Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning

Document Type

Article

Publication Date

1-1-2023

Abstract

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.

Identifier

85149999013 (Scopus)

Publication Title

Genome Research

External Full Text Location

https://doi.org/10.1101/gr.277068.122

e-ISSN

15495469

ISSN

10889051

PubMed ID

36849204

First Page

232

Last Page

246

Issue

2

Volume

33

Grant

ACI-1548562

Fund Ref

National Science Foundation

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