random_dataset: Generate a random MOBSTER model and data.

In caravagnalab/mobster: MOBSTER - Model-based tumour subclonal deconvolution

Generate a random MOBSTER model and data.

Description

Generate a random MOBSTER model, its data and creates a plot for it.

Usage

random_dataset(
  N = 5000,
  K_betas = 2,
  pi_tail_bounds = c(0.2, 0.4),
  pi_min = 0.1,
  Betas_separation = 0.1,
  Beta_variance_scaling = 1000,
  Beta_bounds = c(0.1, 0.9),
  shape_bounds = c(1, 1, 3),
  scale = 0.05,
  seed = NULL
)

Arguments

N	Number of samples to generate (mutations).
K_betas	Number of Beta components (subclones).
pi_tail_bounds	2D vector with min and max size of the tail's mutations (proportions).
pi_min	Minimum mixing proportion for every component.
Betas_separation	Minimum separation between the means of the Beta components.
Beta_variance_scaling	The variance of the Beta is generated as U[0,1] and scaled by this value. Values on the order of 1000 give low variance, 100 represents a dataset with quite some dispersion ( compared to a putative Binomial generative model).
Beta_bounds	Range of values to sample the Beta means.
shape_bounds	Range of values to sample the tail shape, default [1, 3],
scale	Tail scale, default 0.05.
seed	The seed to fix the process, default is 123.

Value

A list with the dataset in a tibble, the model parameters and a plot the data.

Examples

x = random_dataset()
print(x)

caravagnalab/mobster documentation built on March 25, 2023, 3:40 p.m.