Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering

Abstract Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular pers...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 25; no. 6
Main Authors: Shu, Zhenqiu, Xia, Min, Tan, Kaiwen, Zhang, Yongbing, Yu, Zhengtao
Format: Journal Article
Language:English
Published: England Oxford University Press 23.09.2024
Oxford Publishing Limited (England)
Subjects:
Algorithms
Cellular structure
Cluster Analysis
Clustering
Computational Biology - methods
Graphical representations
Humans
Learning
Machine Learning
Problem Solving Protocol
RNA-Seq - methods
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Single-Cell Gene Expression Analysis
Source code
contrastive clustering
multi-level
deep views
multi-view
graph laplacian filter
shallow views
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae562