Compute Spatial Enrichment Index (SEI) for Each Gene — computeSpatialEnrichmentIndex • SpNeigh

Calculates the spatial enrichment index (SEI) for each gene based on a user-supplied set of spatial weights. The SEI reflects the extent to which gene expression is enriched in spatially weighted regions of the tissue (e.g., near boundaries or centroids).

Usage

computeSpatialEnrichmentIndex(exp_mat = NULL, weights = NULL)

Arguments

exp_mat: A normalized gene expression matrix with genes as rows and cells as columns. Should be of class matrix or dgCMatrix.
weights: A numeric vector of spatial weights (e.g., from computeBoundaryWeights or computeCentroidWeights). Must be the same length as the number of columns (cells) in exp_mat.

Value

A data frame with the following columns:

gene: Gene name
SEI: Spatial enrichment index: weighted mean expression across cells
mean_expr: Mean expression across all cells (unweighted)
normalized_SEI: Ratio of SEI to mean expression; used to compare genes independent of baseline expression

The result is sorted in descending order by normalized_SEI.

Details

This method supports both dense (matrix) and sparse (dgCMatrix) gene expression formats, and can be applied using any distance-based weighting scheme.

Examples

# Load spatial coordinates and log-normalized expression
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
    package = "SpNeigh"
))
logNorm_expr <- readRDS(system.file("extdata", "LogNormExpr.rds",
    package = "SpNeigh"
))

# Compute spatial weights and SEI
bon_c0 <- getBoundary(data = coords, one_cluster = 0)
cells_c0 <- subset(coords, cluster == 0)$cell
weights <- computeBoundaryWeights(
    data = coords,
    cell_ids = cells_c0,
    boundary = bon_c0
)
sei_df <- computeSpatialEnrichmentIndex(logNorm_expr[, cells_c0], weights)
head(sei_df)
#>      gene      SEI mean_expr normalized_SEI
#> 1    Fmod 1.742086  1.562370       1.115027
#> 2    Gjb2 1.325063  1.193263       1.110452
#> 3 Aldh1a2 1.820161  1.642933       1.107872
#> 4 Slc13a4 2.106610  1.906511       1.104955
#> 5  Col1a1 1.842083  1.675953       1.099125
#> 6  Cyp1b1 1.495145  1.365958       1.094575