spatialDistribution: Quantify the spatial distribution of mutants

In phillipnicol/SITH: A Spatial Model of Intra-Tumor Heterogeneity

Quantify the spatial distribution of mutants

Description

Provides a summary the spatial distribution of mutants within the simulated tumor.

Usage

spatialDistribution(tumor, N = 500, cutoff = 0.01, make.plot = TRUE)

Arguments

tumor	A list which is the output of simulateTumor().
N	The number of pairs to sample.
cutoff	For a plot of clone sizes, all mutations with a MAF below cutoff are ignored.
make.plot	Whether or not to make plots.

Details

The genotype of a cell can be interpreted as a binary vector where the i-th component is 1 if mutation i is present in the cell and is 0 otherwise. Then a natural comparison of the similarity between two cells is the Jaccard index J(A,B) = |I(A,B)|/|U(A,B)|, where I(A,B) is the intersection of A and B and U(A,B) is the union. This function estimates the Jaccard index as a function of Euclidean distance between the cells by randomly sampling N pairs of cells.

Value

A list with the following components

• mean_mutant - A data frame with 2 columns giving the mean number of mutants as a function of Euclidean distance from the lattice origin (Euclid. distance rounded to nearest integer).

• mean_driver - The same as mean_mutant except for driver mutations only. Will be NULL if no drivers are present in the simulated tumor.

• jaccard A data frame with two columns giving mean jaccard index as a function of Euclidean distance between pairs of cells (rounded to nearest integer).

Author(s)

Phillip B. Nicol

Examples

set.seed(1126490984)
out <- simulateTumor(max_pop = 1000, driver_prob = 0.1)
sp <- spatialDistribution(tumor = out, make.plot = FALSE)

phillipnicol/SITH documentation built on April 4, 2024, 8:10 p.m.