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Calculate UMAP cluster hulls

Source: R/hc_cluster_hulls.R
hc_cluster_hulls.Rd

hc_cluster_hulls() computes density-based cluster hulls on UMAP embeddings stored within an AnnDatR object. It identifies subclusters using DBSCAN, estimates density, filters based on cumulative density and fraction limits, and generates concave hulls for each cluster.

Usage

hc_cluster_hulls(
  AnnDatR,
  n = 300,
  cum_z_lim = 0.95,
  frac_lim = 0.05,
  plot_range = rep(c(min(c(AnnDatR[["obs"]][["UMAP1"]], AnnDatR[["obs"]][["UMAP2"]])),
    max(c(AnnDatR[["obs"]][["UMAP1"]], AnnDatR[["obs"]][["UMAP2"]]))), 2) * 1.05,
  poly_concavity = 1,
  poly_smoothing = 1,
  relative_bandwidth = 1/200
)

Arguments

AnnDatR

An AnnDatR object containing UMAP embeddings and cluster assignments.

n

Number of grid points for density estimation (default is 300).

cum_z_lim

Cumulative density limit for filtering (default is 0.95).

frac_lim

Minimum fraction limit for subcluster inclusion (default is 0.05).

plot_range

Range of the plot in the format c(xmin, xmax, ymin, ymax) (default is 1.05 times the min and max of UMAP1 and UMAP2).

poly_concavity

Controls the concavity of the polygons (default is 1). Higher values result in less detailed polygons.

poly_smoothing

Controls the smoothing of the polygons (default is 1). Higher values result in less detailed polygons.

relative_bandwidth

Relative bandwidth for density estimation as a fraction of the plot diagonal (default is 1/200).

Value

AnnDatR object with UMAP cluster hulls within the AnnDatR object.

Examples

# Calculate UMAP cluster hulls on an AnnDatR object after UMAP and clustering
adata_res <- hc_pca(example_adata, components = 40)
adata_res <- hc_distance(adata_res, components = 20)
adata_res <- hc_snn(adata_res, neighbors = 15)
#> Building SNN based on a provided distance matrix
#> Computing SNN
adata_res <- hc_cluster_consensus(adata_res, resolution = 7)
#> Iteration: 0 *** value: 948.279
#> Iteration: 1 *** value: 70.5063
#> Iteration: 2 *** value: 22.246
#> Iteration: 3 *** value: 22.2447
#> Iteration: 4 *** value: 22.2447
#> Minimum: 22.2447
#> Joining with `by = join_by(cons_cluster)`
adata_res <- hc_umap(adata_res)
adata_res <- hc_cluster_hulls(adata_res)
head(adata_res$uns$UMAP_hulls$hulls)
#> # A tibble: 6 × 7
#>   cluster sub_cluster sub_type landmass      X     Y polygon_id
#>   <chr>         <dbl> <chr>       <dbl>  <dbl> <dbl> <chr>     
#> 1 17                1 primary         1 -0.994 0.432 17_1_1    
#> 2 17                1 primary         1 -1.00  0.439 17_1_1    
#> 3 17                1 primary         1 -1.00  0.446 17_1_1    
#> 4 17                1 primary         1 -0.994 0.453 17_1_1    
#> 5 17                1 primary         1 -0.987 0.446 17_1_1    
#> 6 17                1 primary         1 -0.973 0.446 17_1_1