Find ROIs based on cell type-specific densities via graph-based method.

Usage

findROI(
  spe,
  coi,
  probs = 0.85,
  ngrid.min = 20,
  method = "walktrap",
  diag.nodes = FALSE,
  sequential.roi.name = TRUE,
  directed = FALSE,
  zoom.in = FALSE,
  zoom.in.size = 500L,
  ...
)

Arguments

spe: A SpatialExperiment object.
coi: A character vector of cell types of interest (COIs).
probs: A numeric scalar. The threshold of proportion that used to filter grid by density. Default to 0.85.
ngrid.min: An integer. The minimum number of grids required for defining a ROI. Default to 20.
method: The community dectection method to be used, possible options are walktrap, connected, hdbscan, eigen or greedy. Default to walktrap, can be abbreviated.
diag.nodes: Logical. Set this to TRUE to allow diagonal grid points to be adjacent nodes.
sequential.roi.name: Logical. Set this to FALSE if you want the original ROI name before filtering are retained.
directed: Logical. For graph-based approaches, whether to build a directed graph.
zoom.in: Logical. For very large ROIs, whether to zoom in and try to get more refined ROIs.
zoom.in.size: A numeric scaler. Smallest size of an ROI to be able to zoom in. Default is 500L.
...: Other parameters that passed to walktrap.community when method = "walktrap".

Value

A SpatialExperiment object.

Examples


data("xenium_bc_spe")

coi <- c("Breast cancer", "Fibroblasts")

spe <- gridDensity(spe, coi = coi)

spe <- findROI(spe, coi = coi, method = "walktrap")