sim_biallelic: Simulate biallelic data

In mrc-ide/MIPanalyzer: Filtering and analysis of MIP data

Simulate biallelic data

Description

Simulate biallelic data from a simple statistical model. Inputs include the complexity of infection (COI), population-level allele frequencies (PLAF) and some parameters dicating skew and error distributions. Outputs include the phased haplotypes and the un-phased read count and coverage data.

Usage

sim_biallelic(
  COI = 3,
  PLAF = runif(10, 0, 0.5),
  coverage = 100,
  alpha = 1,
  overdispersion = 0,
  epsilon = 0
)

Arguments

COI	complexity of infection.
PLAF	vector of population-level allele frequencies at each locus.
coverage	coverage at each locus. If a single value then the same coverage is applied over all loci.
alpha	shape parameter of the symmetric Dirichlet prior on strain proportions.
overdispersion	the extent to which counts are over-dispersed relative to the binomial distribution. Counts are Beta-binomially distributed, with the beta distribution having shape parameters p/overdispersion and (1-p)/overdispersion.
epsilon	the probability of a single read being mis-called as the other allele. Applies in both directions.

Details

Simulated data are drawn from a simple statistical model:

1. Strain proportions are drawn from a symmetric Dirichlet distribution with shape parameter alpha.

2. Phased haplotypes are drawn at every locus, one for each COI. The allele at each locus is drawn from a Bernoulli distribution with probability given by the PLAF.

3. The "true" within-sample allele frequency at every locus is obtained by multiplying haplotypes by their strain proportions, and summing over haplotypes. Errors are introduced through the equation

   wsaf_error = wsaf*(1-e) + (1-wsaf)*e

   where wsaf is the WSAF without error and e is the error parameter epsilon.

4. Final read counts are drawn from a beta-binomial distribution with expectation w_error. The raw number of draws is given by the coverage, and the skew of the distribution is given by the overdispersion parameter. If overdispersion = 0 then the distribution is binomial, rather than beta-binomial.

mrc-ide/MIPanalyzer documentation built on Jan. 17, 2024, 7:16 p.m.