Calculate Neighborhood Purity

Integrated from simPIC (Chugh et al., 2024; bluster::neighborPurity). For each cell, evaluates the proportion of its k-nearest neighbors that share the same cluster or cell-type identity.

Usage

calc_neighborhood_purity(data, cluster_labels, k = NULL, is_distance = FALSE)

Arguments

data

Matrix of coordinates (cells x dimensions) or expression matrix (features x cells) or dist matrix.

cluster_labels

Vector of cluster or cell-type labels.

k

Number of nearest neighbors (default 5% of cells).

is_distance

Logical, whether data is already a distance matrix. Default FALSE.

Value

A numeric vector of neighborhood purity scores (values from 0 to 1), with an attribute "mean_purity" containing the overall average.

Examples

data <- matrix(stats::rnorm(200), 20, 10)
cl <- factor(rep(c("A", "B"), each = 10))
dist_mat <- stats::dist(data)
calc_neighborhood_purity(data, cl)
#>  [1] 0.4285714 0.4285714 0.6250000 0.7500000 1.0000000 0.5000000 1.0000000
#>  [8] 0.6666667 0.6000000 0.4285714 0.6000000 0.5000000 0.7142857 0.6666667
#> [15] 0.8333333 0.6000000 0.8571429 0.4000000 0.2500000 0.6363636
#> attr(,"mean_purity")
#> [1] 0.6242587