Getting started

Basic log normal example

Below, we load allele frequencies provided with the package and demonstrate how mixtures can be sampled using a log normal model for peak heights (refer to (Taylor, Bright, and Buckleton 2013) for details on the model). The template parameters for each contributor are picked uniformly between 50 and 10,000. The degradation parameter is picked from a gamma distribution with shape 2.5 and scale 0.001.

freqs <- read_allele_freqs(system.file("extdata","FBI_extended_Cauc_022024.csv",
                                       package = "simDNAmixtures"))
gf <- gf_configuration()

sampling_parameters <- list(min_template = 50., max_template = 10000.,
                            degradation_shape = 2.5, degradation_scale = 1e-3)

After the setup, a single function call is sufficient to generate a set of samples. We set n=2 to generate two samples. The contributors to the sample are named U1 and U2 which means that each mixed sample has two unrelated contributors.

set.seed(1)
mixtures <- sample_mixtures(n = 2, contributors = c("U1", "U2"), freqs = freqs,
                            sampling_parameters = sampling_parameters,
                            model_settings = gf$log_normal_bwfw_settings,
                            sample_model = sample_log_normal_model)

The mixtures object contains the simulation results. The sample_mixtures function has an optional results_directory argument. If the path to a directory is provided, then all simulation results will be persisted on disk in file formats that are readily imported in probabilistic genotyping software. We inspect the parameter_summary property of the result of sample_mixtures.

knitr::kable(mixtures$parameter_summary[1:5])

The first sample is in approximately a 2:1 ratio. We print the first 10 peaks of the profile below.

knitr::kable(head(mixtures$samples[[1]]$mixture, 10))

Basic gamma example

We repeat the example above with a gamma distribution for peak heights instead of a log normal one. The mean peak height parameter mu is chosen uniformly between 50 and 5,000. The variance parameter cv parameter (\(\sigma\) in (Bleka, Storvik, and Gill 2016)) is chosen uniformly between 0.05 and 0.35. The degradation parameter is sampled from a Beta distribution with parameters 10 and 1, which means most profile will be only mildly degraded.

gamma_sampling_parameters <- list(min_mu = 50., max_mu = 5e3,
                            min_cv = 0.05, max_cv = 0.35,
                            degradation_shape1 = 10, degradation_shape2 = 1)

We now invoke the sample_mixtures function again to sample two mixtures; both consisting of two unrelated contributors. We do not sample stutters.

set.seed(2)
mixtures <- sample_mixtures(n = 2, contributors = c("U1", "U2"), freqs = freqs,
                            sampling_parameters = gamma_sampling_parameters,
                            model_settings = gf$gamma_settings_no_stutter,
                            sample_model = sample_gamma_model)

We see that the first sample has \(\mu\) around 1,236 and is in a 65:35 ratio.

knitr::kable(mixtures$parameter_summary[1:4]) 

The coefficient of variation for a full heterozygote is about 9% for the first sample.

knitr::kable(mixtures$parameter_summary[c(1,5:7)])

We print the first 10 peaks of the profile below.

knitr::kable(head(mixtures$samples[[1]]$mixture, 10))