Attention-based deep clustering method for scRNA-seq cell type identification

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not availab...

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Vydané v:PLoS computational biology Ročník 19; číslo 11; s. e1011641
Hlavní autori: Li, Shenghao, Guo, Hui, Zhang, Simai, Li, Yizhou, Li, Menglong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 01.11.2023
Public Library of Science (PLoS)
Predmet:
Algorithms
Biological research
Breast cancer
Cell fusion
Cells
Clustering
Computational linguistics
Data visualization
Datasets
Deep learning
Embedding
Gene expression
Gene sequencing
Genetic aspects
Graph neural networks
Heterogeneity
Identification and classification
Language processing
Methods
Natural language interfaces
Representations
RNA sequencing
China
ISSN:1553-7358, 1553-734X, 1553-7358
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Shrnutí:Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.
Bibliografia:new_version
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ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1011641