadRef(gds, target = "marker", prop = TRUE)

# Draw histgrams of reference allele read counts per sample and marker.
histGBSR(gds, stats = "ad_ref")

# Close the connection to the GDS file.
closeGDS(gds)

Description

Clean up genotype data by error correction based on genotype estimation using a hidden Markov model.

Usage

estGeno(
  object,
  node = "raw",
  recomb_rate = 0.04,
  error_rate = 0.0025,
  call_threshold = 0.9,
  het_parent = FALSE,
  optim = TRUE,
  iter = 2,
  n_threads = 1,
  dummy_reads = 5,
  ...
)

## S4 method for signature 'GbsrGenotypeData'
estGeno(
  object,
  node,
  recomb_rate,
  error_rate,
  call_threshold,
  het_parent,
  optim,
  iter,
  n_threads,
  dummy_reads
)

Arguments

Details

If you have not set parental samples by setParents() and initialized the scheme object using initScheme(), you have the scheme object without explicit parental information that is assumed to be a bi-parental population. In this case, estGeno() will run in the parentless mode. In the parentless mode, the algorithm assumes that the given population is a bi-parental population. The number of reference allele reads and the number of alternative allele reads of the dummy parents are set based on dummy_reads, respectively. Dummy parent 1 has dummy_reads of the reference allele reads and 0 alternative allele reads at all markers, while dummy parent 2 has 0 and dummy_reads of reference and alternative allele reads at all markers. If the parents of your population were outbred lines or you cannot assume one of the parents has completely reference homozygotes and another has laternative homozygotes at all markers, Set dummy_reads = 0 to leave uncertainty to estimate parental genotypes based on the offspring genotypes. Nevertheless, the parentless mode is less accurate and has more chance to get a genotype estimate randomly selected from the equally likely genotype estimates.

Value

A GbsrGenotypeData object in which the "estimated.haplotype", "corrected.genotype" and "parents.genotype" nodes were added.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Find the IDs of parental samples.
parents <- grep("Founder", getSamID(gds), value = TRUE)

# Set the parents and flip allele information
# if the reference sample (Founder1 in our case) has homozygous
# alternative genotype at some markers of which alleles will
# be swapped to make the reference sample have homozygous
# reference genotype.
gds <- setParents(gds, parents = parents)

# Initialize a scheme object stored in the slot of the GbsrGenotypeData.
# We chose `crosstype = "pair"` because two inbred founders were mated
# in our breeding scheme.
# We also need to specify the mating matrix which has two rows and
# one column with integers 1 and 2 indicating a sample (founder)
# with the memberID 1 and a sample (founder) with the memberID 2
# were mated.
gds <- initScheme(gds, mating = cbind(c(1:2)))

# Add information of the next cross conducted in our scheme.
# We chose 'crosstype = "selfing"', which do not require a
# mating matrix.
gds <- addScheme(gds, crosstype = "selfing")

# Execute error correction by estimating genotype and haplotype of
# founders and offspring.
gds <- estGeno(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Write out a CSV file with raw, filtered, corrected genotype data or estimated haplotype data stored in a GDS file.

Usage

gbsrGDS2CSV(
  object,
  out_fn,
  node = "raw",
  incl_parents = TRUE,
  bp2cm = NULL,
  format = "",
  read = FALSE,
  ...
)

## S4 method for signature 'GbsrGenotypeData'
gbsrGDS2CSV(object, out_fn, node, incl_parents, bp2cm, format, read)

Arguments

Details

Create a CSV file at location specified by out_fn. The setting format = "qtl" makes the function export the data in the r/qtl format that can be loaded using read.cross as format = "csvs" with a phenotype data. If you have executed estGeno() and your population is a biparental population, set 'node = "dosage"' to export a r/qtl format CSV in which homozygoutes of the alleles of Parent 1 and 2, which have been specified by setParents(), are represented by A and B, respectively. If 'node = "raw"', 'node = "fill"', and 'node = "cor"', A and B in the r/qtl format CSV indicate homozygoutes of reference and alternative alleles shown in a given VCF file. This means that if Parent 1 has the alternative allele homozygoute at Marker 1 and Offspring 1 has the same genotype with Parent 1, the genotype of Offspring 1 at Marker 1 will be B in the r/qtl format CSV. On the other hand, if you set 'node = "dosage"', the genotype of Offspring 1 at Marker 1 will be A in the r/qtl format CSV. The output CSV file has the rows indicating chromosome ID and positions of markers followed by the rows indicating genotype or haplotype data of samples. If read = TRUE, the output of each genotype call would be in the form of ⁠GT:ADR,ADA⁠ where GT, ADR, and ADA represent genotype, referenece read count, and alternative read count, respectively. If format = "qtl", read = TRUE will be ignored.

Value

The path to the CSV file.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Create a CSV file with data from the GDS file
#  connected to the [GbsrGenotypeData] oobject.
out_fn <- tempfile("sample_out", fileext = ".csv")
gbsrGDS2CSV(gds, out_fn)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Write out a VCF file with raw, filtered, or corrected genotype data stored in a GDS file. The output VCF file contains the GT, AD, and DP fields.

Usage

gbsrGDS2VCF(
  object,
  out_fn,
  node = "raw",
  info.export = NULL,
  fmt.export = NULL,
  parents = TRUE,
  ...
)

## S4 method for signature 'GbsrGenotypeData'
gbsrGDS2VCF(object, out_fn, node, info.export, fmt.export, parents)

Arguments

Details

Create a VCF file at location specified by out_fn. The connection to the GDS file of the input GbsrGenotypeData object will be automatically closed for internal file handling in this function. Please use reopenGDS() to open the connection again. If you use loadGDS(), summary statistics and filtering information will be discarded.

Value

The path to the VCF file.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Create a VCF file with data from the GDS file
#  connected to the [GbsrGenotypeData] oobject.
out_fn <- tempfile("sample_out", fileext = ".vcf.gz")
# gbsrGDS2VCF(gds, out_fn)

# Close the connection to the GDS file.
closeGDS(gds)

Description

The GbsrGenotypeData class is the main class of GBScleanR and user work with this class object.

Details

The GbsrGenotypeData class is an extention of SeqVarGDSClass in the SeqArray package to store summary data of genotypes and reads and a GbsrScheme object that contains mating scheme information of the given population.. The slots marker and sample store a data.frame object for variant-wise and sample-wise summary information, respectively. The scheme slot holds a GbsrScheme object. The function loadGDS() initialize the GbsrGenotypeData class.

Slots

marker

A data.frame object.

sample

A data.frame object.

scheme

A GbsrScheme object.

Examples

# `loadGDS()` initialize the `GbsrGenotypeData` object.

# Load a GDS file and instantiate a `GbsrGenotypeData` object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Close connection to the GDS file.
closeGDS(gds)

Description

GBScleanR uses breeding scheme information to set the expected number of cross overs in a chromosome which is a required parameter for the genotype error correction with the hidden Markov model implemented in the estGeno() function. This class stores those information including ID of parental samples, type crosses performed at each generation of breeding and population sizes of each generation. This class is not exported.

Slots

crosstype

A vector of strings indicating the type of crossing done at each generation.

mating

A list of matrices showing combinations member IDs of samples mated.

parents

A vector of member IDs of parents.

progenies

A vector of memeber IDs of progenies produced at each generation.

samples

A vector of member IDs of samples indicating which samples are derived from which pedigrees.

See Also

GbsrGenotypeData dnd loadGDS().

Examples

# `loadGDS()` initialize a `GbsrScheme` object internally and
# attache it to the shceme slot of a [GbsrGenotypeData] object.

# Load data in the GDS file and instantiate
# a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Print the information stored in the `GbsrScheme` object.
showScheme(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function converts a variant call data in the VCF format. The current implementation only accepts biallelic single nucleotide polymorphisms. Please filter out variants which are insertions and deletions or multiallelic. You may use "bcftools" or "vcftools" for filtering.

Usage

gbsrVCF2GDS(
  vcf_fn,
  out_fn,
  gt = "GT",
  info.import = NULL,
  fmt.import = NULL,
  force = FALSE,
  verbose = TRUE
)

Arguments

Details

gbsrVCF2GDS converts a VCF file to a GDS file. The data structure of the GDS file created via this functions is same with those created by seqVCF2GDS() of SeqArray.

Value

The output GDS file path.

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a `GbsrGenotypeData` object.
gds <- loadGDS(gds_fn)

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function returns the reference allele and alternative allele(s).

Usage

getAllele(object, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
getAllele(object, valid, chr)

Arguments

Details

If valid = TRUE, the alleles of markers which are labeled TRUE in the "valid" column of the "marker" slot will be returned. If you need the number of over all markers, set valid = FALSE. validMar() tells you which markers are valid.

Value

A vector of strings each of which is a "/" separated string and indicates the reference allele and the alternative allele(s) at a marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

getAllele(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function returns chromosome IDs of markers.

Usage

getChromosome(object, valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getChromosome(object, valid)

Arguments

Details

If valid = TRUE, the chromosome IDs of the markers which are labeled TRUE in the "valid" column of the "marker" slot will be returned. If you need the number of over all markers, set valid = FALSE. validMar() tells you which markers are valid.

Value

A vector of factors indicating chromosome IDs.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

getChromosome(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain total alternative allele counts per SNP or per sample

Usage

getCountAlleleAlt(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountAlleleAlt(object, target, valid, prop)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) alternative alleles per marker.

Examples

gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)
gds <- countGenotype(gds)
getCountAlleleAlt(gds)
closeGDS(gds) # Close the connection to the GDS file

Description

Obtain total missing allele counts per SNP or per sample

Usage

getCountAlleleMissing(
  object,
  target = "marker",
  valid = TRUE,
  prop = FALSE,
  ...
)

## S4 method for signature 'GbsrGenotypeData'
getCountAlleleMissing(object, target, valid, prop)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) missing alleles per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

getCountAlleleMissing(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain total reference allele counts per SNP or per sample

Usage

getCountAlleleRef(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountAlleleRef(object, target, valid, prop)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) reference alleles per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

getCountAlleleRef(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain total alternative genotype counts per SNP or per sample

Usage

getCountGenoAlt(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountGenoAlt(object, target, valid, prop)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) homozygous alternative genotype calls per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

getCountGenoAlt(gds)

# Close the connection to the GDS file
closeGDS(gds)

Description

Obtain total heterozygote counts per SNP or per sample

Usage

getCountGenoHet(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountGenoHet(object, target, valid, prop)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) heterozugous genotype calls per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

getCountGenoHet(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain total missing genotype counts per SNP or per sample

Usage

getCountGenoMissing(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountGenoMissing(object, target, valid, prop)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) missing genotype calls per marker.

Examples

gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)
gds <- countGenotype(gds)
getCountGenoMissing(gds)
closeGDS(gds) # Close the connection to the GDS file

Description

Obtain total reference genotype counts per SNP or per sample

Usage

getCountGenoRef(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountGenoRef(object, target, valid, prop)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) homozygous reference genotype calls per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

getCountGenoRef(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain total read counts per SNP or per sample

Usage

getCountRead(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountRead(object, target, valid)

Arguments

Details

You need to execute countRead() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A integer vector of total read counts (reference allele reads + alternative allele reads) per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)
gds <- countRead(gds)

getCountRead(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain total alternative read counts per SNP or per sample

Usage

getCountReadAlt(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountReadAlt(object, target, valid, prop)

Arguments

Details

You need to execute countRead() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) alternative allele read counts per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)
gds <- countRead(gds)

getCountReadAlt(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain total reference read counts per SNP or per sample

Usage

getCountReadRef(object, target = "marker", valid = TRUE, prop = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getCountReadRef(object, target, valid, prop)

Arguments

Details

You need to execute countRead() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of (proportion of) reference read counts per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)
gds <- countRead(gds)

getCountReadRef(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Get fixed allele read biases of markers

Usage

getFixedBias(object, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
getFixedBias(object, valid, chr)

Arguments

Details

If valid = TRUE, A logical vector for the markers which are labeled TRUE in the "valid" column of the "marker" slot will be returned. If you need check the dominant markers in all markers, set valid = FALSE. validMar() tells you which markers are valid.

Value

A numeric vector of fixed allele read biases.

A GbsrGenotypeData object after adding dominant marker information

See Also

setFixedBias()

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds <- loadGDS(gds_fn)

# Set fixed allele read biases.
# Initialize the bias vector to be assinged.
bias <- rep(NA, nmar(gds))

# As an example, select 20 markers randomly and assign 0 or 1 to them.
# Since the bias set by setFixedBias() function is the reference allele read
# bias. Thus, the values 0 and 1 means that the marker only gives alternative
# and reference allele reads, respectively.
# Set these fixed biases if some of your markers are dominant markers.
bias[sample(seq_along(bias), 20)] <- sample(c(0, 1), 20, replace = TRUE)

gds <- setFixedBias(gds, bias = bias)

fixed_bias <- getFixedBias(gds)

# Close the connection to the GDS file
closeGDS(gds)

Description

Genotype calls are retrieved from the GDS file linked to the given GbsrGenotypeData object.

Usage

getGenotype(
  object,
  node = "raw",
  parents = FALSE,
  valid = TRUE,
  chr = NULL,
  phased = FALSE,
  ...
)

## S4 method for signature 'GbsrGenotypeData'
getGenotype(object, node, parents, valid, chr, phased)

Arguments

Details

When ⁠node = "raw⁠, the raw genotype data stored in the "genotype/data" node will be returned, while ⁠node = "filt⁠ make the function to return the filtered genotype data stored in the "annotation/format/FGT/data" that can be generated via the setCallFilter() function. ⁠node = "cor⁠ indicates to get the corrected genotype data stored in the "annotation/format/CGT/data" that can be generated via the estGeno() function. The estimated parental genotypes, which also can be generated via the estGeno() function and stored in the "annotation/info/PGT" node, can be obtained with node = "parents". If valid = TRUE, genotype calls for only valid marker and valid samples will be obtained.

Value

An integer matirix of genotype data which is represented by the number of reference alleles at each marker of each sample.

See Also

setCallFilter() and estGeno()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

geno <- getGenotype(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Haplotype calls are retrieved from the GDS file linked to the given GbsrGenotypeData object.

Usage

getHaplotype(object, parents = FALSE, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
getHaplotype(object, parents, valid, chr)

Arguments

Details

Haplotype call data can be obtained from the "estimated.haplotype" node of the GDS file which can be generated via the estGeno() function. Thus, this function is valid only after having executed estGeno(). If valid = TRUE, read counts for only valid marker and valid samples will be obtained.

Value

An integer array of haplotype data. The array have 2 x M x N dimensions, where M is the number of markers and N is the number of samples. Each integer values represent the origin of the haplotype. For example, in the population with two inbred founders, values take either 1 or 2 indicating the hapotype descent from founder 1 and 2. If two outbred founders, values take 1, 2, 3, or 4 indicating the first and second haplotype in founder 1 and the first and second haplotype in founder 2.

See Also

estGeno()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Find the IDs of parental samples.
parents <- grep("Founder", getSamID(gds), value = TRUE)

# Set the parents and flip allele information
# if the reference sample (Founder1 in our case) has homozygous
# alternative genotype at some markers of which alleles will
# be swapped to make the reference sample have homozygous
# reference genotype.
gds <- setParents(gds, parents = parents)

# Initialize a scheme object stored in the slot of the GbsrGenotypeData.
# We chose `crosstype = "pair"` because two inbred founders were mated
# in our breeding scheme.
# We also need to specify the mating matrix which has two rows and
# one column with integers 1 and 2 indicating a sample (founder)
# with the memberID 1 and a sample (founder) with the memberID 2
# were mated.
gds <- initScheme(gds, mating = cbind(c(1:2)))

# Add information of the next cross conducted in our scheme.
# We chose 'crosstype = "selfing"', which do not require a
# mating matrix.
gds <- addScheme(gds, crosstype = "selfing")

# Execute error correction by estimating genotype and haplotype of
# founders and offspring.
gds <- estGeno(gds)

hap <- getHaplotype(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

The "annotation/info" node stores annotation infromation of markers obtained via SNP calling tools like bcftools and GATK.

Usage

getInfo(object, var, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
getInfo(object, var, valid, chr)

Arguments

Details

If valid = TRUE, the information of the markers which are labeled TRUE in the "valid" column of the "marker" slot will be returned. If you need the number of over all markers, set valid = FALSE. validMar() tells you which markers are valid.

Value

A numeric vector of data stored in INFO node of the GDS file.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Get mapping qualities (MQ) of markers.
mq <- getInfo(gds, "MQ")

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain minor allele counts per SNP or per sample

Usage

getMAC(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getMAC(object, target, valid)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the minor allele counts per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

getMAC(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain minor allele frequencies per SNP or per sample

Usage

getMAF(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getMAF(object, target, valid)

Arguments

Details

You need to execute countGenotype() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the minor allele frequencies per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the genotype count information and store them in the
# [marker] and [sample] slots of the [GbsrGenotypeData] object.
gds <- countGenotype(gds)

getMAF(gds)

# Close the connection to the GDS file
closeGDS(gds)

Description

Obtain the marker IDs

Usage

getMarID(object, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
getMarID(object, valid, chr)

Arguments

Details

If valid = TRUE, the IDs of markers which are labeled TRUE in the "valid" column of the "marker" slot will be returned. If you need the number of over all markers, set valid = FALSE. validMar() tells you which markers are valid.

Value

A integer vector of marker IDs.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

getMarID(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain mean values of total alternative read counts per SNP or per sample

Usage

getMeanReadAlt(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getMeanReadAlt(object, target, valid)

Arguments

Details

You need to execute countRead() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the mean values of alternative allele reads per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Calculate means, standard deviations, quantiles of read counts
# per marker and per sample with or without standardization of
# the counts and store them in the [marker] and [sample] slots
# of the [GbsrGenotypeData] object.
gds <- countRead(gds)

getMeanReadAlt(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain mean values of total reference read counts per SNP or per sample

Usage

getMeanReadRef(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getMeanReadRef(object, target, valid)

Arguments

Details

You need to execute countRead() to calculate summary statistics to be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the mean values of reference allele reads per marker.

Examples

gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)
gds <- countRead(gds)
getMeanReadRef(gds)
closeGDS(gds) # Close the connection to the GDS file

Description

Obtain quantile values of total alternative read counts per SNP or per sample

Usage

getMedianReadAlt(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getMedianReadAlt(object, target, valid)

Arguments

Details

You need to execute countRead() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the quantile values of alternative allele reads per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Calculate means, standard deviations, quantiles of read counts
# per marker and per sample with or without standardization of
# the counts and store them in the [marker] and [sample] slots
# of the [GbsrGenotypeData] object.
gds <- countRead(gds)

getMedianReadAlt(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain quantile values of total reference read counts per SNP or per sample

Usage

getMedianReadRef(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getMedianReadRef(object, target, valid)

Arguments

Details

You need to execute countRead() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the quantile values of alternative allele reads per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Calculate means, standard deviations, quantiles of read counts
# per marker and per sample with or without standardization of
# the counts and store them in the [marker] and [sample] slots
# of the [GbsrGenotypeData] object.
gds <- countRead(gds)

getMedianReadRef(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function returns sample IDs, member IDs and indexes of parental samples set via setParents(). Sample IDs are IDs given by user or obtained from the original VCF file. Member IDs are serial numbers assigned by setParents().

Usage

getParents(object, bool = FALSE, verbose = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getParents(object, bool, verbose = TRUE)

Arguments

Value

A data frame of parents information indicating sampleIDs, memberIDs and indexes of parental lines assigned via setParents().

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Find the IDs of parental samples.
parents <- grep("Founder", getSamID(gds), value = TRUE)

# Set the parents.
gds <- setParents(gds, parents = parents)

# Get the information of parents.
getParents(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function returns physical positions of markers.

Usage

getPosition(object, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
getPosition(object, valid, chr)

Arguments

Details

If valid = TRUE, the positions of the markers which are labeled TRUE in the "valid" column of the "marker" slot will be returned. If you need the positions of over all markers, set valid = FALSE. validMar() tells you which markers are valid.

Value

An integer vector indicating the physical positions of markers.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

getPosition(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Read counts for reference allele and alternative allele are retrieved from the GDS file linked to the given GbsrGenotypeData object.

Usage

getRead(object, node = "raw", parents = FALSE, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
getRead(object, node, parents, valid, chr)

Arguments

Details

When ⁠node = "raw⁠, the raw read counts stored in the "annotation/format/AD/data" node will be returned, while ⁠node = "filt⁠ make the function to return the filtered read counts stored in the "annotation/format/FAD/data" that can be generated via the setCallFilter() function. If valid = TRUE, read counts for only valid marker and valid samples will be obtained.

Value

A named list with two elements "ref" and "alt" storing a matrix of reference allele read counts and a matrix of alternative read counts for all markers in all samples.

See Also

setCallFilter()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

read <- getRead(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Not implemented yet. This function assign identifiers that indicates which samples are replicates those which should have the same genotypes at all markers.

Usage

getReplicates(object, parents = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getReplicates(object, parents)

Arguments

Details

The replicates of samples specified in setReplicates() will have the same genotypes at all markers in the estimated genotypes obtained via estGeno(). In the genotype estimation by estGeno(), the Viterbi scores for each possible genotype (haplotype) at each marker for the replicates will be replaced with the average score for the replicates.

Value

A GbsrGenotypeData object with genotype count information.

See Also

setReplicates()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

gds <- setParents(gds, parents = c("Founder1", "Founder2"))

# When your data has 100 samples, two replicates for each offspring,
# and the samples are ordered as the 1st replicate followed by the 2nd
# replicate, you can specify replicates as below.
# gds <- setReplicates(gds, replicates = rep(1:50, each = 2))

# If you need to confirm the order of samples, run the following code.
# id <- getSamID(gds)

# Replicate IDs should be set also to parents. Therefore, please include


# getReplicates(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function returns sample IDs.

Usage

getSamID(object, valid = TRUE, parents = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
getSamID(object, valid, parents)

Arguments

Details

If valid = TRUE, the IDs of samples which are labeled TRUE in the "valid" column of the "sample" slot will be returned. If you need the number of over all samples, set valid = FALSE. validSam() tells you which samples are valid.

Value

A character vector of sample IDs.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

getSamID(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain standard deviations of total alternative read counts per SNP or per sample

Usage

getSDReadAlt(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getSDReadAlt(object, target, valid)

Arguments

Details

You need to execute countRead() to calculate sumaary statisticsto be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the standard deviations of alternative allele reads per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Calculate means, standard deviations, quantiles of read counts
# per marker and per sample with or without standardization of
# the counts and store them in the [marker] and [sample] slots
# of the [GbsrGenotypeData] object.
gds <- countRead(gds)

getSDReadAlt(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Obtain standard deviations of total reference read counts per SNP or per sample

Usage

getSDReadRef(object, target = "marker", valid = TRUE, ...)

## S4 method for signature 'GbsrGenotypeData'
getSDReadRef(object, target, valid)

Arguments

Details

You need to execute countRead() to calculate summary statistics to be obtained via this function. If valid = TRUE, the chromosome information of markers which are labeled TRUE in the sample slot will be returned. validMar() tells you which samples are valid.

Value

A numeric vector of the standard deviations of reference allele reads per marker.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Calculate means, standard deviations, quantiles of read counts
# per marker and per sample with or without standardization of
# the counts and store them in the [marker] and [sample] slots
# of the [GbsrGenotypeData] object.
gds <- countRead(gds)

getSDReadRef(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Draw histograms of specified statistics

Usage

histGBSR(
  x,
  stats = c("dp", "missing", "het"),
  target = c("marker", "sample"),
  binwidth = NULL,
  color = c(Marker = "darkblue", Sample = "darkblue"),
  fill = c(Marker = "skyblue", Sample = "skyblue")
)

Arguments

Details

You can draw histograms of several summary statistics of genotype counts and read counts per sample and per marker. The "stats" argument can take the following values:

missing

Proportion of missing genotype calls.

het

Proportion of heterozygote calls.

raf

Reference allele frequency.

dp

Total read counts.

ad_ref

Reference allele read counts.

ad_alt

Alternative allele read counts.

rrf

Reference allele read frequency.

mean_ref

Mean of reference allele read counts.

sd_ref

Standard deviation of reference allele read counts.

median_ref

Quantile of reference allele read counts.

mean_alt

Mean of alternative allele read counts.

sd_alt

Standard deviation of alternative allele read counts.

median_alt

Quantile of alternative allele read counts.

mq

Mapping quality.

fs

Phred-scaled p-value (strand bias)

qd

Variant Quality by Depth

sor

Symmetric Odds Ratio (strand bias)

mqranksum

Alt vs. Ref read mapping qualities

readposranksum

Alt vs. Ref read position bias

baseqranksum

Alt Vs. Ref base qualities

To draw histograms for "missing", "het", "raf", you need to run countGenotype() first to obtain statistics. Similary, "dp", "ad_ref", "ad_alt", "rrf" requires values obtained via countRead(). "mq", "fs", "qd", "sor", "mqranksum", "readposranksum", and "baseqranksum" only work with target = "marker", if your data contains those values supplied via SNP calling tools like GATK.

Value

A ggplot object.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize genotype count information to be used in `histGBSR()`
gds <- countGenotype(gds)

# Draw histograms of missing rate, heterozygosity, and reference
# allele frequency per SNP and per sample.
histGBSR(gds, stats = "missing")

# Close the connection to the GDS file
closeGDS(gds)

Build a GbsrScheme object

Description

GBScleanR uses breeding scheme information to set the expected number of cross overs in a chromosome which is a required parameter for the genotype error correction with the hidden Markov model implemented in the estGeno() function. This function build the object storing type crosses performed at each generation of breeding and population sizes.

Usage

initScheme(object, mating, ...)

## S4 method for signature 'GbsrGenotypeData'
initScheme(object, mating)

## S4 method for signature 'GbsrScheme'
initScheme(object, mating, parents)

Arguments

Details

A GbsrScheme object stores information of a population size, mating combinations and a type of cross applied to each generation of the breeding process to generate the population which you are going to subject to the estGeno() function. The first generation should be parents of the population. It is supposed that setParents() has been already executed and parents are labeled in the GbsrGenotypeData object. The number of parents are automatically recognized. The "crosstype" of the first generation can be "pairing" or "random" with pop_size = N, where N is the number of parents. You need to specify a matrix indicating combinations of mating, in which each column shows a pair of parental samples. For example, if you have only two parents, the mating matrix is mating = cbind(c(1:2)). The indices used in the matrix should match with the IDs labeled to parental samples by setParents(). The created GbsrScheme object is set in the scheme slot of the GbsrGenotypeData object.

Value

A GbsrGenotypeData object storing a GbsrScheme object in the "scheme" slot.

See Also

addScheme() and showScheme()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Biparental F2 population.
gds <- setParents(gds, parents = c("Founder1", "Founder2"))

# setParents gave member ID 1 and 2 to Founder1 and Founder2, respectively.
gds <- initScheme(gds, mating = cbind(c(1:2)))

# Now the progenies of the cross above have member ID 3.
# If `crosstype = "selfing"` or `"sibling"`, you can omit a `mating` matrix.
gds <- addScheme(gds, crosstype = "self")

# Now you can execute `estGeno()` which requires a [GbsrScheme] object.

# Close the connection to the GDS file
closeGDS(gds)

Description

Check if a GDS file has been opened or not.

Usage

isOpenGDS(object, ...)

## S4 method for signature 'GbsrGenotypeData'
isOpenGDS(object)

Arguments

Value

TRUE if the GDS file linked to the input GbsrGenotypeData object has been opened, while FALSE if closed.

Examples

# Use a GDS file of example data.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")

# Instantiation of [GbsrGenotypeData]
gds <- loadGDS(gds_fn)

# Check connection to the GDS file
isOpenGDS(gds)

# Close the connection to the GDS file
closeGDS(gds)

Description

Load data stored in an input GDS file to R environment and create a GbsrGenotypeData instance. GBScleanR handles only one class GbsrGenotypeData and conducts all data manipulation via class methods for it.

Usage

loadGDS(x, load_filter = FALSE, ploidy = 2, verbose = TRUE)

Arguments

Details

The first time to load a newly produced GDS file will take long time due to data reformatting for quick access. The GbsrGenotypeData object returned from loadGDS() can be also handled as SeqVarGDSClass of the SeqArray package.

Value

A GbsrGenotypeData object.

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a `GbsrGenotypeData` object.
gds <- loadGDS(gds_fn)

# Reload data from the GDS file.
gds <- loadGDS(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Automate a GbsrScheme object building.

Description

GBScleanR uses breeding scheme information to set the expected number of cross overs in a chromosome which is a required parameter for the genotype error correction with the Hidden Markov model implemented in the estGeno() function. This function automates the building of a GbsrScheme object.

Usage

makeScheme(object, generation, crosstype, ...)

## S4 method for signature 'GbsrGenotypeData'
makeScheme(object, generation, crosstype)

Arguments

Details

A scheme object is just a data.frame indicating a population size and a type of cross applied to each generation of the breeding process to generate the population which you are going to subject to the estGeno() function. The crosstype specified to makeScheme() can take "selfing" and "sibling". When your population has $2^n$ parents specified by setParents(), makeScheme() assumes those parents were crossed in the "funnel" design in which $2^n$ parents are crossed to obtain $2^n/2$ F1 hybrids followed by successive intercrossings (pairings) of the hybrids to combine the genomes of all parents in one family of siblings. The makeScheme() function assumes that the parents that were assigned an odd number member ID (N) in setParents() had been crossed with the parent that were assigned an even number (N+1). For example, if you set parents as shown below. The makeScheme() function prepare a scheme information that indicates the intercrossings of "p1 x p2", "p3 x p4", "p5 x p6", and "p7 x p8" followed by crossing of "p1xp2_F1 x p3xp4_F1" and "p5xp6_F1 x p7xp8_F1" and then crossing of the two 4-way crossed liens to produce 8-way crossed hybrid lines. If, for example, generation = 5 indicating an F5 generation was specified to makeScheme(), the function adds 4 successive selfing or sibling crossings in the scheme. The created GbsrScheme object will be set in the scheme slot of the GbsrGenotypeData object.

Value

A GbsrGenotypeData object storing a GbsrScheme object in the "scheme" slot.

See Also

initScheme(), addScheme(), and showScheme()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Biparental F2 population.
gds <- setParents(gds, parents = c("Founder1", "Founder2"))

gds <- makeScheme(gds, generation = 2, crosstype = "self")

############################################################################
# Now you can execute `estGeno()` which requires a [GbsrScheme] object.

# Close the connection to the GDS file
closeGDS(gds)

Description

This function returns the number of SNPs recorded in the GDS file connected to the given GbsrGenotypeData object.

Usage

nmar(object, valid = TRUE, chr = NULL, ...)

## S4 method for signature 'GbsrGenotypeData'
nmar(object, valid, chr)

Arguments

Details

If valid = TRUE, the number of markers which are labeled TRUE in the "valid" column of the "marker" slot will be returned. If you need the number of over all markers, set valid = FALSE. validMar() tells you which markers are valid.

Value

An integer value to indicate the number of SNP markers.

See Also

validMar()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

nmar(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

This function returns the number of samples recorded in the GDS file connected to the given GbsrGenotypeData object.

Usage

nsam(object, valid = TRUE, parents = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
nsam(object, valid, parents)

Arguments

Details

If valid = TRUE, the number of the samples which are labeled TRUE in the "valid" column of the "sample" slot will be returned. If you need the number of over all samples, set valid = FALSE. validSam() tells you which samples are valid.

Value

An integer value to indicate the number of samples.

See Also

validSam()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

nsam(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Draw a scatter plot of a pair of specified statistics

Usage

pairsGBSR(
  x,
  stats1 = "dp",
  stats2 = "missing",
  target = "marker",
  size = 0.5,
  alpha = 0.8,
  color = c(Marker = "darkblue", Sample = "darkblue"),
  fill = c(Marker = "skyblue", Sample = "skyblue"),
  smooth = FALSE
)

Arguments

Details

You can draw a scatter plot of per-marker and/or per-sample summary statistics specified at stats1 and stats2. The "stats1" and "stats2" arguments can take the following values:

missing

Proportion of missing genotype calls.

het

Proportion of heterozygote calls.

raf

Reference allele frequency.

dp

Total read counts.

ad_ref

Reference allele read counts.

ad_alt

Alternative allele read counts.

rrf

Reference allele read frequency.

mean_ref

Mean of reference allele read counts.

sd_ref

Standard deviation of reference allele read counts.

median_ref

Quantile of reference allele read counts.

mean_alt

Mean of alternative allele read counts.

sd_alt

Standard deviation of alternative allele read counts.

median_alt

Quantile of alternative allele read counts.

mq

Mapping quality.

fs

Phred-scaled p-value (strand bias)

qd

Variant Quality by Depth

sor

Symmetric Odds Ratio (strand bias)

mqranksum

Alt vs. Ref read mapping qualities

readposranksum

Alt vs. Ref read position bias

baseqranksum

Alt Vs. Ref base qualities

To draw scatter plots for "missing", "het", "raf", you need to run countGenotype() first to obtain statistics. Similary, "dp", "ad_ref", "ad_alt", "rrf" requires values obtained via countRead(). "mq", "fs", "qd", "sor", "mqranksum", "readposranksum", and "baseqranksum" only work with target = "marker", if your data contains those values supplied via SNP calling tools like GATK.

Value

A ggplot object.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize genotype count information to be used in `pairsGBSR()`
gds <- countGenotype(gds)

# Draw scatter plots of missing rate vs heterozygosity.
pairsGBSR(gds, stats1 = "missing", stats2 = "het")

# Close the connection to the GDS file
closeGDS(gds)

Description

This function counts a reference allele dosage per marker per sample and draw line plots of them in facets for each chromosome for each sample.

Usage

plotDosage(
  x,
  coord = NULL,
  chr = NULL,
  ind = 1,
  node = "raw",
  showratio = TRUE,
  dot_fill = c("green", "darkblue"),
  size = 0.8,
  alpha = 0.8,
  line_color = "magenta"
)

Arguments

Value

A ggplot object.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

plotDosage(gds, ind = 1)

# Close the connection to the GDS file
closeGDS(gds)

Description

Draw line plots of specified statistics

Usage

plotGBSR(
  x,
  stats = c("dp", "missing", "het"),
  coord = NULL,
  lwd = 0.5,
  binwidth = NULL,
  color = c(Marker = "darkblue", Ref = "darkgreen", Het = "magenta", Alt = "blue")
)

Arguments

Details

You can draw line plots of several summary statistics of genotype counts and read counts per sample and per marker. The "stats" argument can take the following values:

marker

Marker density.

geno

Proportion of missing genotype calls.

missing

Proportion of missing genotype calls.

het

Proportion of heterozygote calls.

raf

Reference allele frequency.

dp

Total read counts.

ad_ref

Reference allele read counts.

ad_alt

Alternative allele read counts.

rrf

Reference allele read frequency.

mean_ref

Mean of reference allele read counts.

sd_ref

Standard deviation of reference allele read counts.

median_ref

Quantile of reference allele read counts.

mean_alt

Mean of alternative allele read counts.

sd_alt

Standard deviation of alternative allele read counts.

median_alt

Quantile of alternative allele read counts.

mq

Mapping quality.

fs

Phred-scaled p-value (strand bias)

qd

Variant Quality by Depth

sor

Symmetric Odds Ratio (strand bias)

mqranksum

Alt vs. Ref read mapping qualities

readposranksum

Alt vs. Ref read position bias

baseqranksum

Alt Vs. Ref base qualities

To draw line plots for "missing", "het", "raf", you need to run countGenotype() first to obtain statistics. Similary, "dp", "ad_ref", "ad_alt", "rrf" requires values obtained via countRead(). "mq", "fs", "qd", "sor", "mqranksum", "readposranksum", #' and "baseqranksum" only work with target = "marker", if your data contains those values supplied via SNP calling tools like GATK.

Value

A ggplot object.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize genotype count information to be used in `plotGBSR()`
gds <- countGenotype(gds)

# Draw line plots of missing rate, heterozygosity, proportion of genotype
# calls per SNP.
plotGBSR(gds, stats = "missing")

# Close the connection to the GDS file
closeGDS(gds)

Description

This function calculate a proportion of reference allele read counts per marker per sample and draw line plots of them in facets for each chromosome for each sample.

Usage

plotReadRatio(
  x,
  coord = NULL,
  chr = NULL,
  ind = 1,
  node = "raw",
  dot_fill = c("green", "darkblue"),
  size = 0.8,
  alpha = 0.8
)

Arguments

Value

A ggplot object.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

plotReadRatio(gds, ind = 1)

# Close the connection to the GDS file
closeGDS(gds)

Description

Reopen the connection to the GDS file.

Usage

reopenGDS(object, ...)

## S4 method for signature 'GbsrGenotypeData'
reopenGDS(object)

Arguments

Details

The GbsrGenotypeData object stores the file path of the GDS file even after closing the connection the file. This function open again the connection to the GDS file at the file path stored in the GbsrGenotypeData object. If the GbsrGenotypeData object witch has an open connection to the GDS file, this function will reopen the connection. The data stored in the marker and sample slots will not be changed. Thus, you can open a connection with the GDS file with keeping information of filtering and summary statistics.

Value

A GbsrGenotypeData object.

Examples

# Use a GDS file of example data.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")

# Instantiation of [GbsrGenotypeData]
gds <- loadGDS(gds_fn)

# Close the connection to the GDS file
closeGDS(gds)

gds <- reopenGDS(gds)

# Close the connection to the GDS file
closeGDS(gds)

Description

Set the "genotype" node and the "data" node as primary nodes for genotype data and read count data. The data stored in the primary nodes are used in the functions of GBScleanR.

Usage

resetCallFilter(object, ...)

## S4 method for signature 'GbsrGenotypeData'
resetCallFilter(object)

Arguments

Details

A GbsrGenotypeData object storing information of the primary node of genotype data and read count data. All of the functions implemented in GBScleanR check the primary nodes and use data stored in those nodes. setCallFilter() create new nodes storing filtered genotype calls and read counts in a GDS file and change the primary nodes to "filt.genotype" and "filt.data" for genotype and read count data, respectively. resetCallFilter() set back the nodes to the original, those are "genotype" and "data" for genotype and read count data, respectively.

Value

A GbsrGenotypeData object.

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds <- loadGDS(gds_fn)

# Filter out set zero to read counts and
# missing to genotype calls of which meet the criteria.
gds <- setCallFilter(gds, dp_count = c(5, Inf))

# Now any functions of [GBScleanR] reference the genotype data
# stored in the "filt.genotype" node of the GDS file.

# If you need to set the "genotype" node, where store the raw genotype data
# as genotype to be referenced by the functions of GBScleanR,
# run the following.
gds <- resetCallFilter(gds)

# Reopening the connection to the GDS file also set the raw genotype again.
gds <- loadGDS(gds)

# Close the connection to the GDS file
closeGDS(gds)

Reset all filters made by setSamFilter(), setMarFilter(), and setCallFilter().

Description

Return all data intact.

Usage

resetFilter(object, ...)

## S4 method for signature 'GbsrGenotypeData'
resetFilter(object)

Arguments

Value

A GbsrGenotypeData object after removing all filters.

A GbsrGenotypeData object after removing all filters on markers.

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds <- loadGDS(gds_fn)

# `setCallFilter()` do not require summarized information of
# genotype counts and read counts.
gds <- setCallFilter(gds, dp_count = c(5, Inf))

# `setSamFilter()` and `setMarFilter()` needs information of
# the genotype count summary and the read count summary.
gds <- countGenotype(gds)
gds <- countRead(gds)

gds <- setSamFilter(gds,
                       id = getSamID(gds)[1:10],
                       missing = 0.2,
                       dp = c(5, Inf))

gds <- setMarFilter(gds,
                      id = getMarID(gds)[1:100],
                      missing = 0.2,
                      dp = c(5, Inf))

gds <- setInfoFilter(gds, mq = 40, qd = 20)

# Reset all filters applied above.
gds <- resetFilter(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Reset the filter made by setMarFilter()

Description

Remove "invalid" labels put on markers and make all markers valid.

Usage

resetMarFilter(object, ...)

## S4 method for signature 'GbsrGenotypeData'
resetMarFilter(object)

Arguments

Value

A GbsrGenotypeData object after removing all filters on markers.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Check the number of markers.
nmar(gds)

# Summarize the information needed for filtering.
gds <- countGenotype(gds)
gds <- countRead(gds)

# filter out some markers meeting the criteria.
gds <- setMarFilter(gds,
                      id = getMarID(gds)[1:100],
                      missing = 0.2,
                      dp = c(5, Inf))

# Check the number of the retained markers.
nmar(gds)

# Reset all filters applied above.
gds <- resetMarFilter(gds)

# Check the number of the markers again.
nmar(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Reset the filter made by setSamFilter()

Description

Remove "invalid" labels put on samples and make all samples valid.

Usage

resetSamFilter(object, ...)

## S4 method for signature 'GbsrGenotypeData'
resetSamFilter(object)

Arguments

Value

A GbsrGenotypeData object after removing all filters on samples.

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds <- loadGDS(gds_fn)

# Summarize the information needed for filtering.
gds <- countGenotype(gds)
gds <- countRead(gds)

gds <- setSamFilter(gds,
                       id = getSamID(gds)[1:10],
                       missing = 0.2,
                       dp = c(5, Inf))

# Reset all filters applied above.
gds <- resetSamFilter(gds)

# Close the connection to the GDS file
closeGDS(gds)

Description

Perform filtering of each genotype call, neither markers nor samples. Each genotype call is supported by its read counts for the reference allele and the alternative allele of a marker of a sample. setCallFilter() set missing to the genotype calls which are not reliable enough and set zero to reference and alternative read counts of the genotype calls.

Usage

setCallFilter(
  object,
  dp_count = c(0, Inf),
  ref_count = c(0, Inf),
  alt_count = c(0, Inf),
  dp_qtile = c(0, 1),
  ref_qtile = c(0, 1),
  alt_qtile = c(0, 1),
  ...
)

## S4 method for signature 'GbsrGenotypeData'
setCallFilter(
  object,
  dp_count,
  ref_count,
  alt_count,
  dp_qtile,
  ref_qtile,
  alt_qtile
)

Arguments

Details

dp_qtile, ref_qtile, and alt_qtile use quantile values of read counts of each sample to decide the lower and upper limit of read counts. This function generate two new nodes in the GDS file linked with the given GbsrGenotypeData object. The filtered read counts and genotype calls will be stored in the data node in the "FAD" folder and the data node in the "FGT" folder, while the data node in the "CFT" stores call fitering informatin. To reset the filter applied by setCallFilter(), run resetCallFilter().

Value

A GbsrGenotypeData object with filters on genotype calls.

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds <- loadGDS(gds_fn)

# Filter out genotype calls supported by less than 5 reads.
gds <- setCallFilter(gds, dp_count = c(5, Inf))

# Filter out genotype calls supported by reads less than
# the 20 percentile of read counts per marker in each sample.
gds <- setCallFilter(gds, dp_qtile = c(0.2, 1))

# Reset the filter
gds <- resetCallFilter(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Set fixed allele read biases of valid markers

Usage

setFixedBias(object, bias, ...)

## S4 method for signature 'GbsrGenotypeData'
setFixedBias(object, bias)

Arguments

Details

Since the bias set by setFixedBias() function is the reference allele read bias. Thus, the values 0 and 1 mean that the marker only gives alternative and reference allele reads, respectively. Set these fixed biases if some of your markers are dominant markers.

Value

A GbsrGenotypeData object after adding dominant marker information

Examples

# Create a GDS file from a sample VCF file.
vcf_fn <- system.file("extdata", "sample.vcf", package = "GBScleanR")
gds_fn <- tempfile("sample", fileext = ".gds")
gbsrVCF2GDS(vcf_fn = vcf_fn, out_fn = gds_fn, force = TRUE)

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds <- loadGDS(gds_fn)

# Set fixed allele read biases.
# Initialize the bias vector to be assinged.
bias <- rep(NA, nmar(gds))

# As an example, select 20 markers randomly and assign 0 or 1 to them.
# Since the bias set by setFixedBias() function is the reference allele read
# bias. Thus, the values 0 and 1 means that the marker only gives alternative
# and reference allele reads, respectively.
# Set these fixed biases if some of your markers are dominant markers.
bias[sample(seq_along(bias), 20)] <- sample(c(0, 1), 20, replace = TRUE)

gds <- setFixedBias(gds, bias = bias)

# Close the connection to the GDS file
closeGDS(gds)

Description

A VCF file usually has marker quality metrics in the INFO filed and those are stored in a GDS file created via GBScleanR. This function filter out markers based on those marker quality metrics.

Usage

setInfoFilter(
  object,
  mq = 0,
  fs = Inf,
  qd = 0,
  sor = Inf,
  mqranksum = c(-Inf, Inf),
  readposranksum = c(-Inf, Inf),
  baseqranksum = c(-Inf, Inf),
  ...
)

## S4 method for signature 'GbsrGenotypeData'
setInfoFilter(object, mq, fs, qd, sor, mqranksum, readposranksum, baseqranksum)

Arguments

Details

Detailed explanation of each metric can be found in GATK's web site.

Value

A GbsrGenotypeData object with filters on markers.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

gds <- setInfoFilter(gds, mq = 40, qd = 20)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Search markers which do not meet the criteria and label them as "invalid".

Usage

setMarFilter(
  object,
  id = NA_integer_,
  missing = 1,
  het = c(0, 1),
  mac = 0,
  maf = 0,
  ad_ref = c(0, Inf),
  ad_alt = c(0, Inf),
  dp = c(0, Inf),
  mean_ref = c(0, Inf),
  mean_alt = c(0, Inf),
  sd_ref = Inf,
  sd_alt = Inf,
  ...
)

## S4 method for signature 'GbsrGenotypeData'
setMarFilter(
  object,
  id,
  missing,
  het,
  mac,
  maf,
  ad_ref,
  ad_alt,
  dp,
  mean_ref,
  mean_alt,
  sd_ref,
  sd_alt
)

Arguments

Details

For mean_ref, mean_alt, sd_ref, and sd_alt, this function calculate mean and standard deviation of reads obtained for samples at each SNP marker. If a mean read counts of a marker was smaller than the specified lower limit or larger than the upper limit, this function labels the marker as "invalid". In the case of sd_ref and sd_alt, standard deviations of read counts of each marker are checked and the markers having a larger standard deviation will be labeled as "invalid". To check valid and invalid markers, run validMar().

Value

A GbsrGenotypeData object with filters on markers.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the information needed for filtering.
gds <- countGenotype(gds)
gds <- countRead(gds)

gds <- setMarFilter(gds,
                      id = getMarID(gds)[1:100],
                      missing = 0.2,
                      dp = c(5, Inf))

# Close the connection to the GDS file.
closeGDS(gds)

Description

Specify two or more samples in the dataset as parents of the population. Markers will be filtered out up on your specification.

Usage

setParents(object, parents, nonmiss = FALSE, mono = FALSE, bi = FALSE, ...)

## S4 method for signature 'GbsrGenotypeData'
setParents(object, parents, nonmiss, mono, bi)

Arguments

Details

The clean function of GBScleanR uses read count information of samples and their parents separately to estimate most probable genotype calls of them. Therefore, you must specify proper samples as parents via this function. If you would like to remove SNP markers which are not biallelic and/or not monomorphic in each parent, set mono = TRUE and bi = TRUE. The replicates of parental samples specified to the repliate argument of setParents() will have the same genotypes at all markers in the estimated genotypes obtained via estGeno(). In the genotype estimation by estGeno(), the Viterbi scores for each possible genotype at each marker for the replicates will be replaced with the average score for the replicates.

Value

A GbsrGenotypeData object with parents information.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Find the IDs of parental samples.
parents <- grep("Founder", getSamID(gds), value = TRUE)

# Set the parents and flip allele information
# if the reference sample (Founder1 in our case) has homozygous
# alternative genotype at some markers of which alleles will
# be swapped to make the reference sample have homozygous
# reference genotype.
gds <- setParents(gds, parents = parents)

# Initialize a scheme object stored in the slot of the GbsrGenotypeData.
# We chose `crosstype = "pair"` because two inbred founders were mated
# in our breeding scheme.
# We also need to specify the mating matrix which has two rows and
# one column with integers 1 and 2 indicating a sample (founder)
# with the memberID 1 and a sample (founder) with the memberID 2
# were mated.
gds <- initScheme(gds, mating = cbind(c(1:2)))

# Add information of the next cross conducted in our scheme.
# We chose 'crosstype = "selfing"', which do not require a
# mating matrix.
gds <- addScheme(gds, crosstype = "selfing")

# Execute error correction by estimating genotype and haplotype of
# founders and offspring.
gds <- estGeno(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Not implemented yet. This function assign identifiers that indicates which samples are replicates those which should have the same genotypes at all markers.

Usage

setReplicates(object, replicates, ...)

## S4 method for signature 'GbsrGenotypeData'
setReplicates(object, replicates)

Arguments

Details

The replicates of samples specified in setReplicates() will have the same genotypes at all markers in the estimated genotypes obtained via estGeno(). In the genotype estimation by estGeno(), the Viterbi scores for each possible genotype (haplotype) at each marker for the replicates will be replaced with the average score for the replicates.

Value

A GbsrGenotypeData object with genotype count information.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

gds <- setParents(gds, parents = c("Founder1", "Founder2"))

# When your data has 100 samples, two replicates for each offspring,
# and the samples are ordered as the 1st replicate followed by the 2nd
# replicate, you can specify replicates as below.
# gds <- setReplicates(gds, replicates = rep(1:50, each = 2))

# If you need to confirm the order of samples, run the following code.
# id <- getSamID(gds)

# Replicate IDs should be set also to parents. Therefore, please include

# Close the connection to the GDS file.
closeGDS(gds)

Description

Search samples which do not meet the criteria and label them as "invalid".

Usage

setSamFilter(
  object,
  id = NA_character_,
  missing = 1,
  het = c(0, 1),
  mac = 0,
  maf = 0,
  ad_ref = c(0, Inf),
  ad_alt = c(0, Inf),
  dp = c(0, Inf),
  mean_ref = c(0, Inf),
  mean_alt = c(0, Inf),
  sd_ref = Inf,
  sd_alt = Inf,
  ...
)

## S4 method for signature 'GbsrGenotypeData'
setSamFilter(
  object,
  id,
  missing,
  het,
  mac,
  maf,
  ad_ref,
  ad_alt,
  dp,
  mean_ref,
  mean_alt,
  sd_ref,
  sd_alt
)

Arguments

Details

For mean_ref, mean_alt, sd_ref, and sd_alt, this function calculate mean and standard deviation of reads obtained at SNP markers of each sample. If a mean read counts of a sample was smaller than the specified lower limit or larger than the upper limit, this function labels the sample as "invalid". In the case of sd_ref and sd_alt, standard deviations of read counts of each sample are checked and the samples having a larger standard deviation will be labeled as "invalid". To check valid and invalid samples, run validSam().

Value

A GbsrGenotypeData object with filters on samples.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize the information needed for filtering.
gds <- countGenotype(gds)
gds <- countRead(gds)

gds <- setSamFilter(gds,
                       id = getSamID(gds)[1:10],
                       missing = 0.2,
                       dp = c(5, Inf))

# Close the connection to the GDS file.
closeGDS(gds)

Show the information stored in a GbsrScheme object

Description

Print the information of each generation in a GbsrScheme object in the scheme slot of a GbsrGenotypeData object. A GbsrScheme object stores information of a population size, mating combinations and a type of cross applied to each generation of the breeding process to generate the population which you are going to subject to the estGeno() function.

Usage

showScheme(object, ...)

## S4 method for signature 'GbsrGenotypeData'
showScheme(object)

## S4 method for signature 'GbsrScheme'
showScheme(object, parents_name, pedigree)

Arguments

Value

NULL. Print the scheme information on the R console.

See Also

initScheme() and addScheme()

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Biparental F2 population.
gds <- setParents(gds, parents = c("Founder1", "Founder2"))

# setParents gave member ID 1 and 2 to Founder1 and Founder2, respectively.
gds <- initScheme(gds, mating = cbind(c(1:2)))

# Now the progenies of the cross above have member ID 3.
# If `crosstype = "selfing"` or `"sibling"`, you can omit a `mating` matrix.
gds <- addScheme(gds, crosstype = "self")

# Now you can execute `estGeno()` which requires a [GbsrScheme] object.

# Close the connection to the GDS file
closeGDS(gds)

Description

Markers within the length of the sequenced reads (usually ~ 150 bp, up to your sequencer) potentially have redundant information and those will cause unexpected errors in error correction which assumes independency of markers each other. This function only retains the first marker or the least missing rate marker from the markers locating within the specified stretch.

Usage

thinMarker(object, range = 150, ...)

## S4 method for signature 'GbsrGenotypeData'
thinMarker(object, range)

Arguments

Details

This function search valid markers from the first marker of each chromosome and compare its physical position with a neighbor marker. If the distance between those markers are equal or less then range, one of them which has a larger missing rate will be removed (labeled as invalid marker). When the first marker was retained and the second marker was removed as invalid marker, next the distance between the first marker and the third marker will be checked and this cycle is repeated until reaching the end of each chromosome. Run validMar() to check the valid SNP markers.

Value

A GbsrGenotypeData object with filters on markers.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

# Summarize genotype count information to be used in thinMarker().
gds <- countGenotype(gds)
gds <- thinMarker(gds, range = 150)

closeGDS(gds) # Close the connection to the GDS file

Description

Return a logical vector indicating which are valid SNP markers.

Usage

validMar(object, chr = NULL, ...)

validMar(object) <- value

## S4 method for signature 'GbsrGenotypeData'
validMar(object, chr)

## S4 replacement method for signature 'GbsrGenotypeData'
validMar(object) <- value

Arguments

Value

A logical vector of the same length with the number of total SNP markers

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

validMar(gds)

# Close the connection to the GDS file.
closeGDS(gds)

Description

Return a logical vector indicating which are valid samples.

Usage

validSam(object, parents = FALSE, ...)

validSam(object) <- value

## S4 method for signature 'GbsrGenotypeData'
validSam(object, parents)

## S4 replacement method for signature 'GbsrGenotypeData'
validSam(object) <- value

Arguments

Value

A logical vector of the same length with the number of total samples.

Examples

# Load data in the GDS file and instantiate a [GbsrGenotypeData] object.
gds_fn <- system.file("extdata", "sample.gds", package = "GBScleanR")
gds <- loadGDS(gds_fn)

validSam(gds)

# Close the connection the GDS file.
closeGDS(gds)