Cell neighbourhood detection

A function to identify the neighbourhood of each cell. If sort = TRUE, the neighbourhoods are also sorted such that cells belonging to the same 'initial cluster' as the index cell are arranged closer to it.

Usage

neighbourDetect(spe, samples, NN = 30, sort = TRUE, threads = 1)

Arguments

spe

SpatialExperiment object with initial cluster and subcluster labels.

samples

a character indicating name of colData(spe) column containing sample names.

NN

an integer for the number of neighbouring cells the function should consider. The value must be greater than or equal to 1. Default value is 30.

sort

a logical parameter for whether to sort the neighbourhood by initial clusters. Default value is TRUE.

threads

a numeric value for the number of CPU cores to be used for the analysis. Default value set to 1.

Value

a list containing two items:

1. nnCells, a character matrix of NN nearest neighbours - rows are index cells and columns are their nearest neighbours ranging from closest to farthest neighbour. For sort = TRUE, the neighbours belonging to the same initial cluster as the index cell are moved closer to it.

2. regXclust, a list of vectors of each cell's neighbourhood composition indicated by the proportion of initial subclusters it contains.

Examples

data(ClustSignal_example)

out_list <- clustSIGNAL::neighbourDetect(spe, samples = "sample_id")
#> [1] "Regions defined. Time 08:51:44"
out_list |> names()
#> [1] "nnCells"   "regXclust"