Merge overlapping spots

Given a stitched SpatialExperiment-class, merge overlapping (same array coordinates) spots by adding expression (i.e. from assays(spe)$counts), returning a SpatialExperiment with at most one spot per array location.

Usage

merge_overlapping(spe)

Arguments

spe

A SpatialExperiment-class with colData(spe) columns array_row, array_col, key, group, and capture_area.

Value

A SpatialExperiment with at most one spot per array location

Details

colData(spe) and spatialCoords(spe) of the merged spots are taken from the spots whose exclude_overlapping values are TRUE.

Author

Nicholas J. Eagles

Examples

if (!exists("spe")) {
    spe <- spatialLIBD::fetch_data(type = "visiumStitched_brain_spe")
}
#> 2024-11-08 18:53:28.036147 loading file /github/home/.cache/R/BiocFileCache/6c1f1cbefd45_visiumStitched_brain_spe.rds%3Frlkey%3Dnq6a82u23xuu9hohr86oodwdi%26dl%3D1

#   Group colData by group and array coordinates
grouped_coldata = colData(spe) |>
    dplyr::as_tibble() |>
    dplyr::group_by(group, array_row, array_col)

#   Find the first 100 keys that overlap other spots and don't, respectively
overlapping_keys = grouped_coldata |>
    dplyr::filter(dplyr::n() > 1) |>
    dplyr::slice_head(n = 2) |>
    dplyr::ungroup() |>
    dplyr::slice_head(n = 100) |>
    dplyr::pull(key)
nonoverlapping_keys = grouped_coldata |>
    dplyr::filter(dplyr::n() == 1) |>
    dplyr::ungroup() |>
    dplyr::slice_head(n = 100) |>
    dplyr::pull(key)

#   Built a small SPE containing some overlaps and some non-overlapping spots
small_spe = spe[, c(overlapping_keys, nonoverlapping_keys)]

#   Merge overlapping spots
small_spe_merged = merge_overlapping(small_spe)
#> Warning: Dropping assays other than 'counts' for merging.
#> Warning: Dropped reducedDims(spe) for merging
#> 'sample_id's are duplicated across 'SpatialExperiment' objects to cbind; appending sample indices.

#   All array coordinates have just one unique spot after merging
colData(small_spe_merged) |>
    dplyr::as_tibble() |>
    dplyr::group_by(group, array_row, array_col) |>
    dplyr::summarize(n = dplyr::n()) |>
    dplyr::pull(n) |>
    table()
#> `summarise()` has grouped output by 'group', 'array_row'. You can override
#> using the `.groups` argument.
#> 
#>   1 
#> 142