cointf.simintf: simulation¶

Simulation of crossover positions under the Housworth-Stahl model.

The interference pathway is a stationary renewal process with gap density f* rescaled to rate 1 - p; the escape pathway is a homogeneous Poisson process with rate p. Observed crossovers are their superposition.

cointf.simintf.simulate_meioses(L, nu, p, n, seed)¶

Simulate independent meioses under the Housworth-Stahl model.

Parameters:

Lfloat: Chromosome length in Morgans, > 0.
nufloat: Interference parameter, > 0.
pfloat: Fraction of crossovers escaping interference, in [0, 1].
nint: Number of meioses, > 0.
seedint: Seed for numpy.random.default_rng(), > 0.

Returns:

list of numpy.ndarray: Sorted crossover positions in Morgans, one array per meiosis.

Raises:

ValueError: If n or seed is not positive, or a model parameter is out of range.

See also

simulate_meiosis: Simulate a single meiosis.

Examples

>>> data = simulate_meioses(L=1.5, nu=4.0, p=0.05, n=3, seed=7)
>>> len(data)
3

cointf.simintf.simulate_meiosis(L: float, nu: float, p: float, rng: Generator | None = None) → ndarray¶

Simulate one meiosis under the Housworth-Stahl model.

Crossovers are the superposition of the interference pathway (rate 1 - p) and the escape pathway (rate p).

Parameters:

Lfloat: Chromosome length in Morgans, > 0.
nufloat: Interference parameter, > 0. nu = 1 is no interference and nu > 1 positive interference.
pfloat: Fraction of crossovers escaping interference, in [0, 1]. p = 0 is the pure interference model and p = 1 a Poisson process.
rngnumpy.random.Generator, optional: Random number generator; a fresh numpy.random.default_rng() if None.

Returns:

numpy.ndarray: Sorted crossover positions in Morgans, in [0, L]; may be empty.

Raises:

ValueError: If L, nu or p is out of range.

See also

simulate_meioses: Simulate many meioses from one seed.

Notes

The expected number of crossovers is L for every (nu, p). For nu > 1 the interference pathway has sub-Poisson counts.

Examples

>>> import numpy as np
>>> xs = simulate_meiosis(1.5, nu=4.0, p=0.05, rng=np.random.default_rng(1))
>>> bool(np.all((xs >= 0) & (xs <= 1.5)))
True

cointf.simintf.simulate_quartets(L, nu, p, n, seed)¶

Simulate phase-unknown (quartet) crossover data.

Each dataset pools the crossovers of two independent meioses with the same parameters, as observed for one parent in a family with two offspring when it is unknown which offspring inherited each crossover.

Parameters:

Lfloat: Chromosome length in Morgans, > 0.
nufloat: Interference parameter, > 0.
pfloat: Fraction of crossovers escaping interference, in [0, 1].
nint: Number of quartets, > 0.
seedint: Seed for numpy.random.default_rng(), > 0.

Returns:

list of numpy.ndarray: Sorted pooled crossover positions in Morgans, one array per quartet.

Raises:

ValueError: If n or seed is not positive, or a model parameter is out of range.