Detect the spatial community of each cell — detectCommunity • imcRtools

Function to detect the spatial community of each cell as proposed by Jackson et al., The single-cell pathology landscape of breast cancer, Nature, 2020. Each cell is clustered based on its interactions as defined by a spatial object graph.

detectCommunity(
  object,
  colPairName,
  size_threshold = 0,
  group_by = NULL,
  name = "spatial_community",
  cluster_fun = "louvain",
  BPPARAM = SerialParam()
)

Arguments

object: a SingleCellExperiment or SpatialExperiment object
colPairName: single character indicating the colPair(object) entry containing the neighbor information.
size_threshold: single positive numeric that specifies the minimum number of cells per community. Defaults to 0.
group_by: single character indicating that spatial community detection will be performed separately for all unique entries to colData(object)[,group_by].
name: single character specifying the name of the output saved in colData(object). Defaults to "spatial_community".
cluster_fun: single character specifying the function to use for community detection. Options are all strings that contain the suffix of an igraph community detection algorithm (e.g. "walktrap"). Defaults to "louvain".
BPPARAM: a BiocParallelParam-class object defining how to parallelize computations. Applicable when group_by is specified and defaults to SerialParam(). For reproducibility between runs, we recommend defining RNGseed in the BiocParallelParam-class object.

Value

returns an object of class(object) containing a new column entry to colData(object)[[name]].

Spatial community detection procedure

1. Create an igraph object from the edge list stored in colPair(object, colPairName).

2. Perform community detection using the specified cluster_fun algorithm.

3. Store the community IDs in a vector and replace all communities with a size smaller than size_threshold by NA.

Optional steps: Specify group_by to perform spatial community detection separately for all unique entries to colData(object)[,group_by] e.g. for tumor and non-tumor cells.

References

Jackson et al., The single-cell pathology landscape of breast cancer, Nature, 2020

Author

Lasse Meyer (lasse.meyer@uzh.ch)

Examples

library(cytomapper)
library(BiocParallel)
data(pancreasSCE)

sce <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb",
                        type = "expansion",
                        name = "neighborhood",
                        threshold = 20)
#> The returned object is ordered by the 'ImageNb' entry.

## Detect spatial community 
set.seed(22)
sce <- detectCommunity(sce, 
                      colPairName = "neighborhood", 
                      size_threshold = 10)

plotSpatial(sce,
            img_id = "ImageNb",
            node_color_by = "spatial_community",
            scales = "free")

            
## Detect spatial community - specify group_by
sce <- detectCommunity(sce, 
                       colPairName = "neighborhood", 
                       group_by = "CellType", 
                       size_threshold = 10,
                       BPPARAM = SerialParam(RNGseed = 22)) 

plotSpatial(sce,
            img_id = "ImageNb",
            node_color_by = "spatial_community",
            scales = "free")