Initial Clustering for Evaluating Integration

This function applies HDBSCAN, a density-based clustering algorithm, to the corrected dimension reduction of a Seurat object.

Usage

hdbscan.seurat(
  seu,
  batch.var = "Batch",
  reduction = "pca",
  dims = seq_len(15),
  minPts = 25
)

seu: A Seurat object containing integrated or batch-corrected data (e.g. PCA results).
batch.var: Character string specifying the metadata column that contains batch information. Default is "Batch".
reduction: Character string specifying the name of the dimension reduction to use (e.g. "PCA"). Default is "PCA".
dims: Numeric vector indicating the dimensions to be used for initial clustering. Default is 1:15.
minPts: Integer specifying the minimum number of points required to form a cluster. This value is passed to the hdbscan function. Default is 25.

A Seurat object with two additional columns in its meta.data: dbscan_cluster and initial_cluster.