Package 'DNAtools' reference manual

Title:	Tools for Analysing Forensic Genetic DNA Data
Description:	Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.
Authors:	Torben Tvedebrink [aut], James Curran [aut], Mikkel Meyer Andersen [aut, cre]
Maintainer:	Mikkel Meyer Andersen <[email protected]>
License:	GPL (>= 2) \| file LICENSE
Version:	0.2-4.9001
Built:	2024-09-08 03:27:21 UTC
Source:	https://github.com/mikldk/dnatools

Tools for analysing forensic genetic DNA databases

Description

Computational efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F-designation) is implemented. Dedicated functions ease plotting the results.

Details

Package:	DNAtools
Type:	Package
Version:	0.1
Date:	2014-08-25
License:	GPL (>= 2)

dbCompare: Compares make all n(n-1)/2 pairwise comparisons between profiles of a database with n DNA profiles. dbExpect: Computes the expected number of matching and partial matching loci for a given number of profiles in a database. dbVariance: Calculates the associated covariance matrix.

Author(s)

Torben Tvedebrink <[email protected]>, James Curran <[email protected]> and Mikkel Meyer Andersen <[email protected]>.

References

Tvedebrink T, JM Curran, PS Eriksen, HS Mogensen and N Morling (2012). Analysis of matches and partial-matches in a Danish STR data set. Forensic Science International: Genetics, 6(3): 387-392.

Read the vignette: vigette('DNAtools')

Examples


  ## Not run: 
  data(dbExample)
  dbCompare(dbExample,hit=5,trace=TRUE)
  
## End(Not run)
## Not run: 
  data(dbExample)
  dbCompare(dbExample,hit=5,trace=TRUE)
  
## End(Not run)

Collapse m/p output to vector

Description

Collapse a m/p-matrix from dbCompare/dbExpect to a vector.

Usage

dbCollapse(x)
dbCollapse(x)

Arguments

`x`	Either a object of class 'dbcompare' (result from dbCompare) or 'matrix'.

Details

Collapse a m/p-matrix from dbCompare/dbExpect to a vector with entry i being the sum of all entries from m/p-matrix satisfying 2*m+p=i.

Value

A vector of length 2*max(m)+1 with entries begin the sum of entries i in m/p-matrix satisfying i=2*m+p.

Author(s)

Torben Tvedebrink

Examples


  ## Not run: 
  data(dbExample)
  res <- dbCompare(dbExample, hit=5, trace=TRUE)
  dbCollapse(res) ## same as dbCompare(dbExample, hit=5, trace=TRUE, collapse=TRUE)
  
## End(Not run)

## Not run: 
  data(dbExample)
  res <- dbCompare(dbExample, hit=5, trace=TRUE)
  dbCollapse(res) ## same as dbCompare(dbExample, hit=5, trace=TRUE, collapse=TRUE)
  
## End(Not run)

Compare DNA profiles

Description

Compare DNA profiles

Usage

dbCompare(
  x,
  profiles = NULL,
  hit = 7,
  trace = TRUE,
  vector = FALSE,
  collapse = FALSE,
  wildcard = FALSE,
  wildcard.effect = FALSE,
  wildcard.impose = FALSE,
  Rallele = FALSE,
  threads = 2
)
dbCompare(
  x,
  profiles = NULL,
  hit = 7,
  trace = TRUE,
  vector = FALSE,
  collapse = FALSE,
  wildcard = FALSE,
  wildcard.effect = FALSE,
  wildcard.impose = FALSE,
  Rallele = FALSE,
  threads = 2
)

Arguments

`x`	Database with DNA profiles. The database format is expected to be a data frame with each column containing an allelic number such that for each DNA marker there are two columns in the data frame. See `data(dbExample)` for an example of the format.
`profiles`	One or more profiles to be compared with all profiles in the database. Input is a vector, matrix or data frame of same length/width as a row in the database `x`. If profiles is non-null only one CPU will be used. In case threads>1 a warning will be given but computations performed using single core.
`hit`	The number of matching loci for further investigation
`trace`	Shows a progress bar
`vector`	Logical. Whether the result should be returned as vector or a matrix. Note if 'collapse' is TRUE vector is ignored.
`collapse`	Logical (default FALSE). If TRUE the (m,p)-matrix will be collapased into a (2*m+p)-vector containing the total number of matching alleles.
`wildcard`	Use the wildcard comparing.
`wildcard.effect`	Compare result of wildcard and no wildcard.
`wildcard.impose`	Force homozygouse profiles (aa) to have wildcard (aF).
`Rallele`	Implementation of 'Rare allele'designation matching.
`threads`	The number of threads to use for performing comparisons in parallel for increased computation time. Use 0 for using the same number as the computer has CPU cores. NOTE: Only available on Linux and MacOS operating systems.

Details

Computes the distance between DNA profiles in terms of matching and partially-matching STR loci.

Value

Returns a matrix with the number of pairs mathcing/partially-matching at (i,j)-loci.

Author(s)

James Curran and Torben Tvedebrink. The multicore/CPU implementation was provided by Mikkel Meyer Andersen.

Examples


  ## Not run: 
  data(dbExample)
  dbCompare(dbExample,hit=5,trace=TRUE)
  
## End(Not run)

## Not run: 
  data(dbExample)
  dbCompare(dbExample,hit=5,trace=TRUE)
  
## End(Not run)

Simulated database with 1,000 individuals

Description

Database containing 1,000 simulated DNA profiles typed on ten autosomal markers.

Format

A data frame with each row being a DNA profile and each column a part of a genetic marker. Note that homozygote profiles has the same allelic value in the two columns associated to the same marker.

Expected value of cell counts in DNA database comparison

Description

Computes the expected number of cell counts when comparing DNA profiles in a DNA database. For every pair of DNA profiles in a database the number of matching and partial matching loci is recorded. A match is declared if the two DNA profiles coincide for both alleles in a locus and a partial-match is recorded if only one allele is shared between the profiles. With a total of L loci the number of matching loci is 0,...,L and partial number of matches is 0,...,L-m, where m is the number of matching loci.

Usage

dbExpect(
  probs,
  theta = 0,
  k = c(0, 0, 1),
  n = 1,
  r = 0,
  R = 0,
  round = FALSE,
  na = TRUE,
  vector = FALSE,
  collapse = FALSE,
  wildcard = FALSE,
  no.wildcard = NULL,
  rare.allele = FALSE,
  no.rare.allele = NULL
)
dbExpect(
  probs,
  theta = 0,
  k = c(0, 0, 1),
  n = 1,
  r = 0,
  R = 0,
  round = FALSE,
  na = TRUE,
  vector = FALSE,
  collapse = FALSE,
  wildcard = FALSE,
  no.wildcard = NULL,
  rare.allele = FALSE,
  no.rare.allele = NULL
)

Arguments

`probs`	List of vectors with allele probabilities for each locus
`theta`	The coancestery coefficient
`k`	The vector of identical-by-descent probabilities, k=(k2,k1,k0), where for full-siblings k=c(1,2,1)/4. The default is k=c(0,0,1) refering to unrelated individuals.
`n`	Number of DNA profiles in the database
`r`	The probability assigned to the rare alleles (see rare allele matching). If a vector must be of same length as `probs`.
`R`	The probability assigned to alleles shorter or longer than allelic ladder (see rare allele matching). If a vector must be of length 1 or 2, and if a list it must be same length as `probs`.
`round`	Whether or not the results should be rounded or not
`na`	Whether or not the off-elements should be returned as 0 or NA
`vector`	Whether or not the result should be returned as a matrix or vector. Note if 'collapse' is TRUE vector is ignored.
`collapse`	Logical (default FALSE). If TRUE the (m,p)-matrix will be collapased into a (2*m+p)-vector containing the total number of matching alleles.
`wildcard`	Should wildcards be used?
`no.wildcard`	Should 'w' wildcards be used?
`rare.allele`	Should rare allele matching be used?
`no.rare.allele`	Should 'r' rare allele loci be used?

Details

Computes the expected cell counts using a recursion formula. See Tvedebrink et al (2011) for details.

Value

Returns a matrix (or vector, see above) of expected cell counts.

Author(s)

James Curran and Torben Tvedebrink

References

T Tvedebrink, PS Eriksen, J Curran, HS Mogensen, N Morling. 'Analysis of matches and partial-matches in Danish DNA reference profile database'. Forensic Science International: Genetics, 2011.

Examples


  ## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Compute the expected number for a DB with 10000 profiles:
  dbExpect(freqs,theta=0,n=10000)
  
## End(Not run) 

## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Compute the expected number for a DB with 10000 profiles:
  dbExpect(freqs,theta=0,n=10000)
  
## End(Not run)

Simulate a DNA database

Description

Simulates a DNA database given a set of allele probabilities and theta value. It is possible to have close relatives in the database simulated in pairs, such that within each pair the profiles are higher correlated due to close familial relationship, but between pairs of profiles the correlation is only modelled by theta.

Usage

dbSimulate(probs, theta = 0, n = 1000, relatives = NULL)
dbSimulate(probs, theta = 0, n = 1000, relatives = NULL)

Arguments

`probs`	List of allele probabilities, where each element in the list is a vector of allele probabilities.
`theta`	The coancestry coefficient
`n`	The number of profiles in the database
`relatives`	A vector of length 4. Determining the number of PAIRS of profiles in the database: (FULL-SIBLINGS, FIRST-COUSINS, PARENT-CHILD, AVUNCULAR). They should obey that 2*sum(relatives)<=n.

Details

Simulates a DNA database with a given number of DNA profiles (and possibly relatives) with a correlation between profiles governed by theta.

Value

A data frame where each row represents a DNA profile. The first column is a profile identifier (id) and the next 2*L columns contains the simulated genotype for each of the L loci. L is determined by the length of the list 'probs' with allele probabilities

Author(s)

James Curran and Torben Tvedebrink

Examples


  ## Not run: 
  ## Simulate some allele frequencies:
                                                                                
  freq <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
             simplify=FALSE)
  ## Simulate a single database with 5000 DNA profiles:
  simdb <- dbSimulate(freq,theta=0,n=5000)
  ## Simulate a number of databases, say N=50. For each database compute
  ## the summary statistic using dbCompare:
  N <- 50
  Msummary <- matrix(0,N,(length(freq)+1)*(length(freq)+2)/2)
  for(i in 1:N)
    Msummary[i,] <- dbCompare(dbSimulate(freq,theta=0,n=1000),
                      vector=TRUE,trace=FALSE)$m
  ## Give the columns  representative names:
  dimnames(Msummary)[[2]] <- DNAtools:::dbCats(length(freq),vector=TRUE)
  ## Plot the simulations using a boxplot
  boxplot(log10(Msummary))
  ## There might come some warnings due to taking log10 to zero-values (no counts)
  ## Add the expected number to the plot:
  points(1:ncol(Msummary),log10(dbExpect(freq,theta=0,n=1000,vector=TRUE)),
         col=2,pch=16)
  
## End(Not run)

## Not run: 
  ## Simulate some allele frequencies:
                                                                                
  freq <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
             simplify=FALSE)
  ## Simulate a single database with 5000 DNA profiles:
  simdb <- dbSimulate(freq,theta=0,n=5000)
  ## Simulate a number of databases, say N=50. For each database compute
  ## the summary statistic using dbCompare:
  N <- 50
  Msummary <- matrix(0,N,(length(freq)+1)*(length(freq)+2)/2)
  for(i in 1:N)
    Msummary[i,] <- dbCompare(dbSimulate(freq,theta=0,n=1000),
                      vector=TRUE,trace=FALSE)$m
  ## Give the columns  representative names:
  dimnames(Msummary)[[2]] <- DNAtools:::dbCats(length(freq),vector=TRUE)
  ## Plot the simulations using a boxplot
  boxplot(log10(Msummary))
  ## There might come some warnings due to taking log10 to zero-values (no counts)
  ## Add the expected number to the plot:
  points(1:ncol(Msummary),log10(dbExpect(freq,theta=0,n=1000,vector=TRUE)),
         col=2,pch=16)
  
## End(Not run)

Covariance matrix of cell counts in DNA database comparison

Description

Computes the covariance matrix for the cell counts when comparing DNA profiles in a DNA database. For every pair of DNA profiles in a database the number of matching and partial matching loci is recorded. A match is declared if the two DNA profiles coincide for both alleles in a locus and a partial-match is recorded if only one allele is shared between the profiles. With a total of L loci the number of matching loci is 0,...,L and partial number of matches is 0,...,L-m, where m is the number of matching loci. The expression is given by:

$latex$

Usage

dbVariance(probs, theta = 0, n = 1, collapse = FALSE)
dbVariance(probs, theta = 0, n = 1, collapse = FALSE)

Arguments

`probs`	List of vectors with allele probabilities for each locus
`theta`	The coancestery coefficient. If a vector of different theta values are supplied a list of covariance matrices is returned. Note it is faster to give a vector of theta values as argument than calculating each matrix at the time.
`n`	Number of DNA profiles in the database. If n=1 is supplied a list of the components for computing the variance is returned. That is, the variance and two covariances on the right hand side of the equation above.
`collapse`	Logical, default FALSE. If TRUE the covariance matrix is collapsed such that it relates to (2*m+p)-vectors of total number of matching alleles rather than (m,p)-matrix.

Details

Computes the covariance matrix of the cell counts using a recursion formula. See Tvedebrink et al (2011) for details.

Value

Returns a covariance matrix for the cell counts.

Author(s)

James Curran and Torben Tvedebrink

References

T Tvedebrink, PS Eriksen, J Curran, HS Mogensen, N Morling. 'Analysis of matches and partial-matches in Danish DNA reference profile database'. Forensic Science International: Genetics, 2011.

Examples


  ## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)}, simplify=FALSE)
  ## List of elements needed to compute the covariance matrix.
  ## Useful option when the covariance needs to be computed for varying
  ## database sizes but for identical theta-value.
  comps <- dbVariance(freqs,theta=0,n=1)
  ## Covariance for a DB with 1000 DNA profiles
  cov1000 <- dbVariance(freqs,theta=0,n=1000)
  ## The result is the same as:
  comps1000 <- choose(1000,2)*comps$V1 + 6*choose(1000,3)*comps$V2 + 6*choose(1000,4)*comps$V3
  
## End(Not run)

## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)}, simplify=FALSE)
  ## List of elements needed to compute the covariance matrix.
  ## Useful option when the covariance needs to be computed for varying
  ## database sizes but for identical theta-value.
  comps <- dbVariance(freqs,theta=0,n=1)
  ## Covariance for a DB with 1000 DNA profiles
  cov1000 <- dbVariance(freqs,theta=0,n=1000)
  ## The result is the same as:
  comps1000 <- choose(1000,2)*comps$V1 + 6*choose(1000,3)*comps$V2 + 6*choose(1000,4)*comps$V3
  
## End(Not run)

Estimate the drop-out probability based on number of alleles

Description

An inferior may to estimate the drop-out probability compared to using the peak heights from the electropherogram. However, to compare the performance with Gill et al. (2007) this implements a theoretical approach based on their line of arguments.

Usage

estimatePD(n0, m, pnoa = NULL, probs = NULL, theta = 0, locuswise = FALSE)
estimatePD(n0, m, pnoa = NULL, probs = NULL, theta = 0, locuswise = FALSE)

Arguments

`n0`	Vector of observed allele counts - same length as the number of loci
`m`	The number of contributors
`pnoa`	The vector of $\P(N(m)=n)$ for $n=1,\ldots,2Lm$ , where $L$ is the number of loci and $m$ is the number of contributors OR
`probs`	List of vectors with allele probabilities for each locus
`theta`	The coancestery coefficient
`locuswise`	Logical. Indicating whether computations should be done locuswise.

Details

Computes the $\Pr(D)$ that maximises equation (10) in Tvedebrink (2014).

Value

Returns the MLE of $\Pr(D)$ based on equation (10) in Tvedebrink (2014)

Author(s)

Torben Tvedebrink

References

Gill, P., A. Kirkham, and J. Curran (2007). LoComatioN: A software tool for the analysis of low copy number DNA profiles. Forensic Science International 166(2-3): 128 - 138.

T. Tvedebrink (2014). 'On the exact distribution of the number of alleles in DNA mixtures', International Journal of Legal Medicine; 128(3):427–37. <https://doi.org/10.1007/s00414-013-0951-3>

Examples


  ## Simulate some allele frequencies:
  freqs <-  simAlleleFreqs()
  ## Assume 15 alleles are observed in a 2-person DNA mixture with 10 loci:
  estimatePD(n0 = 15, m = 2, probs = freqs)

## Simulate some allele frequencies:
  freqs <-  simAlleleFreqs()
  ## Assume 15 alleles are observed in a 2-person DNA mixture with 10 loci:
  estimatePD(n0 = 15, m = 2, probs = freqs)

Simple allele frequency estimation

Description

Estimates allele frequencies from a database with DNA profiles

Usage

freqEst(x)
freqEst(x)

Arguments

`x`	A database of the form ['id','locus1 allele1','locus1 allele2',...,'locusN allele 1','locusN allele2'].

Details

Computes the allele frequencies for a given database.

Value

Returns a list of probability vectors - one vector for each locus.

Author(s)

James Curran and Torben Tvedebrink

Examples


  data(dbExample)
  freqEst(dbExample)

data(dbExample)
  freqEst(dbExample)

Generates DNA profiles of n individuals.

Description

These are formed as n/2 pairs for relatives with a IDB-vector given by k. I.e. the profiles are mutually unrelated between pairs.

Usage

genRypeRec(x, t, k, n, print = FALSE)
genRypeRec(x, t, k, n, print = FALSE)

Arguments

`x`	Allele probabilities
`t`	theta correction
`k`	Relatedness vector
`n`	Number of probles
`print`	Print information

Generates DNA profiles of n unrelated individuals for a locus

Description

Generates DNA profiles of n unrelated individuals for a locus

Usage

genTypeRec(x, t, n, z = rep(0, lx <- length(x)))
genTypeRec(x, t, n, z = rep(0, lx <- length(x)))

Arguments

`x`	Allele probabilities
`t`	theta correction
`n`	Number of probles
`z`	FIXME

Estimate theta and the fraction of comparisons between close relatives

Description

Estimates the fraction of comparisons between pairs of close relatives while fitting the theta parameter minimising the object function. The function makes use of the R-package 'Rsolnp' which is an implementation of an solver for non-linear minimisation problems with parameter constraints.

Usage

optim.relatedness(
  obs,
  theta0 = 0,
  theta1 = 0.03,
  theta.tol = 10^(-7),
  theta.step = NULL,
  max.bisect = 15,
  probs,
  var.list = NULL,
  init.alpha = 10^c(-4, -6, -8, -10),
  init.keep = FALSE,
  objFunction = c("T2", "T1", "C3", "C2", "C1"),
  collapse = FALSE,
  trace = FALSE,
  solnp.ctrl = list(tol = 10^(-9), rho = 10, delta = min(init.alpha) * 0.01, trace =
    FALSE)
)
optim.relatedness(
  obs,
  theta0 = 0,
  theta1 = 0.03,
  theta.tol = 10^(-7),
  theta.step = NULL,
  max.bisect = 15,
  probs,
  var.list = NULL,
  init.alpha = 10^c(-4, -6, -8, -10),
  init.keep = FALSE,
  objFunction = c("T2", "T1", "C3", "C2", "C1"),
  collapse = FALSE,
  trace = FALSE,
  solnp.ctrl = list(tol = 10^(-9), rho = 10, delta = min(init.alpha) * 0.01, trace =
    FALSE)
)

Arguments

`obs`	The matrix or vector of observed matches/partial-matches as returned by the dbCompare()-function
`theta0`	The left value of the interval in which a bisection-like search is performed for theta
`theta1`	Right value of interval (see theta0)
`theta.tol`	A stopping criterion for the search. If the search narrows within theta.tol the function terminates
`theta.step`	Default is NULL. If not a grid search will be performed on seq(from = theta0, to = theta1, by = theta.step)
`max.bisect`	The maximum number of bisectional iterations perform prior to termination
`probs`	List of vectors with allele probabilities for each locus
`var.list`	A named list of components for computing variances, see dbVariance. The names of the elements are the associated theta-values, and each component is a list of (V1,V2,V3) - see dbVariance with n=1
`init.alpha`	Initial values for alpha, where the order is (First-cousins, Avuncular, Parent-child, Full-siblings). The value for Unrelated is computed as 1-sum(init.alpha)
`init.keep`	Whether the initial values should be used in successive steps for the current optimum should be used.
`objFunction`	Which of the five different object functions should be used to compare observed and expected
`collapse`	Not yet implemented
`trace`	Should iteration steps and other process indicators be printed
`solnp.ctrl`	See solnp for details

Details

Computes the proportion of comparisons between close relatives in a database matching exercise for each theta value under investigation.

Value

Returns a list of three components: value, solution and var.list. The first element, value, is a dataframe with the value of the objection function for each of the theta values investigated. Solution is the estimated alpha-vector where the objection function was minimised. Finally, var.list is a names list of components for computing variances. May be reused in later computations for increased speed in some iterations.

Author(s)

James Curran and Torben Tvedebrink

References

T Tvedebrink, PS Eriksen, J Curran, HS Mogensen, N Morling. 'Analysis of matches and partial-matches in Danish DNA reference profile database'. Forensic Science International: Genetics, 2011.

Examples


  ## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Load the sample database:
  data(dbExample)
  obs <- dbCompare(dbExample,trace=FALSE)$m
  C3 <- optim.relatedness(obs,theta0=0.0,theta1=0.03,probs=freqs,
          objFunction='C3',max.bisect=30,trace=TRUE)
  
## End(Not run)

## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Load the sample database:
  data(dbExample)
  obs <- dbCompare(dbExample,trace=FALSE)$m
  C3 <- optim.relatedness(obs,theta0=0.0,theta1=0.03,probs=freqs,
          objFunction='C3',max.bisect=30,trace=TRUE)
  
## End(Not run)

Compute the posterior probabilities for P(m|n0) for a given prior P(m) and observed vector n0 of locus counts

Description

where m ranges from 1 to $m_{\max}$ and $n_0$ is the observed locus counts.

Usage

pContrib(n0, probs = NULL, m.prior = rep(1/m.max, m.max), m.max = 8, theta = 0)
pContrib(n0, probs = NULL, m.prior = rep(1/m.max, m.max), m.max = 8, theta = 0)

Arguments

`n0`	Vector of observed allele counts - same length as the number of loci.
`probs`	List of vectors with allele probabilities for each locus
`m.prior`	A vector with prior probabilities (summing to 1), where the length of `m.prior` determines the plausible range of m
`m.max`	Derived from the length of `m.prior`, and if `m.prior=NULL` a uniform prior is speficied by `m.max`: `m.prior = rep(1/m.max,m.max)`.
`theta`	The coancestery coefficient

Details

Computes a vector P(m|n0) evaluated over the plausible range 1,...,m.max.

Value

Returns a vector P(m|n0) for m=1,...,m.max

Author(s)

Torben Tvedebrink, James Curran

References

T. Tvedebrink (2014). 'On the exact distribution of the number of alleles in DNA mixtures', International Journal of Legal Medicine; 128(3):427–37. <https://doi.org/10.1007/s00414-013-0951-3>

Examples


  ## Simulate some allele frequencies:
  freqs <-  simAlleleFreqs()
  m <- 2
  n0 <- sapply(freqs, function(px){
                            peaks = unique(sample(length(px),
                                             size = 2 * m,
                                             replace = TRUE,
                                             prob = px))
                            return(length(peaks))
                       })
  ## Compute P(m|n0) for m=1,...,4 and the sampled n0
  pContrib(n0=n0,probs=freqs,m.max=4)

## Simulate some allele frequencies:
  freqs <-  simAlleleFreqs()
  m <- 2
  n0 <- sapply(freqs, function(px){
                            peaks = unique(sample(length(px),
                                             size = 2 * m,
                                             replace = TRUE,
                                             prob = px))
                            return(length(peaks))
                       })
  ## Compute P(m|n0) for m=1,...,4 and the sampled n0
  pContrib(n0=n0,probs=freqs,m.max=4)

Compute the posterior probabilities for $\Pr(m|n_0)$ for a given prior $\Pr(m)$ .

Description

Compute a matrix of posterior probabilties $\Pr(m|n_0)$ where $m$ ranges from 1 to $m_{\max}$ , and $n_0$ is $0,\ldots,2m_{\max}$ . This is done by evaluating $\Pr(m|n_0)=Pr(n_0|m)Pr(m)/Pr(n)$ , where $\Pr(n_0|m)$ is evaluated by pNoA.

Usage

pContrib_locus(
  prob = NULL,
  m.prior = NULL,
  m.max = 8,
  pnoa.locus = NULL,
  theta = 0
)
pContrib_locus(
  prob = NULL,
  m.prior = NULL,
  m.max = 8,
  pnoa.locus = NULL,
  theta = 0
)

Arguments

`prob`	Vectors with allele probabilities for the specific locus
`m.prior`	A vector with prior probabilities (summing to 1), where the length of `m.prior` determines the plausible range of $m$
`m.max`	Derived from the length of `m.prior`, and if `m.prior=NULL` a uniform prior is speficied by `m.max`: `m.prior = rep(1/m.max,m.max)`.
`pnoa.locus`	A named vector of locus specific probabilities $P(N(m)=n), n=1,\ldots,2m$ .
`theta`	The coancestery coefficient

Details

Computes a matrix of $\Pr(m|n_0)$ values for a specific locus.

Value

Returns a matrix $[\Pr(m|n_0)]$ for $m = 1,\ldots,m.max$ and $n_0 = 1,\ldots,2m.max$ .

Author(s)

Torben Tvedebrink, James Curran

References

T. Tvedebrink (2014). 'On the exact distribution of the number of alleles in DNA mixtures', International Journal of Legal Medicine; 128(3):427–37. <https://doi.org/10.1007/s00414-013-0951-3>

Examples


  ## Simulate some allele frequencies:
  freqs <-  simAlleleFreqs()
  
  ## Compute Pr(m|n0) for m = 1, ..., 5 and n0 = 1, ..., 10 for the first locus:
  pContrib_locus(prob = freqs[[1]], m.max = 5)

## Simulate some allele frequencies:
  freqs <-  simAlleleFreqs()
  
  ## Compute Pr(m|n0) for m = 1, ..., 5 and n0 = 1, ..., 10 for the first locus:
  pContrib_locus(prob = freqs[[1]], m.max = 5)

Plots the summary matrix

Description

Plots the summary matrix with counts on y-axis and classification on x-axis.

Usage

## S3 method for class 'dbcompare'
plot(x, log = "y", las = 3, xlab = "Match/Partial", ylab = "Counts", ...)
## S3 method for class 'dbcompare'
plot(x, log = "y", las = 3, xlab = "Match/Partial", ylab = "Counts", ...)

Arguments

`x`	Summary matrix returned from dbcompare
`log`	Specifies whether log(Counts) should be plotted (default)
`las`	Direction of the labels on x-axis. Default is 3 which gives perpendicular labels
`xlab`	Axis label
`ylab`	Axis label
`...`	Other plot options

Value

A plot of the summary matrix. The counts are on log10 scale and the x-axis is labeled by appropriate matching/partially-matching levels.

Author(s)

James Curran and Torben Tvedebrink

Examples


  ## Not run: 
  data(dbExample)
  M = dbCompare(dbExample,hit=5)
  plot(M)
  
## End(Not run)

## Not run: 
  data(dbExample)
  M = dbCompare(dbExample,hit=5)
  plot(M)
  
## End(Not run)

Plots the fitted object function for estimated familial relationships in the database and theta.

Description

Plots the minimised object function for included values of theta

Usage

## S3 method for class 'dbOptim'
plot(x, type = "l", ...)
## S3 method for class 'dbOptim'
plot(x, type = "l", ...)

Arguments

`x`	Object returned by optim.relatedness
`type`	The type of plot character ('l'=line, 'p'=points, ...), see 'par' for more details
`...`	Other plot options

Details

Plots the object function

Value

A plot of the object function

Author(s)

James Curran and Torben Tvedebrink

Examples


  ## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Load the sample database:
  data(dbExample)
  obs <- dbCompare(dbExample,trace=FALSE)$m
  C3 <- optim.relatedness(obs,theta0=0.0,theta1=0.03,probs=freqs,
          objFunction='C3',max.bisect=30,trace=TRUE)
  plot(C3)
  
## End(Not run)

## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Load the sample database:
  data(dbExample)
  obs <- dbCompare(dbExample,trace=FALSE)$m
  C3 <- optim.relatedness(obs,theta0=0.0,theta1=0.03,probs=freqs,
          objFunction='C3',max.bisect=30,trace=TRUE)
  plot(C3)
  
## End(Not run)

The exact distribution of the number of alleles in a m-person DNA mixture

Description

Computes the exact distribution of the number of alleles in a $m$ -person DNA mixture typed with STR loci. For a m-person DNA mixture it is possible to observe $1,\ldots,2\times m \times L$ alleles, where $L$ is the total number of typed STR loci. The method allows incorporation of the subpopulation correction, the so-called $\theta$ -correction, to adjust for shared ancestry. If needed, the locus-specific probabilities can be obtained using the locuswise argument.

Usage

Pnm_all(m, theta, probs, locuswise = FALSE)
Pnm_locus(m, theta, alleleProbs)
Pnm_all(m, theta, probs, locuswise = FALSE)
Pnm_locus(m, theta, alleleProbs)

Arguments

`m`	The number of contributors
`theta`	The coancestery coefficient
`probs`	List of vectors with allele probabilities for each locus
`locuswise`	Logical. If `TRUE` the locus-wise probabilities will be returned. Otherwise, the probability over all loci is returned.
`alleleProbs`	Vectors with allele probabilities

Details

Computes the exact distribution of the number of alleles for a m-person DNA mixture.

Value

Returns a vector of probabilities, or a matrix of locuswise probability vectors.

Author(s)

Torben Tvedebrink, James Curran, Mikkel Andersen

References

T. Tvedebrink (2014). 'On the exact distribution of the number of alleles in DNA mixtures', International Journal of Legal Medicine; 128(3):427–37. <https://doi.org/10.1007/s00414-013-0951-3>

Examples


  ## Simulate some allele frequencies:
  freqs <-  structure(replicate(10, { g = rgamma(n = 10, scale = 4, shape = 3); 
                                      g/sum(g)
                                    },
              simplify = FALSE), .Names = paste('locus', 1:10, sep = '.'))

  ## Compute \eqn{\Pr(N(m = 3) = n)}, \eqn{n = 1,\ldots,2 * L *m}, where \eqn{L = 10}
  ## here
  Pnm_all(m = 2, theta = 0, freqs)
  ## Same, but locuswise results
  Pnm_all(m = 2, theta = 0, freqs, locuswise = TRUE)
  
## Simulate some allele frequencies:
  freqs <-  structure(replicate(10, { g = rgamma(n = 10, scale = 4, shape = 3); 
                                      g/sum(g)
                                    },
              simplify = FALSE), .Names = paste('locus', 1:10, sep = '.'))

  ## Compute \eqn{\Pr(N(m = 3) = n)}, \eqn{n = 1,\ldots,2 * L *m}, where \eqn{L = 10}
  ## here
  Pnm_all(m = 2, theta = 0, freqs)
  ## Same, but locuswise results
  Pnm_all(m = 2, theta = 0, freqs, locuswise = TRUE)

Prints the summary matrix

Description

Prints the summary matrix and possible 'big hits'.

Usage

## S3 method for class 'dbcompare'
print(x, ...)
## S3 method for class 'dbcompare'
print(x, ...)

Arguments

`x`	Summary matrix returned from dbcompare
`...`	...

Details

Prints the summary matrix

Value

Prints the summary matrix and data frame with 'big hits'

Author(s)

James Curran and Torben Tvedebrink

Examples


  ## Not run: 
  data(dbExample)
  M = dbCompare(dbExample,hit=5)
  M
  
## End(Not run)

## Not run: 
  data(dbExample)
  M = dbCompare(dbExample,hit=5)
  M
  
## End(Not run)

Prints the results from optim.relatedness()

Description

Prints the evaluated functions for the object function, best estimate of alpha and possibly list of variances.

Usage

## S3 method for class 'dbOptim'
print(x, var.list = FALSE, ...)
## S3 method for class 'dbOptim'
print(x, var.list = FALSE, ...)

Arguments

`x`	Object returned by optim.relatedness()
`var.list`	Logical. Whether the (long) list of variance components should be printed to the screen.
`...`	...

Details

Prints the summary details of the fit

Value

A dataframe with [theta,value] and a vector of fitted alpha parameters

Author(s)

James Curran and Torben Tvedebrink

Examples


  ## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Load the sample database:
  data(dbExample)
  obs <- dbCompare(dbExample,trace=FALSE)$m
  C3 <- optim.relatedness(obs,theta0=0.0,theta1=0.03,probs=freqs,
          objFunction='C3',max.bisect=30,trace=TRUE)
  print(C3)
  
## End(Not run)
## Not run: 
  ## Simulate some allele frequencies:
  freqs <-  replicate(10, { g = rgamma(n=10,scale=4,shape=3); g/sum(g)},
              simplify=FALSE)
  ## Load the sample database:
  data(dbExample)
  obs <- dbCompare(dbExample,trace=FALSE)$m
  C3 <- optim.relatedness(obs,theta0=0.0,theta1=0.03,probs=freqs,
          objFunction='C3',max.bisect=30,trace=TRUE)
  print(C3)
  
## End(Not run)

Simulate Allele Frequencies

Description

Simulate some allele frequencies using Dirichlet Random variables

Usage

simAlleleFreqs(
  nLoci = 10,
  allelesPerLocus = rep(10, nLoci),
  shape = rep(3, nLoci)
)
simAlleleFreqs(
  nLoci = 10,
  allelesPerLocus = rep(10, nLoci),
  shape = rep(3, nLoci)
)

Arguments

`nLoci`	$L$ the number of loci in the multiplex
`allelesPerLocus`	the number of alleles per locus
`shape`	the shape parameter

Value

a list with elements locus. $l$ where $l=1,\ldots,L$ , each of which are vectors of length allelesPerLocus[l], consisting of allele frequencies for that locus

Examples

set.seed(123)
simAlleleFreqs()

set.seed(123)
simAlleleFreqs()

Package 'DNAtools'

Help Index

Tools for analysing forensic genetic DNA databases

Description

Details

Author(s)

References

Examples

Collapse m/p output to vector

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Compare DNA profiles

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Simulated database with 1,000 individuals

Description

Format

Expected value of cell counts in DNA database comparison

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Simulate a DNA database

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Covariance matrix of cell counts in DNA database comparison

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Estimate the drop-out probability based on number of alleles

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Simple allele frequency estimation

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Generates DNA profiles of n individuals.

Description

Usage

Arguments

Generates DNA profiles of n unrelated individuals for a locus

Description

Usage

Arguments

Estimate theta and the fraction of comparisons between close relatives

Description

Compute the posterior probabilities for $\Pr(m|n_0)$ for a given prior $\Pr(m)$ .