Uses Euclidean distances to identify neighborhoods of cells. Three clustering methods are available, including hierarchical clustering, dbscan, and Rphenograph.
Usage
identify_neighborhoods(
sce_object,
method = "hierarchical",
cell_types_of_interest,
radius,
min_neighborhood_size = 10,
k = 100,
feature_colname,
no_pheno = NULL
)
Arguments
- sce_object
SingleCellExperiment object in the form of the output of
format_image_to_sce
.- method
String. The clustering method. Choose from "hierarchical", "dbscan" and "Rphenograph".
- 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")
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")