Compute a spatial neighborhood interaction matrix using K-nearest neighbors (KNN)

Computes a spatial interaction matrix where each entry quantifies the number of neighboring cells from a given cluster (columns) that are among the k-nearest neighbors of cells in another cluster (rows). This provides a summary of spatial proximity and enrichment of neighboring clusters around each focal cluster.

Usage

computeSpatialInteractionMatrix(data = NULL, cluster_col = NULL, k = 10)

Arguments

data

A Seurat object, a SpatialExperiment object, or a data frame containing spatial coordinates.

cluster_col

Character scalar specifying the metadata column name containing cluster assignments. If NULL, a default is used depending on the input object type:

• "seurat_clusters" for Seurat objects

• "cluster" for SpatialExperiment objects

k

Integer. Number of nearest neighbors to use for each cell. Default is 10.

Value

A numeric matrix where rows represent focal clusters and columns represent neighboring clusters. Each cell in the matrix indicates how frequently a neighbor cluster appears among the k-nearest neighbors of cells from the focal cluster.

Details

The matrix is built by identifying the k nearest neighbors for each cell based on spatial coordinates, and then tabulating the cluster identities of those neighbors with respect to the cluster identity of the focal cell.

Examples

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

# Compute interaction matrix using all cells
interaction_matrix <- computeSpatialInteractionMatrix(coords)
head(interaction_matrix)
#>       0     1     2     3     4     5    6   7   8   9   10  11
#> 0 26326  6590  5670  7847  4608  4212 2814 548 364 542  456 193
#> 1  6540 33843  2707  7455   607    15 5119   0   0   0   11 203
#> 2  5956  2774 30886  4694  5792  1908 1914 219  79 230 1393 155
#> 3  8572  7701  4895 11808  5558   901 3666 881 628 980  146 194
#> 4  4729   624  5754  5605 23932     0 3589   0   0   0    0   7
#> 5  4337    12  1682   849     0 29000   27   6   0   1    6   0

# Compute interaction matrix for cells inside boundaries
boundary_points <- getBoundary(
    data = coords, one_cluster = 2,
    eps = 120, minPts = 10
)
boundary_points <- subset(boundary_points, region_id == 2) # (Optional)
cells_inside <- getCellsInside(data = coords, boundary = boundary_points)
coords_sub <- subset(coords, cell %in% cells_inside$cell)
computeSpatialInteractionMatrix(coords_sub)
#>      0    2  3   5
#> 0 3003   94 12  21
#> 2  179 1239  2 170
#> 3   10    2  0  18
#> 5   23  112 15 940

# Compute interaction matrix for cells inside ring region 2
ring_regions <- getRingRegion(boundary = boundary_points, dist = 100)
cells_ring <- getCellsInside(data = coords, boundary = ring_regions)
coords_sub <- subset(coords, cell %in% cells_ring$cell)
computeSpatialInteractionMatrix(coords_sub)
#>      0   2   3    5 6 7
#> 0 2634 219 230  476 8 3
#> 2  253 183  51  527 6 0
#> 3  304  52 230   66 7 1
#> 5  492 384  54 7479 1 0
#> 6    6   5   7    2 0 0
#> 7    4   0   6    0 0 0