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Title:

Predicting Ploidal Level from Sequence Data

Description:

Predicts ploidal level from sequence data using site-based heterozygosity and a mixture models approach. See Gaynor et al. (2024) <doi:10.1002/aps3.11606>.

Version:

1.0.4

Encoding:

UTF-8

RoxygenNote:

7.3.3

Suggests:

knitr, dplyr, kableExtra, rmarkdown

Imports:

Rcpp (≥ 1.0.11), RcppArmadillo (≥ 14.0.2-1), truncdist, data.table, foreach, future, parallel, doParallel, extraDistr, RcppProgress, httr2, magrittr

LinkingTo:

Rcpp, RcppArmadillo, extraDistr, RcppProgress

SystemRequirements:

C++17 samtools (>= 1.10)

Depends:

R (≥ 4.1.0)

LazyData:

true

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

VignetteBuilder:

knitr

Maintainer:

Michelle L. Gaynor <shellyleegaynor@gmail.com>

URL:

http://mlgaynor.com/nQuack/, https://github.com/mgaynor1/nQuack/

BugReports:

https://github.com/mgaynor1/nQuack/issues

Config/testthat/edition:

NeedsCompilation:

yes

Packaged:

2026-07-07 13:46:08 UTC; shellygaynor

Author:

Michelle L. Gaynor

[aut, cre, cph]

Repository:

CRAN

Date/Publication:

2026-07-16 12:50:19 UTC

nQuack: Predicting Ploidal Level from Sequence Data

Description

Predicts ploidal level from sequence data using site-based heterozygosity and a mixture models approach. See Gaynor et al. (2024) doi:10.1002/aps3.11606.

Author(s)

Maintainer: Michelle L. Gaynor shellyleegaynor@gmail.com (ORCID) [copyright holder]

Remove Noise with the Beta Distribution

Description

Here we filter allele frequencies with a beta mixture model that contains 5 mixtures: three mixtures representing cytotypes included in nQuack and two mixtures representing a U-shaped distribution. We constrained the first three mixtures to have shape and scale parameters above 1, while the last two mixtures shape and scale are constrained to be less than 1. With this implementation of expectation-maximization, we utilizes the scaled probability of each data point belonging to each mixture model to remove site where the probability of belonging to a U-shaped mixture is higher than the probability of belonging to any other mixture. Due to the computational time needed to run the expectation-maximization algorithm, by default, we simple calculate this probability matrix with the E-step and do not run the complete algorithm.

Usage

Bclean(xm, plot = TRUE, quick = TRUE)

Arguments

xm

Matrix with total coverage and coverage for a randomly sampled allele.

plot

Default to TRUE. The plots do not share the same y-axis, so careful interpretation is key. Warning, if nothing is removed, the plot of removed data will be missing.

quick

Default to TRUE. If set as FALSE, the expectation-maximization algorithm will be run in full.

Value

Numeric matrix with total coverage and coverage for a randomly sampled allele.

Examples

out <- Bclean(xm[1:100,])

Setup Basic Example

Description

This function was made to download all files needed to run the Basic Example vignette. It downloads a zipped file from Zenodo and unzips it to the "inst/extdata/" directory within the nQuack package.

Usage

SetupBasicExample(overwrite = FALSE)

Arguments

overwrite

Logical. If TRUE, the function will overwrite any existing files in the data directory. Default is FALSE.

Value

This function downloads files to the "inst/extdata/" directory within the nQuack package and does not return any value. The function will print messages as it downloads to keep you updated on the progress.

Examples

if(exists("crazy")){
   SetupBasicExample(overwrite = FALSE)
}

Calculate Alpha and Beta from Mean and Variance

Description

Calculate Alpha and Beta from Mean and Variance

Usage

alphabetacalc(mu, var)

Arguments

mu

Mean.

var

Variance.

Value

Numeric vector of alpha and beta.

Examples

abc <- alphabetacalc(0.5, 0.01)

Calculate Alpha and Beta from Mean, Tau, and Error rate.

Description

Calculate Alpha and Beta from Mean, Tau, and Error rate.

Usage

alphabetacalctau(mu, tau, error)

Arguments

mu

Mean.

tau

Overdispersion parameter. Ranges from 0 to 1, where 0 indicates less overdispersion and 1 indicates high overdispersion. Here tau must be greater than 0.

error

Sequencing error rate.

Value

Numeric vector of alpha and beta.

Examples

abc <- alphabetacalctau(0.5, 0.01, 0.01)

Vector-based - Calculate Alpha and Beta from Mean, Tau, and Error rate.

Description

Vector-based - Calculate Alpha and Beta from Mean, Tau, and Error rate.

Usage

alphabetacalctauvec(mu, tau, error)

Arguments

mu

Vector of mean.

tau

Overdispersion parameter. Ranges from 0 to 1, where 0 indicates less overdispersion and 1 indicates high overdispersion. Here tau must be greater than 0.

error

Sequencing error rate. Ranges from 0 to 1.

Value

Numeric matrix of alpha and beta.

Examples

abc <- alphabetacalctauvec(c(0.5,0.5), 0.01, 0.01)

Vector-based - Calculate Alpha and Beta from Mean and Variance

Description

Vector-based - Calculate Alpha and Beta from Mean and Variance

Usage

alphabetacalcvec(mu, var)

Arguments

mu

Vector of mean.

var

Vector of variance.

Value

Numeric matrix of alpha and beta.

Examples

abc <- alphabetacalcvec(c(0.5, 0.5), c(0.01, 0.01))

Model Selection - Expectation Maximization - Optimal Distribution and Type

Description

This function was made to run a subset of models based on a selected distribution and type. There are many limitations to this function to make this tractable, as there are 128 models that could be run with our package. Here we do not include models or comparisons we found unhelpful, this includes the nQuire implementation and log-likelihood ratio tests.

Usage

bestquack(
  xm,
  distribution,
  type,
  uniform,
  mixtures = c("diploid", "triploid", "tetraploid", "hexaploid", "pentaploid"),
  samplename,
  trunc = c(0, 0),
  lowvar = FALSE,
  tau = NA,
  error = NA,
  verbose = TRUE
)

Arguments

xm

Matrix with two columns with total coverage and coverage for a randomly sampled allele.

distribution

May be set to normal, beta, or beta-binomial. We do not include the implementation with nQuire.

type

May be equal to fixed, fixed_2, or fixed_3.

uniform

If equal to 1, a uniform mixture is included. If equal to 0, no uniform mixture is included.

mixtures

Defaults to c("diploid", "triploid", "tetraploid", "hexaploid", "pentaploid").

samplename

Name of sample to be included in output.

trunc

List of two values representing the lower and upper bounds for allele frequency truncation ,c_L and c_U. If allele frequency truncation was done to remove error, then you do not need to truncate the expected. If no truncation has been done, this should be set to c(0,0), which is the default.

lowvar

Default to FALSE. When false, variance is equal to 0.01. If set to TRUE and tau and error are not provided, the variance will be set as 0.001.

tau

Sequencing overdispersion parameter. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

error

Sequencing error rate. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

verbose

Default to TRUE. If TRUE, progress messages will be printed to the console.

Value

BIC scores and log-likelihood (LL) for the included mixture models. For BIC, the smallest score is the most likely model. For LL, the largest score is the most likely model.

Examples

 out <- bestquack(xm[1:100,],
                  distribution = "normal",
                  type = "fixed",
                  uniform = 1,
                  samplename = "sample1")

Denoise Data

Description

Here we filter allele frequencies with a normal + uniform mixture model. nQuack utilizes the scaled probability of each data point belonging to each mixture model, which is inferred in the expectation maximization algorithm. We remove allele frequencies where probability of belonging to uniform mixture is higher than their probability of belonging to any other mixture. We also implement nQuire's denoise method here, which utilizes the inferred alpha parameter and a histogram of base frequencies to filter the data.

Usage

denoise_data(xm, plot = TRUE, filter = "both")

Arguments

xm

Matrix with total coverage and coverage for a randomly sampled allele.

plot

Default to TRUE. The plots do not share the same y-axis, so careful interpretation is key. Warning, if nothing is removed, the plot of removed data will be missing.

filter

Indicates which method to remove data based upon. Options: 'both', 'nquire', or 'nquack'. nQuack utilizes the scaled probability of each data point belonging to each mixture model, removing sites where the probability of belonging to uniform mixture is higher than their probability of belonging to any other mixture. nQuire utilizes the inferred alpha parameter and a histogram of base frequencies to filter the data.

Value

Numeric matrix with total coverage and coverage for a randomly sampled allele.

Examples

out <- denoise_data(xm[1:100,])

Expectation maximization - Beta Distribution

Description

This function calculates the log-likelihood using the expectation maximization algorithm with Nelder-Mead numerical optimization and a beta distribution.

Usage

emstepB(parmlist, xi, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance values.

xi

List of observations, in this case allele frequencies.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

String indicating model type. Options: "free" (estimated parameter(s): alpha, mean, and variance), "fixed" (estimated parameter(s): alpha), "fixed-2" (estimated parameter(s): alpha and variance), or "fixed-3" (estimated parameter(s): variance). If avec is length of 1, fixed and fixed-3 will not be able to return a log-likelihood.

Value

List of elements including the log likelihood, the negative log likelihood, the number of iterates, and the optimized parameter values.

Examples

  if(exists("crazy")){
    xi <- (xm[,2]/xm[,1])
    p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
             mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
             svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
    mout <- emstepB(p,
                    xi,
                    niter = 100,
                    epsilon = 0.1,
                    trunc = c(0.0,0.0))
}

Expectation maximization - Beta + Beta + Beta Distribution

Description

This function is made for the Bclean() function and preforms expectation maximization with Nelder-Mead numerical optimization for beta distribution.

Usage

emstepB3(parmlist, xi, niter, epsilon, trunc)

Arguments

parmlist

A list containing initial alpha, mean, and variance.

xi

Matrix where the first column is total coverage and the second is the count of base A or B.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

Value

List of elements including the negative log likelihood, the number of iterates, and the optimized parameter values.

Examples

 if(exists("crazy")){
    xi <- (xm[,2]/xm[,1])
    tcalc <- alphabetacalcvec(mu = c(0.287, 0.50, 0.713),
                              var = c(0.01, 0.01, 0.01))
    set <-  list(avec = c(0.25, 0.25, 0.25, 0.125, 0.125),
                 t1vec = c(tcalc[,1], 0.5, 0.33),
                 t2vec = c(tcalc[,2], 0.33, 0.5))
    checkB <- emstepB3(set,
                       xi,
                       1000,
                       0.1,
                       c(0,0))
}

Expectation maximization - Beta-Binomial Distribution

Description

This function calculates the negative log-likelihood using the expectation maximization algorithm with Nelder-Mead numerical optimization and beta-binomial distribution.

Usage

emstepBB(parmlist, xm, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance.

xm

Matrix where the first column is total coverage and the second is the count of base A or B.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

String indicating "Free" or "Fixed".

Value

List of elements including the negative log likelihood, the number of iterates, and the optimized parameter values.

Examples

if(exists("crazy")){
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
           mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
           svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepBB(p,
                   xm,
                   niter = 100,
                   epsilon = 0.1,
                   trunc = c(0.0,0.0))
}

Expectation maximization - Beta-Binomial and Uniform Distributions

Description

This function calculates the log-likelihood using the expectation-maximization algorithm with Nelder-Mead numerical optimization and beta distribution with one uniform mixture.

Usage

emstepBBU(parmlist, xm, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance values.

xm

Matrix where the first column is total coverage and the second is the count of base A or B.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

Value

List of elements including the log likelihood, the negative log likelihood, the number of iterates, and the optimized parameter values.

Examples

 if(exists("crazy")){
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
          mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
          svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepBBU(p,
                    xm,
                    niter = 100,
                    epsilon = 0.1,
                    trunc = c(0.0,0.0))
}

Expectation maximization - Beta and Uniform Distributions

Description

This function calculates the log-likelihood using the expectation maximization algorithm with Nelder-Mead numerical optimization and beta distribution with one uniform mixture.

Usage

emstepBU(parmlist, xi, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance values.

xi

List of observations, in this case allele frequencies.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

String indicating model type. Options: "free" (estimated parameter(s): alpha, mean, and variance), "fixed" (estimated parameter(s): alpha), "fixed_2" (estimated parameter(s): alpha and variance), or "fixed_3" (estimated parameter(s): variance). If avec is length of 1, fixed and fixed_3 will not be able to return a log-likelihood.

Value

List of elements including the log likelihood, the negative log likelihood, the number of iterates, and the optimized parameter values.

Examples

if(exists("crazy")){
  xi <- (xm[,2]/xm[,1])
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
           mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
           svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepBU(p,
                   xi,
                   niter = 100,
                   epsilon = 0.1,
                   trunc = c(0.0,0.0))
}

Expectation maximization - Normal Distribution

Description

This function calculates the log-likelihood using the expectation maximization algorithm with the Normal Distribution. This code follows nQuire and does not use an augmented likelihood.

Usage

emstepN(parmlist, xi, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance values.

xi

List of observations, in this case allele frequencies.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

Value

List of elements including the log-likelihood, the number of iterates, and the optimized parameter values.

Examples

 if(exists("crazy")){
  xi <- (xm[,2]/xm[,1])
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
           mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
           svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepN(p,
                  xi,
                  niter = 100,
                  epsilon = 0.1,
                  trunc = c(0.0,0.0))
}

Expectation maximization - Normal Distribution

Description

This function calculates the log-likelihood using the expectation maximization algorithm with the Normal Distribution. This code is not identical to nQuire and uses an augmented likelihood.

Usage

emstepNA(parmlist, xi, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance values.

xi

List of observations, in this case allele frequencies.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

Value

List of elements including the log-likelihood, the number of iterates, and the optimized parameter values.

Examples

if(exists("crazy")){
  xi <- (xm[,2]/xm[,1])
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
           mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
           svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepNA(p,
                   xi,
                   niter = 100,
                   epsilon = 0.1,
                   trunc = c(0.0,0.0))
}

Expectation maximization - Normal and Uniform Distribution

Description

This function calculates the log-likelihood using the expectation maximization algorithm with the Normal-Uniform Distribution. This code follows nQuire and does not use an augmented likelihood.

Usage

emstepNU(parmlist, xi, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance values. The list of alpha must include a proportion for the uniform mixture.

xi

List of observations, in this case allele frequencies.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

Value

List of elements including the log-likelihood, the number of iterates, and the optimized parameter values.

Examples

 if(exists("crazy")){
  xi <- (xm[,2]/xm[,1])
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
           mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
           svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepNU(p,
                   xi,
                   niter = 100,
                   epsilon = 0.1,
                   trunc = c(0.0,0.0))
}

Expectation maximization - Normal Distribution

Description

This function calculates the log-likelihood using the expectation maximization algorithm with the Normal-Uniform Distribution. This code is not identical to nQuire and uses an augmented likelihood.

Usage

emstepNUA(parmlist, xi, niter, epsilon, trunc, type = "free")

Arguments

parmlist

A list containing initial alpha, mean, and variance values. The list of alpha must include a proportion for the uniform mixture.

xi

List of observations, in this case allele frequencies.

niter

Max number of iterates.

epsilon

Epsilon value for convergence tolerance. When the absolute delta log-likelihood is below this value, convergence is reached.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

type

Value

List of elements including the log-likelihood, the number of iterates, and the optimized parameter values.

Examples

if(exists("crazy")){
  xi <- (xm[,2]/xm[,1])
  p = list(avec = c(0.11, 0.22, 0.34, 0.22, 0.11),
           mvec = c(0.20, 0.33, 0.50, 0.67, 0.80),
           svec = c(0.01, 0.01, 0.01, 0.01, 0.01));
  mout <- emstepNUA(p,
                    xi,
                    niter = 100,
                    epsilon = 0.1,
                    trunc = c(0.0,0.0))
}

E-Step for Expectation Maximization - Beta + Beta + Beta Distribution

Description

This is used in the Bclean() function. Here we complete the E-Step and calculate the log-likelihood. Modifications include a correction for the truncated distribution.

Usage

estepB3(parmlist, xi, trunc)

Arguments

parmlist

A list containing initial alpha, mean, and variance.

xi

List of observations, in this case allele frequencies.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}.

Value

List of zprob, parm.list, xi, denom, and trunc.

Examples

 if(exists("crazy")){
 xi <- (xm[,2]/xm[,1])
 tcalc <- alphabetacalcvec(mu = c(0.287, 0.50, 0.713),
                           var = c(0.01, 0.01, 0.01))
 set <-  list(avec = c(0.25, 0.25, 0.25, 0.125, 0.125),
              t1vec = c(tcalc[,1], 0.5, 0.33),
              t2vec = c(tcalc[,2], 0.33, 0.5))
 checkB <- estepB3(set,
                   xi,
                   c(0,0))
 }

Variance calculation from Mean, Tau, and Sequencing Error

Description

This function is used to calculate variance.

Usage

muvarcalcvec(mu, tau, error)

Arguments

mu

Vector of means.

tau

Sequence overdispersion parameter for read counts.

error

Sequencing error rate.

Value

Mean and variance for the associated tau and error.

Examples

var <- muvarcalcvec(mu = 0.5, tau = 0.01, error = 0.01)

Data Preparation - Use nQuire's Data

Description

This function reduce a three column data frame to two columns by randomly sampling allele A or B for every site. This is used in our function process_nquire()

Usage

nQuire_reformat(xm)

Arguments

xm

A matrix with three columns: Total Coverage, Counts for Allele A, and Counts for Allele B.

Value

Numeric Matrix with total coverage and coverage for a randomly sampled allele.

Examples

 if(exists("nxm")){
  out <- nQuire_reformat(nxm)
}

Prepare Data - Step 1

Description

This function transforms a BAM file into a text file. Specifically, this function uses samtools mpileup to translate your BAM into a tab-separated file. We then filter this file to remove indels and deletions. When running this function, a temporary folder will be created (named 'temp/'), however this folder will be removed once the process is complete.

Usage

prepare_data(name, inpath, outpath, tempfolder = "temp")

Arguments

name

File name without the suffix. For example, if your file is called "frog.bam", this input should be "frog".

inpath

Location of input file.

outpath

Location for output file.

tempfolder

Location for temp folder.

Details

Warning, due to the processing time needed for samtools mpileup, this step may take some time. This function also requires samtools to be located locally. Please see our Data Preparation article for more information. Warning, this writes a temporary folder titled 'temp'. If you want to run multiple samples at once, we suggest you set the working directory to separate locations to ensure that your temp folder/files are not overwritten.

Value

Writes text file with the following columns: chromosome, position, depth, A, C, G, and T.

Examples

if(exists("crazy")){
## Prepare many samples
  inpath <- "filtered/"
  outpath <- "Processed/"
  filelist <- list.files(path = inpath, pattern = "*.bam" )
  filelist <- gsub(".bam", "", filelist)
  for( i in 1:length(filelist)){
    prepare_data(filelist[i], inpath, outpath)
  }
}

Process Data - Step 2

Description

Based on the file generated with prepare_data(), which contains the total depth and sequencing coverage for each nucleotide (A, C, G, and T), this function remove all but single nucelotide polymorphisms. When supplied, this function will filter on coverage or allele frequency. To filter by total coverage, a user must supply the min.depth and max.depth.quantile.prob. If an error is provided, sites will be retained where allele coverage is greater than the sequencing error rate times the total coverage, but less than one minus the sequencing error rate times the total coverage. Lastly, based on trunc, allele frequencies will be filtered based on a provided lower and upper bound. Finally, the function samples a single allele frequency per site to avoid data duplication.

Usage

process_data(
  file,
  min.depth = 2,
  max.depth.quantile.prob = 0.9,
  error = 0.01,
  trunc = c(0, 0)
)

Arguments

file

Output txt file created with prepare_data().

min.depth

Minimum sequencing depth, default as 2.

max.depth.quantile.prob

Maximum sequencing depth quantile cut off, default = 0.9.

error

Sequencing error rate. If an error is provided, sites will be retained where allele coverage is greater than the sequencing error rate times the total coverage, but less than one minus the sequencing error rate times the total coverage.

trunc

List of two values representing the lower and upper bounds, c_L and c_U which are used to filter allele frequencies.

Value

Numeric matrix with total coverage and coverage for a randomly sampled allele.

Examples

if(file.exists("mybamfile.csv")){
  cleaned_data <- process_data(file = "mybamfile.csv")
}

Use nQuire's Data

Description

If you happen to like nQuire's data preparation more than ours, uses their data in our program. After processing samples with nQuire's create and view functions, the resulting text file can be read into R. To prepare the data frame for nQuack, we reduce the three column data frame to two columns by randomly sampling allele A or B for every site.

Usage

process_nquire(file)

Arguments

file

Output text file created with nQuire.

Value

Numeric matrix with total coverage and coverage for a randomly sampled allele.

Examples

if(file.exists("mynQuirefile.bin")){
  cleaned_data <- process_nquire(file = "mynQuirefile.bin")
}

Data Preparation - Matrix Filtering

Description

Based on supplied matrix with total depth and sequencing coverage for each nucleotide (A, C, G, and T) this function remove all but single nucelotide polymorphisms. When supplied, this function will filter on coverage or allele frequency. Finally, the function samples a single allele frequency per site to avoid data duplication.

Usage

process_rcpp(x, mindepth, maxprob, trunc, error)

Arguments

x

Matrix with five columns: Depth, A, C, G, and T.

mindepth

Minimum depth, default = 15.

maxprob

Maximum depth quantile cut off, default = 0.9.

trunc

List of two values representing the lower and upper bounds,c_{L} and c_{U}.

error

Sequencing error rate.

Value

Numeric Matrix with total coverage and coverage for a randomly sampled allele.

Examples

if(exists("dfm")){
  allelefreq <- process_rcpp(dfm, min.depth, max.depth.quantile.prob, trunc, error)
}

Model Selection - Expectation Maximization - Beta Mixture

Description

This function uses the expectation maximization of both the beta and beta-uniform mixture models for model selection. Here we can run up to 32 mixture models.

Usage

quackBeta(
  xm,
  samplename,
  cores,
  parallel = FALSE,
  trunc = c(0, 0),
  lowvar = FALSE,
  tau = NA,
  error = NA,
  free = FALSE,
  verbose = TRUE
)

Arguments

xm

Matrix with two columns with total coverage and coverage for a randomly sampled allele.

samplename

Name of sample to be included in output.

cores

Threads available to run process in parallel.

parallel

default = FALSE, set to true if cores > 1.

trunc

List of two values representing the lower and upper bounds for allele frequency truncation , c_L and c_U. If allele frequency truncation was done to remove error, then you do not need to truncate the expected. If no truncation has been done, this should be set to c(0,0), which is the default.

lowvar

Default to FALSE. When false, variance is equal to 0.01. If set to TRUE and tau and error are not provided, the variance will be set as 0.001.

tau

Sequencing overdispersion parameter. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

error

Sequencing error rate. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

free

default = FALSE, skip the free model calculation and does not calculate delta log-likelihood.

verbose

Default to TRUE. If TRUE, progress messages will be printed to the console.

Value

BIC scores and log-likelihood (LL) mixture models including diploid, triploid, tetraploid, pentaploid, and hexaploid. When free = TRUE, the delta log-likelihood (dLL) is calculated based on the associated free model (without or with a uniform mixture). For BIC or delta-log likelihood, the smallest score is the most likely model. For LL, the largest score is the most likely model. The type indicates which parameters are estimated. This function allows all parameters (type = 'free'), only alpha (type = 'fixed'), only alpha and variance (type = 'fixed_2'), and only variance (⁠type ='fixed_3⁠) to be estimated for each mixture.

Examples

 out <- quackBeta(xm[1:100,], samplename = "sample1", cores = 1)

Model Selection - Expectation Maximization - Beta-Binomial Mixture

Description

This function uses the expectation maximization of both the beta-binomial and beta-binomial-uniform mixture models for model selection. Here we can run up to 32 mixture models.

Usage

quackBetaBinom(
  xm,
  samplename,
  cores,
  parallel = FALSE,
  trunc = c(0, 0),
  lowvar = FALSE,
  tau = NA,
  error = NA,
  free = FALSE,
  verbose = TRUE
)

Arguments

xm

Matrix with two columns with total coverage and coverage for a randomly sampled allele.

samplename

Name of sample to be included in output.

cores

Threads available to run process in parallel.

parallel

default = FALSE, set to true if cores > 1.

trunc

List of two values representing the lower and upper bounds for allele frequency truncation , c_L and c_U. If allele frequency truncation was done to remove error, then you do not need to truncate the expected. If no truncation has been done, this should be set to c(0,0), which is the default.

lowvar

Default to FALSE. When false, variance is equal to 0.01. If set to TRUE and tau and error are not provided, the variance will be set as 0.001.

tau

Sequencing overdispersion parameter. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

error

Sequencing error rate. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

free

default = FALSE, skip the free model calculation and does not calculate delta log-likelihood.

verbose

Default to TRUE. If TRUE, progress messages will be printed to the console.

Value

Examples

if(exists("crazy")){
  out <- quackBetaBinom(xm[1:100,], samplename = "sample1", cores = 1)
}

Bootstrapping - Expectation Maximization - Optimal Distribution and Type

Description

This function was made to assist with bootstrap replication for a set of models run a subset of models based on a selected distribution and type. There are many limitations to this function to make this tractable, as there are 128 models that could be run with our package. Here we do not include models or comparisons we found unhelpful, this includes the nQuire implementation and log-likelihood ratio tests.

Usage

quackNboots(
  xm,
  nboots = 100,
  distribution,
  type,
  uniform,
  mixtures = c("diploid", "triploid", "tetraploid", "hexaploid", "pentaploid"),
  samplename,
  trunc = c(0, 0),
  lowvar = FALSE,
  tau = NA,
  error = NA
)

Arguments

xm

Matrix with two columns with total coverage and coverage for a randomly sampled allele.

nboots

Number of bootstrap replicates to examine.

distribution

May be set to normal, beta, or beta-binomial. We do not include the implementation with nQuire.

type

May be equal to fixed, fixed_2, or fixed_3.

uniform

If equal to 1, a uniform mixture is included. If equal to 0, no uniform mixture is included.

mixtures

Defaults to c("diploid", "triploid", "tetraploid", "hexaploid", "pentaploid").

samplename

Name of sample to be included in output.

trunc

lowvar

Default to FALSE. When false, variance is equal to 0.01. If set to TRUE and tau and error are not provided, the variance will be set as 0.001.

tau

Sequencing overdispersion parameter. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

error

Sequencing error rate. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

Value

BIC scores and log-likelihood (LL) for included mixture models. For both, the smallest score is the most likely model.

@export

Examples

 out <- quackNboots(xm[1:100,],
                    distribution = "normal",
                    type = "fixed",
                    uniform = 1,
                    samplename = "sample1",
                    nboots = 2)

Model Selection - Expectation Maximization - Normal Mixture

Description

This function uses the expectation maximization of both the normal and normal-uniform mixture models for model selection. Here we can run up to 32 mixture models.

Usage

quackNormal(
  xm,
  samplename,
  cores,
  parallel = FALSE,
  trunc = c(0, 0),
  lowvar = FALSE,
  tau = NA,
  error = NA,
  free = FALSE,
  verbose = TRUE
)

Arguments

xm

Matrix with two columns with total coverage and coverage for a randomly sampled allele.

samplename

Name of sample to be included in output.

cores

Threads available to run process in parallel.

parallel

default = FALSE, set to true if cores > 1.

trunc

List of two values representing the lower and upper bounds for allele frequency truncation, c_L and c_U. If allele frequency truncation was done to remove error, then you do not need to truncate the expected. If no truncation has been done, this should be set to c(0,0), which is the default.

lowvar

Default to FALSE. When false, variance is equal to 0.01. If set to TRUE and tau and error are not provided, the variance will be set as 0.001.

tau

Sequencing overdispersion parameter. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

error

Sequencing error rate. If tau and error are provided, the variance of each mixture will be inferred from these values.

free

default = FALSE, skip the free model calculation and does not calculate delta log-likelihood.

verbose

Default to TRUE. If TRUE, progress messages will be printed to the console.

Value

Examples

 out <- quackNormalNQ(xm[1:100,], samplename = "sample1", cores = 1)

Model Selection - Expectation Maximization - Normal Mixture (nQuire)

Description

This function uses the expectation maximization of both the normal and normal-uniform mixture models for model selection based on the nQuire approach. Here we can run up to 32 mixture models.

Usage

quackNormalNQ(
  xm,
  samplename,
  cores,
  parallel = FALSE,
  trunc = c(0, 0),
  lowvar = FALSE,
  tau = NA,
  error = NA,
  free = FALSE,
  verbose = TRUE
)

Arguments

xm

Matrix with two columns with total coverage and coverage for a randomly sampled allele.

samplename

Name of sample to be included in output.

cores

Threads available to run process in parallel.

parallel

default = FALSE, set to true if cores > 1.

trunc

List of two values representing the lower and upper bounds for allele frequency truncation , c_L and c_U. If allele frequency truncation was done to remove error, then you do not need to truncate the expected. If no truncation has been done, this should be set to c(0,0), which is the default.

lowvar

Default to FALSE. When false, variance is equal to 0.01. If set to TRUE and tau and error are not provided, the variance will be set as 0.001.

tau

Sequencing overdispersion parameter. If tau and error are provided, the variance of each mixture will be inferred from these values. If not, the variance by default is equal to 0.01 or 0.001.

error

Sequencing error rate. If tau and error are provided, the variance of each mixture will be inferred from these values.

free

default = FALSE, skip the free model calculation and does not calculate delta log-likelihood.

verbose

Default to TRUE. If TRUE, progress messages will be printed to the console.

Value

Examples

 out <- quackNormalNQ(xm[1:100,], samplename = "sample1", cores = 1)

Model Selection - Based on BIC or Log-Likelihood

Description

This function is for model interpretation.

Usage

quackit(
  model_out,
  summary_statistic = "BIC",
  mixtures = c("diploid", "triploid", "tetraploid", "hexaploid", "pentaploid")
)

Arguments

model_out

Data frame containing, at minimum, columns labeled LL, type, mixture, distribution, and BIC.

summary_statistic

May be equal to BIC or LL.

mixtures

Defaults to c("diploid", "triploid", "tetraploid", "hexaploid", "pentaploid").

Value

Returns data frame with the most likely model for each set of mixtures. Includes the best and second best mixtures, as well as the difference between the two. We only use BIC or LL to compare within each distribution and type. To identify the most accurate model, you will need to compare accuracy across distributions and types using a set of known samples. The distributions include Normal, Beta, and Beta-Binomial - each with and without a uniform mixture. The type indicates which parameters are estimated for the mixtures: all parameters (type = 'free', only used to calculate delta log-likelihood), only alpha (type = 'fixed'), only alpha and variance (type = 'fixed_2'), and only variance (⁠type ='fixed_3⁠) to be estimated for each mixture.

Examples

out <- quackNormal(xm[1:100,], samplename = "sample1", cores = 1)
goose <- quackit(out)

Calculate Alpha and Beta from Mean and Variance

Description

Calculate Alpha and Beta from Mean and Variance

Usage

resample_xm(xm, n)

Arguments

xm

Matrix with total coverage and coverage at a randomly sampled allele.

n

Length of matrix.

Value

Randomly sampled matrix.

Examples

outdf <- resample_xm(as.matrix(xm), n = 10)

Calculate Variance from Mean, Tau, and Sequencing Error

Description

This function is used to replace variance in mixture model sets.

Usage

setconvert(set, tau, error)

Arguments

set

A list of lists, each of the lists must contain avec, mvec, and svec.

tau

Sequence overdispersion parameter for read counts.

error

Sequencing error rate.

Value

Mean and variance for the associated tau and error.

Examples

 set <- c()
 set[[1]] =  list(avec = c(1.00), mvec = c(0.50), svec = c(0.01));
 set[[2]] =  list(avec = c(0.50, 0.50), mvec = c(0.67, 0.33), svec = c(0.01, 0.01));
 exset <- setconvert(set, tau = 0.01, error = 0.001)

Simulate Allele Counts for Single Individual - Beta-Binomial Distribution

Description

This function is used to simulate coverage of each allele at biallelic heterozygous sites assuming a beta binomial distribution. Here we sample sequence depth from a truncated poisson distribution between a set minimum, maximum, and lambda. Only heterozygous sites are returned. Based on input variables, the sites may be filtered based on the total coverage (filter.coverage), allele sequencing coverage (filter.error), or allele frequency (filter.freq).

Usage

sim.ind.BB(
  mvec,
  avec,
  svec,
  error = 0.001,
  s.size = 50000,
  lambda = 11,
  max.coverage = 20,
  min.coverage = 2,
  filter.coverage = TRUE,
  max.depth.quantile.prob = 0.9,
  filter.error = TRUE,
  filter.freq = FALSE,
  trunc = c(0, 0),
  sampled = TRUE
)

Arguments

mvec

Vector of mean values of allele frequency.

avec

Vector of alpha values representing the proportion expected of each mean.

svec

Vector of variance values.

error

Sequencing error rate. Default as 0.001, or very low error.

s.size

Number of biallelic sites to generate. Defaults to 50000. Warning, the number of sites generated will not be the number of sites returned due to filtering steps.

lambda

Set lambda for the truncated poisson distrubtion. Defaults to 11.

max.coverage

Maximum sequencing depth per site. Defaults to 20.

min.coverage

Minimum sequencing depth per site. Defaults to 2.

filter.coverage

Default as TRUE. Filters to only retain sites where total sequencing depth is greater than the provided minimum coverage and less than the max quantile depth (set with the max.depth.quantile.prob).

max.depth.quantile.prob

Maximum depth quantile probability. Defaults to 0.9.

filter.error

Default as TRUE. Filter to only retain sites where allele coverage is greater than the sequencing error rate times the total coverage, but less than one minus the sequencing error rate times the total coverage.

filter.freq

Default as FALSE. When set to true, sites are filtered based on provided trunc.

trunc

List of two values representing the lower and upper bounds,c_{L} and c_{U}. Defaults as c(0,0) to represent no truncation.

sampled

Default as TRUE. Will randomly sample allele A or allele B, then return a data frame with total coverage and coverage of a randomly sampled allele will be returned.

Value

If sampled = FALSE, a data frame with total coverage, coverage of allele A, and coverage of allele B will be returned. If sampled = TRUE, a data frame with total coverage and coverage of a randomly sampled allele will be returned.

Examples

xm <- sim.ind.BB(mvec = c(0.5), avec = c(1), svec=c(0.01), s.size = 100)

Simulate Allele Counts for Single Individual - Beta-Binomial Distribution with Overdispersion and Error

Description

This function is used to simulate the frequency of biallelic heterozygous sites assuming a beta-binomial distribution. Here we sample sequence depth from a truncated poisson distribution between a set minimum, maximum, and lambda. Only heterozygous sites are returned. Based on input variables, the sites may be filtered based on the total coverage (filter.coverage), allele sequencing coverage (filter.error), or allele frequency (filter.freq).

Usage

sim.ind.BB.tau(
  mvec,
  avec,
  tau = 0.01,
  error = 0.001,
  s.size = 50000,
  lambda = 11,
  max.coverage = 20,
  min.coverage = 2,
  filter.coverage = TRUE,
  max.depth.quantile.prob = 0.9,
  filter.error = TRUE,
  filter.freq = FALSE,
  trunc = c(0, 0),
  sampled = TRUE
)

Arguments

mvec

Vector of mean values of allele frequency.

avec

Vector of alpha values representing the proportion expected of each mean.

tau

Overdispersion parameter. Defaults to 0.01.

error

Sequencing error rate. Defaults to 0.001.

s.size

Number of biallelic sites to generate. Defaults to 50000. Warning, the number of sites generated will not be the number of sites returned due to filtering steps.

lambda

Set lambda for the truncated poisson distrubtion. Defaults to 11.

max.coverage

Maximum sequencing depth per site. Defaults to 20.

min.coverage

Minimum sequencing depth per site. Defaults to 2.

filter.coverage

Default as TRUE. Filters to only retain sites where total sequencing depth is greater than the provided minimum coverage and less than the max quantile depth (set with the max.depth.quantile.prob).

max.depth.quantile.prob

Maximum depth quantile probability. Defaults to 0.9.

filter.error

filter.freq

Default as FALSE. When set to true, sites are filtered based on provided trunc.

trunc

List of two values representing the lower and upper bounds, c_{L} and c_{U}. Defaults as c(0,0) to represent no truncation.

sampled

Default as TRUE. Will randomly sample allele A or allele B, then return a data frame with total coverage and coverage of a randomly sampled allele will be returned.

Value

Examples

xm <- sim.ind.BB.tau(mvec = c(0.5), avec = c(1), s.size = 100)

Simulate Allele Counts for Single Individual - Simple Approach

Description

This function is used to simulate coverage of each allele at biallelic heterozygous sites assuming a binomial distribution.

Usage

sim.ind.simple(mvec, cover = 100, s.size = 50000, sampled = TRUE)

Arguments

mvec

Vector of means.

cover

Coverage of sites.

s.size

Number of biallelic sites to generate. Defaults to 50000. Warning, the number of sites generated will not be the number of sites returned due to filtering steps.

sampled

Default as TRUE. Will randomly sample allele A or allele B, then return a data frame with total coverage and coverage of a randomly sampled allele will be returned.

Value

Examples

xm <- sim.ind.simple(mvec = c(0.5), s.size = 100)

Simulated data for examples xm <- sim.ind.simple(mvec = c(0.5))

Description

Simulated data for examples xm <- sim.ind.simple(mvec = c(0.5))

Usage

xm

Format

A data frame with 2 columns and 50000 rows

Package {nQuack}

nQuack: Predicting Ploidal Level from Sequence Data

Description

Author(s)

See Also

Remove Noise with the Beta Distribution

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Setup Basic Example

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Calculate Alpha and Beta from Mean and Variance

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Calculate Alpha and Beta from Mean, Tau, and Error rate.

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Vector-based - Calculate Alpha and Beta from Mean, Tau, and Error rate.

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Vector-based - Calculate Alpha and Beta from Mean and Variance

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Model Selection - Expectation Maximization - Optimal Distribution and Type

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Denoise Data

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Expectation maximization - Beta Distribution

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Expectation maximization - Beta + Beta + Beta Distribution

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Examples

Expectation maximization - Beta-Binomial Distribution

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Expectation maximization - Beta-Binomial and Uniform Distributions

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Examples

Expectation maximization - Beta and Uniform Distributions

Description

Usage