Plot a heatmap of a row-scaled spatial interaction matrix

Visualizes a spatial interaction matrix using a heatmap, where rows represent focal clusters and columns represent neighbor clusters. Each row is scaled using z-scores to highlight relative enrichment patterns across neighbor types. This is useful for detecting spatial proximity patterns between cell populations.

Usage

plotInteractionMatrix(
  interaction_matrix = NULL,
  low_color = "blue",
  mid_color = "white",
  high_color = "red",
  angle_x_label = 45,
  title = "Row-Scaled Interaction Matrix (Z-scores)"
)

Arguments

interaction_matrix

A numeric matrix with focal clusters as rows and neighbor clusters as columns. Typically the output from computeSpatialInteractionMatrix().

low_color

Color representing low z-score values. Default is "blue".

mid_color

Color representing the midpoint (z-score = 0). Default is "white".

high_color

Color representing high z-score values. Default is "red".

angle_x_label

Angle (in degrees) to rotate x-axis labels. Default is 45.

title

Title for the heatmap.

Value

A ggplot object representing the row-scaled heatmap of the interaction matrix.

Examples

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

# Obtain boundaries
boundary_points <- getBoundary(
    data = coords, one_cluster = 2,
    eps = 120, minPts = 10
)
# Select regions of interests if needed (Optional)
boundary_points <- subset(boundary_points, region_id == 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)
interaction_matrix <- computeSpatialInteractionMatrix(coords_sub)

plotInteractionMatrix(interaction_matrix)