Global Spatial Variability Analysis

Conducts hypothesis testing to identify spatially variable genes (SVGs) using an adaptive multi-kernel approach, and provides Effective Spatial Variability (ESV) scores (ranging from 0 to 1), where higher values indicate greater spatial variability in gene expression.

Usage

spacelink(
  normalized_counts,
  spatial_coords,
  covariates = NULL,
  lite = FALSE,
  grid_size = NULL,
  kernel = "Exponential",
  n_lengthscales = 5,
  n_workers = 1
)

Arguments

normalized_counts

Normalized count matrix (genes x spots). Accepts a numeric matrix, data.frame, or sparse matrix (sparseMatrix).

spatial_coords

Two-dimensional spatial coordinate matrix (spots x 2). Accepts a numeric matrix or data.frame.

covariates

Optional covariate matrix (spots x covariates, without intercept). Accepts a numeric vector, matrix, or data.frame. Default is NULL.

lite

Logical. If TRUE, uses a grid-based approximation to speed up computation for large datasets. Default is FALSE.

grid_size

Grid cell size for lite mode. If NULL, set automatically to 5 times the minimum nearest-neighbor distance. Default is NULL.

kernel

Spatial kernel type. One of "Exponential" (default) or "Gaussian".

n_lengthscales

Number of length scales (kernels). Default is 5.

n_workers

Number of workers for parallelization. Default is 1.

Value

A data frame (genes x results) containing:

tau.sq

Nugget variance component

sigma.sq1, sigma.sq2, ...

Spatial variance components for each kernel

phi1, phi2, ...

Inverse length scales for each kernel

ESV

Effective Spatial Variability score

pval1, pval2, ...

Score test p-values for each kernel

pval

Combined p-value (ACAT)

padj

Benjamini-Hochberg adjusted p-value

ESV_adj

ESV adjusted for non-SVGs (0 if padj > 0.05)

time

Computation time per gene (seconds)

Examples

# \donttest{
library(spacelink)
set.seed(123)
n_spots <- 100
n_genes <- 10

coords <- expand.grid(
  x = seq(0, 10, length.out = sqrt(n_spots)),
  y = seq(0, 10, length.out = sqrt(n_spots))
)

expr_data <- matrix(nrow = n_genes, ncol = n_spots)
rownames(expr_data) <- paste0("Gene_", 1:n_genes)
D <- as.matrix(dist(coords))
K <- exp(-D / 2); chol_K <- chol(K)
for (i in 1:(n_genes / 2)) {
  expr_data[i, ] <- rnorm(n_spots) + i * rnorm(n_spots) %*% chol_K
}
for (i in (n_genes / 2 + 1):n_genes) {
  expr_data[i, ] <- rnorm(n_spots)
}

results <- spacelink(normalized_counts = expr_data, spatial_coords = coords)
print(results[, c("ESV", "pval", "padj")])
#>                ESV         pval         padj
#> Gene_1  0.36240416 1.316941e-02 2.633882e-02
#> Gene_2  0.67450493 1.641875e-05 5.472917e-05
#> Gene_3  0.63469294 2.190094e-04 5.475235e-04
#> Gene_4  0.67450493 1.096747e-06 5.483735e-06
#> Gene_5  0.67450493 2.671183e-08 2.671183e-07
#> Gene_6  0.00000000 7.483181e-01 8.645015e-01
#> Gene_7  0.00000000 8.050144e-01 8.645015e-01
#> Gene_8  0.06886313 3.269677e-01 5.449462e-01
#> Gene_9  0.00000000 8.351624e-01 8.645015e-01
#> Gene_10 0.00000000 8.645015e-01 8.645015e-01
# }