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Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding

Oncogene volume 43, pages 2279–2292 (2024)Cite this article

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Abstract

Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.

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Fig. 1: The overview of the proposed method scRISE.
Fig. 2: Simulated experimental analysis of clustering metrics for different number of smoothing iterations.
Fig. 3: Comparison of clustering performance.
Fig. 4: The t-SNE visualization results of embedded representations for scRISE and five other deep learning clustering methods.
Fig. 5: Ablation study for scRISE in 17 real datasets.
Fig. 6: Biological analysis for HNSCC dataset.

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Data availability

The authors declare that all data supporting the findings of this study are available within the article or from the corresponding author upon reasonable request.

Code availability

The scRISE software package and source code are available in Github (https://github.com/LiLab-ssruan/scRISE).

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Funding

This work was supported in part by the National Natural Science Foundation of China (grants 82425104 and 82150208 to HL, 82173690 to SLL); the National Key R&D Program of China (2022YFC3400501, 2022YFC3400504); SLL is also sponsored by the Shanghai Rising-Star Program (23QA1402800).

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Author notes

  1. These authors contributed equally: Jinxin Xie, Shanshan Ruan.

Authors and Affiliations

  1. Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China

    Jinxin Xie, Shanshan Ruan, Mingyan Tu, Zhen Yuan, Jianguo Hu, Honglin Li & Shiliang Li

  2. Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, Shanghai, 200062, China

    Honglin Li & Shiliang Li

  3. Lingang Laboratory, Shanghai, 200031, China

    Honglin Li

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  1. Jinxin Xie
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Contributions

Jinxin Xie: Conceptualization, methodology, software, writing - original draft. Shanshan Ruan: Data curation, formal analysis, visualization, writing - review & editing. Mingyan Tu: Validation, investigation, software. Zhen Yuan, Jianguo Hu: Investigation. Honglin Li, Shiliang Li: Supervision, project administration, funding acquisition.

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41388_2024_3074_MOESM1_ESM.docx

Supporting Information for Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding

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Xie, J., Ruan, S., Tu, M. et al. Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding. Oncogene 43, 2279–2292 (2024). https://doi.org/10.1038/s41388-024-03074-5

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