Create Spatial Networks

Create spatial networks from spatial coordinates. The spatial networks are provided in a long format which holds information about spot neighbors, their center-to-center distances and positions.

Usage

GetSpatialNetwork(object, ...)

# Default S3 method
GetSpatialNetwork(object, nNeighbors = 6, maxDist = NULL, minK = 0, ...)

# S3 method for class 'Seurat'
GetSpatialNetwork(object, nNeighbors = 6, maxDist = NULL, minK = 0, ...)

Arguments

object: An object
...: Arguments passed to other methods
nNeighbors: Number of nearest neighbors to calculate for each spot. The default number of neighbors is 6 given the hexagonal pattern of 10x Visium arrays.
maxDist: Distance cut-off for nearest neighbors to consider. If set to NULL (default), maxDist is estimated from the data by taking the minimum neighbor distance multiplied by a factor of 1.2.
minK: Minimum nearest neighbors [default: 0]. Spots with fewer neighbors will be discarded. Useful if you want to remove spots with few or no neighbors.

Value

A list of tibbles, each containing information about the nearest neighbors of each spot. For one spot in the column "from", its nearest neighboring spots are provided in the "to" column. Distances correspond to distances between "to" and "from", and usually correspond to H&E image pixels. nNeighbors defines the number of nearest neighbors for "from" spots selected by GetSpatialNetwork. "x_start", "y_start" are the spatial coordinates for "from" spots while "x_end", "y_end" are the spatial coordinates for the neighboring "to" spots.

Details

The default method expects an object of class tbl or data.frame with four columns "barcode", "x", "y" and "sample" holding the coordinates for a set of spots. The "barcode" column is a character vector with spatial barcodes, "x", "y" hold numeric values representing the spot coordinates and "sample" is a character vector with unique sample IDs.

Author

Ludvig Larsson

Examples

# \donttest{
library(ggplot2)

# Create a spatial network from a tibble with barcodes, (x, y) coordinates and sample IDs
coordfiles <- c(system.file("extdata/mousebrain/spatial",
                            "tissue_positions_list.csv",
                            package = "semla"),
                system.file("extdata/mousecolon/spatial",
                            "tissue_positions_list.csv",
                            package = "semla"))

# Load coordinate data into a tibble
xys <- do.call(rbind, lapply(seq_along(coordfiles), function(i) {
  coords <- setNames(read.csv(coordfiles[i], header = FALSE),
                     nm = c("barcode", "selection", "grid_y", "grid_x", "y", "x"))
  coords$sampleID <- i
  coords <- coords |>
    dplyr::filter(selection == 1) |>
    dplyr::select(barcode, x, y, sampleID) |>
    tibble::as_tibble()
  return(coords)
}))

# Create spatial networks from xys coordinates
spatnet <- GetSpatialNetwork(xys)

# Plot network
p1 <- ggplot(spatnet[["1"]], aes(x = x_start, xend = x_end, y = y_start, yend = y_end)) +
  geom_segment() +
  scale_y_reverse()

p2 <- ggplot(spatnet[["2"]], aes(x = x_start, xend = x_end, y = y_start, yend = y_end)) +
  geom_segment() +
  scale_y_reverse()

p1 + p2

# }


library(semla)

se_mbrain <- readRDS(system.file("extdata/mousebrain", "se_mbrain", package = "semla"))

# Get spatial network from a Seurat object
spatnet <- GetSpatialNetwork(se_mbrain)

# Plot network
ggplot(spatnet[["1"]], aes(x = x_start, xend = x_end, y = y_start, yend = y_end)) +
  geom_segment() +
  scale_y_reverse()