Compute spatial weights based on distance to the centroid

Computes spatial weights for a set of cells based on their Euclidean distance to the centroid of the selected group of cells. This is useful for modeling gradient-like spatial expression centered around a cluster or region. Supports multiple decay methods and optional distance scaling.

Usage

computeCentroidWeights(
  data = NULL,
  cell_ids = NULL,
  scale = TRUE,
  method = c("inverse", "gaussian", "linear", "quadratic"),
  sigma = 0.5
)

Arguments

data

A data frame, Seurat object or SpatialExperiment object containing spatial coordinates. Must include columns: cell, x, and y.

cell_ids

A character vector of cell IDs to use for weight computation.

scale

Logical. Whether to scale distances to the range [0, 1] before applying decay functions. Default is TRUE.

method

Decay function to convert distances to weights. One of: "inverse", "gaussian", "linear", or "quadratic".

sigma

Standard deviation for the Gaussian decay (used only if method = "gaussian"). Default is 0.5 (recommended for scaled distances).

Value

A named numeric vector of weights (length = number of cells in cell_ids). Higher weights correspond to cells closer to the centroid.

Examples

# Load spatial coordinates
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
    package = "SpNeigh"
))

# Select cells from cluster 2
cells_c2 <- subset(coords, cluster == 2)$cell

# Compute centroid-based weights using default settings
weights <- computeCentroidWeights(data = coords, cell_ids = cells_c2)
head(weights)
#>         3         8         9        11        18        20 
#> 0.7829382 0.7033367 0.7067281 0.7090737 0.7185800 0.8012193