Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.
In: Briefings in Bioinformatics, Jg. 23 (2022-03-01), Heft 2, S. 1-13
Online
academicJournal
Zugriff:
Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new d eep s tructural c lustering method for sc RNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC. [ABSTRACT FROM AUTHOR]
Titel: |
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.
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Autor/in / Beteiligte Person: | Gan, Yanglan ; Huang, Xingyu ; Zou, Guobing ; Zhou, Shuigeng ; Guan, Jihong |
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Zeitschrift: | Briefings in Bioinformatics, Jg. 23 (2022-03-01), Heft 2, S. 1-13 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 1467-5463 (print) |
DOI: | 10.1093/bib/bbac018 |
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