Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

Document Type

Article

Publication Date

12-1-2021

Abstract

Clustering is a critical step in single cell-based studies. Most existing methods support unsupervised clustering without the a priori exploitation of any domain knowledge. When confronted by the high dimensionality and pervasive dropout events of scRNA-Seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicates cell type assignment. In such cases, the only recourse is for the user to manually and repeatedly tweak clustering parameters until acceptable clusters are found. Consequently, the path to obtaining biologically meaningful clusters can be ad hoc and laborious. Here we report a principled clustering method named scDCC, that integrates domain knowledge into the clustering step. Experiments on various scRNA-seq datasets from thousands to tens of thousands of cells show that scDCC can significantly improve clustering performance, facilitating the interpretability of clusters and downstream analyses, such as cell type assignment.

Identifier

85103409311 (Scopus)

Publication Title

Nature Communications

External Full Text Location

https://doi.org/10.1038/s41467-021-22008-3

e-ISSN

20411723

PubMed ID

33767149

Issue

1

Volume

12

Grant

CIE160021

Fund Ref

National Science Foundation

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