hdbscan.seurat

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

A workflow of (a) meta-clustering based on
inter-group similarity measures and (b) a ground-truth-free test metric to
assess the biological correctness of integration in real datasets. See Hu Z,
Ahmed A, Yau C (2021) <doi:10.1101/2021.03.29.437525> for
more details.

Zhiyuan Hu

CIDER

Meta-Clustering for Single-Cell Data Integration and Evaluation

Christopher Yau

hdbscan.seurat function

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

Arguments

Initial clustering for evaluating integration — hdbscan.seurat

<dl>

<dt>seu</dt>
<dd>a Seurat object containing integrated or batch corrected
PCA.</dd>


<dt>reduction</dt>
<dd>Character. Name of the dimension reduction after
integration or batch correction. (Default: PCA)</dd>


<dt>dims</dt>
<dd>Numeric vector. Dimensions used for initial clustering.
(Default: 1:15)</dd>


<dt>minPts</dt>
<dd>Interger. Minimum size of clusters. Will be passed
to the `hdbscan` function. (Default: 25)</dd>

</dl>

hdbscan.seurat: Initial clustering for evaluating integration

Description

Usage

Value

Arguments

See Also