detectCommunity: Detect the spatial community of each cell

In BodenmillerGroup/imcRtools: Methods for imaging mass cytometry data analysis

Detect the spatial community of each cell

Description

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.

Usage

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.

Author(s)

Lasse Meyer ([e-mail])

References

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

Examples

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

sce <- buildSpatialGraph(pancreasSCE, img_id = "ImageNb",
                        type = "expansion",
                        name = "neighborhood",
                        threshold = 20)

## 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")
           

BodenmillerGroup/imcRtools documentation built on Oct. 30, 2024, 12:19 p.m.