Count neighborhood composition for all clusters

This function computes neighborhood statistics for all clusters in a dataset in a single call. It returns a dataframe with counts of neighboring cluster types for each source cluster.

Usage

count_all_neighborhoods(
  df,
  clusters = NULL,
  k = 100,
  max_dist = NULL,
  inlaid_col = NULL,
  coords = c("x", "y"),
  cluster_col = "cluster"
)

Arguments

df

A data.frame containing at least the columns `x`, `y`, cluster identifier column, and `spot_id`. - `x`, `y`: numeric coordinates of spots - cluster column (name specified by `cluster_col`): cluster assignment for each spot - `spot_id`: unique identifier for each spot (optional, required for detailed analysis)

clusters

Optional vector of cluster labels to process. If `NULL`, all unique clusters in `df` are used.

k

Integer. Number of nearest neighbors to consider. Default is 100. Larger values capture more distant neighbors.

max_dist

Numeric. Maximum distance from the target to consider. If `NULL` (default), all neighbors up to k are included without distance filtering.

inlaid_col

Character. Name of the column containing inlaid/annotation values to count in neighborhoods. If `NULL` (default), uses the `cluster_col` value. This allows counting neighborhood composition by any column (e.g., cell type, tissue type).

coords

Character vector of length 2 giving the coordinate column names. Default is `c("x", "y")`.

cluster_col

Character. Name of the column containing cluster assignments. Default is `"cluster"`.

Value

A data.frame with columns:

cluster

The source cluster being analyzed.

neighborhood

The neighbor cluster type or inlaid value.

count

Number of neighbor spots of this cluster type.

proportion

Proportion of this cluster among all neighbors of the cluster.

Rows are grouped by source cluster and sorted by count within each group (descending).

Details

This is a batch processing function that applies [count_neighborhood()] to all clusters and combines the results into a single dataframe. Each row represents a neighbor cluster found around a source cluster, with counts and proportions.

Examples

data("visium_simulated_spe", package = "Battlefield")
spe <- visium_simulated_spe
df <- data.frame(
  spot_id = colnames(spe),
  x = spatialCoords(spe)[, 1],
  y = spatialCoords(spe)[, 2],
  cluster = colData(spe)$cluster
)
#> Error in spatialCoords(spe): could not find function "spatialCoords"
# Get neighborhood statistics for all clusters
all_neighbors <- count_all_neighborhoods(df, k = 50)
#> Error in count_all_neighborhoods(df, k = 50): is.data.frame(df) is not TRUE
head(all_neighbors)
#> Error: object 'all_neighbors' not found