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Count annotated spot types in the neighborhood of a cluster or point

Source: R/R-neighbourhood.R
count_neighborhood.Rd

This function computes summary statistics of cluster types in the neighborhood of a target cluster or a specific point. It uses [get_neighborhood_spots()] internally to identify neighbors, then counts how many belong to each cluster type.

Usage

count_neighborhood(
  df,
  cluster = NULL,
  spot_id = 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)

cluster

The cluster label for which to analyze the neighborhood. Either `cluster` or `spot_id` must be provided, but not both.

spot_id

Character. The spot identifier to find neighbors for. Either `cluster` or `spot_id` must be provided, but not both.

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:

neighborhood

Cluster label or inlaid value (from neighboring spots).

count

Number of times this cluster/type appears in the neighborhood.

proportion

Proportion of this cluster/type relative to all neighbors.

Rows are sorted by count in descending order.

Details

This function wraps [get_neighborhood_spots()] and summarizes the results by counting spots from each neighboring cluster or inlaid type. In cluster mode, the source cluster itself is excluded from the counts. In point mode, all neighboring clusters are counted.

The result shows the composition of the neighborhood: how many spots of each cluster type surround the target.

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"
# Count cluster types in neighborhood of cluster 1
neighbor_counts <- count_neighborhood(df, cluster = 1, k = 50)
#> Error in get_neighborhood_spots(df, cluster, spot_id, k, max_dist, inlaid_col,     coords, cluster_col): is.data.frame(df) is not TRUE
neighbor_counts
#> Error: object 'neighbor_counts' not found

# Count cluster types around a specific point
first_spot <- df$spot_id[1]
#> Error in df$spot_id: object of type 'closure' is not subsettable
point_neighbors <- count_neighborhood(df, spot_id = first_spot, k = 10)
#> Error in get_neighborhood_spots(df, cluster, spot_id, k, max_dist, inlaid_col,     coords, cluster_col): is.data.frame(df) is not TRUE
point_neighbors
#> Error: object 'point_neighbors' not found