hdbscan.seurat: Initial clustering for evaluating integration

In zhiyhu/CIDER: Meta-Clustering for Single-Cell Data Integration and Evaluation

Initial clustering for evaluating integration

Description

This function applies HDBSCAN, a density-based clustering method, on the corrected dimension reduction.

Usage

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

Arguments

seu	a Seurat object containing integrated or batch corrected PCA.
reduction	Character. Name of the dimension reduction after integration or batch correction. (Default: PCA)
dims	Numeric vector. Dimensions used for initial clustering. (Default: 1:15)
minPts	Interger. Minimum size of clusters. Will be passed to the 'hdbscan' function. (Default: 25)

Value

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

See Also

Usage of this function should be followed by getIDEr and estimateProb.

zhiyhu/CIDER documentation built on Feb. 4, 2025, 1:09 a.m.