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Cited by 19Single-cell and spatial transcriptomicsGene expression and cancer classificationCell Image Analysis Techniques

scAMAC: Self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder

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Tan, Dayu, Yang, Cheng, Wang, Jing, Su, Yansen, Zheng, Chunhou·2024·Briefings in Bioinformatics
DOI 10.1093/bib/bbae068PMC PMC10905526
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Abstract

Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.

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  • Initial trials using adaptive multi-scale autoencoders on single-cell RNA sequencing data have shown that self-supervised clustering methods are highly beneficial for downstream analysis, such as cell trajectory.

    “Initial trials using adaptive multi-scale autoencoders on single-cell RNA sequencing data have shown that these self-supervised clustering methods are highly beneficial for downstream analysis, such as cell trajectory [157].”

    From Genetic Insights into the Economic Toll of Cell Line Misidentification: A Comprehensive Review (2026) · cites this paper as [157]

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Cited by

Sorted by citations

Authors
Tan, D., Yang, C., Wang, J., Su, Y., & Zheng, C.
Affiliations
  • Anhui University (CN)
  • Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University , 230601 Hefei, China
Corresponding author
Wang, Jing
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2024-01-22
Volume
25
Issue
2
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbae068
PMC
PMC10905526
OpenAlex
W4392393054
Cited by
19
References
2
Citations by year
2026: 4 · 2025: 12 · 2024: 3
FWCI
2.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Key Research and Development Program of China (2021YFE0102100)
  • National Natural Science Foundation of China (62303014, 62172002, 62302007)
  • University Synergy Innovation Program of Anhui Province (GXXT-2022-035)
  • Anhui Provincial Natural Science Foundation (2108085QF267, 2308085QF225)
  • Education Department of Anhui Province (2023AH050061)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Keywords
self-supervised learning, multi-scale attention mechanism, adaptive feedback mechanism, membership matrix, data reconstruction, cell trajectory inference
MeSH terms
Data Analysis, Single-Cell Gene Expression Analysis, Algorithms, Cluster Analysis, Sequence Analysis, RNA, Gene Expression Profiling

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