Here’s everything you need to get started.
# Mock data: 5 SVGs (all cell-type-1 specific ctSVGs) and 5 non-SVGs
set.seed(123)
spatial_coords <- expand.grid(x = seq(0, 10, length.out = 20), y = seq(0, 10, length.out = 20))
cell_type_proportions <- data.frame("Cell_type_1"=c(runif(200,0,0.5),runif(200,0.5,1)),"Cell_type_2"=rep(NA,400)); cell_type_proportions$Cell_type_2 = 1 - cell_type_proportions$Cell_type_1
K <- exp(-as.matrix(dist(spatial_coords))); Sigma <- 2 * chol(K) %*% diag(cell_type_proportions$Cell_type_1); counts <- apply(exp(rbind(matrix(rnorm(2000),5,400) %*% Sigma, matrix(rnorm(2000),5,400))), 1:2, function(x)rpois(1,x)); rownames(counts) <- paste0("Gene_", 1:10)
normalized_counts <- log1p(counts)/log(2)
# Run global Spacelink
global_results <- spacelink(
normalized_counts = normalized_counts, # Gene x Spot log-transformed normalized count matrix
spatial_coords = spatial_coords # Spot x 2 spatial coordinates
)
# Run cell-type-specific Spacelink
cell_type_results <- spacelink_ctSVG(
normalized_counts = normalized_counts, # Gene x Spot log-transformed normalized count matrix
spatial_coords = spatial_coords, # Spot x 2 spatial coordinates
cell_type_proportions = cell_type_proportions, # Spot x Cell-type proportions matrix
focal_cell_type = "Cell_type_1" # Name of focal cell-type
)
# Check results
global_results[, c("padj", "ESV")]
## padj ESV
## Gene_1 6.193842e-20 0.69073236
## Gene_2 1.676751e-15 0.69073236
## Gene_3 3.501070e-10 0.51188196
## Gene_4 1.446757e-27 0.69073236
## Gene_5 2.331051e-16 0.69073236
## Gene_6 8.466960e-01 0.00000000
## Gene_7 4.549321e-01 0.03219048
## Gene_8 8.466960e-01 0.00000000
## Gene_9 1.942449e-01 0.08369567
## Gene_10 8.466960e-01 0.00000000
cell_type_results[, c("padj", "ESV")]
## padj ESV
## Gene_1 3.543090e-34 7.008895e-01
## Gene_2 4.690917e-24 6.877475e-01
## Gene_3 3.163935e-12 6.532014e-01
## Gene_4 2.860108e-33 7.393841e-01
## Gene_5 1.955930e-17 7.325753e-01
## Gene_6 8.934313e-01 4.875191e-06
## Gene_7 1.174734e-01 1.476443e-01
## Gene_8 8.934313e-01 5.869161e-06
## Gene_9 1.142349e-01 1.429539e-01
## Gene_10 8.934313e-01 5.689795e-06