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Identify cells located within spatial boundaries or ring regions

Source: R/GetCellsInside.R
GetCellsInside.Rd

Returns the subset of cells from the input data that fall spatially within a given boundary or ring. The boundary can be provided either as raw boundary points (from getBoundary()), as polygons (from buildBoundaryPoly()), or as ring regions (from getRingRegion()). The function uses spatial point-in-polygon matching via sf::st_within.

Usage

getCellsInside(data = NULL, cluster_col = NULL, boundary = NULL)

Arguments

data

A Seurat object, a SpatialExperiment object, or a data frame containing spatial coordinates.

cluster_col

Character scalar specifying the metadata column name containing cluster assignments. If NULL, a default is used depending on the input object type:

  • "seurat_clusters" for Seurat objects

  • "cluster" for SpatialExperiment objects

boundary

An sf object (polygon or ring) or a data frame of boundary points returned by getBoundary(), buildBoundaryPoly(), or getRingRegion().

Value

A data frame (tibble) of cells located inside the given spatial region(s), with region assignment in a region_id column.

Examples

# Load coordinates
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
    package = "SpNeigh"
))
head(coords)
#>          x        y cell cluster
#> 1 1898.815 2540.963    1       4
#> 2 1895.305 2532.627    2       4
#> 3 2368.073 2534.409    3       2
#> 4 1903.726 2560.010    4       4
#> 5 1917.481 2543.132    5       4
#> 6 1926.540 2560.044    6       4

# Get cells inside 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)

cells_inside <- getCellsInside(data = coords, boundary = boundary_points)
cells_inside
#> Simple feature collection with 584 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4665.024 ymin: 2692.795 xmax: 5163.274 ymax: 3317.818
#> CRS:           NA
#> First 10 features:
#>      cell cluster region_id                  geometry
#> 1860 1860       2         2  POINT (4937.139 3062.65)
#> 1863 1863       2         2  POINT (4926.83 3260.598)
#> 1865 1865       2         2 POINT (4959.216 3264.934)
#> 1875 1875       5         2 POINT (4961.168 3277.609)
#> 1880 1880       2         2 POINT (4939.041 3272.613)
#> 1891 1891       2         2 POINT (4955.413 3256.343)
#> 1894 1894       2         2 POINT (4932.086 3195.083)
#> 1897 1897       2         2 POINT (4934.451 3038.603)
#> 1898 1898       2         2  POINT (4942.686 3216.76)
#> 1902 1902       2         2 POINT (4953.803 3244.153)

# Get cells inside rings
ring_regions <- getRingRegion(boundary = boundary_points, dist = 100)
cells_ring <- getCellsInside(data = coords, boundary = ring_regions)
cells_ring
#> Simple feature collection with 1369 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 4572.773 ymin: 2600.433 xmax: 5264.996 ymax: 3421.448
#> CRS:           NA
#> First 10 features:
#>      cell cluster region_id                  geometry
#> 1861 1861       5         2 POINT (4933.268 3097.647)
#> 1862 1862       2         2 POINT (4934.709 3075.913)
#> 1864 1864       5         2 POINT (4954.823 3070.896)
#> 1866 1866       5         2 POINT (4951.338 3054.113)
#> 1867 1867       5         2 POINT (4952.474 3130.228)
#> 1868 1868       5         2 POINT (4958.769 3086.888)
#> 1869 1869       5         2  POINT (4951.975 3185.69)
#> 1870 1870       5         2 POINT (4950.835 3113.254)
#> 1871 1871       5         2 POINT (4949.809 3047.716)
#> 1872 1872       2         2  POINT (4966.83 3247.072)