identify_neighborhoods: identify_neighborhoods

In TrigosTeam/SPIAT: Spatial Image Analysis of Tissues

identify_neighborhoods

Description

Uses Euclidean distances to identify neighborhoods of cells. Three clustering methods are available, including hierarchical clustering, dbscan, and (Rphenograph).

Usage

identify_neighborhoods(
  spe_object,
  method = "hierarchical",
  cell_types_of_interest,
  radius,
  min_neighborhood_size = 10,
  k = 100,
  feature_colname,
  no_pheno = NULL
)

Arguments

spe_object	SpatialExperiment object in the form of the output of format_image_to_spe.
method	String. The clustering method. Choose from "hierarchical", "dbscan" and "Rphenograph". (Note Rphenograph function is not available for this version yet).
cell_types_of_interest	String Vector of phenotypes to consider.
radius	Numeric specifying the radius of search. Need to specify when 'method' is "hierarchical" or "dbscan".
min_neighborhood_size	Numeric. The minimum number of cells within each cluster. Need to specify when 'method' is "hierarchical" or "dbscan".
k	Numeric. The parameter for "Rphenograph" method.
feature_colname	String. Column from which the cell types are selected.
no_pheno	Cell type corresponding to cells without a known phenotype (e.g. "None", "Other")

Value

An spe object and a plot is returned. The spe object contains information of the defined neighborhood under "Neighborhood" column. The cells of interest that do not form clusters are labelled "Free_cell", cells not of interest are labelled 'NA'.

Examples

neighborhoods <- identify_neighborhoods(image_no_markers, method = "hierarchical",
min_neighborhood_size = 100, cell_types_of_interest = c("Immune", "Immune1", "Immune2"),
radius = 50, feature_colname = "Cell.Type")

TrigosTeam/SPIAT documentation built on Aug. 22, 2024, 7:50 p.m.