Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering

Abstract Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing graph neural networks (GNNs) for cell clust...

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Published in:Briefings in bioinformatics Vol. 26; no. 3
Main Authors: Yu, Xiaoyan, Ren, Yixuan, Xia, Min, Shu, Zhenqiu, Zhu, Liehuang
Format: Journal Article
Language:English
Published: England Oxford University Press 01.05.2025
Oxford Publishing Limited (England)
Subjects:
Algorithms
Animals
Cluster Analysis
Clustering
Complexity
Computational Biology - methods
Gene sequencing
Graph neural networks
Heterogeneity
Humans
Learning
Machine Learning
Multilayer perceptrons
Neural networks
Neural Networks, Computer
Problem Solving Protocol
Representations
RNA-Seq - methods
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Single-Cell Gene Expression Analysis
contrastive learning
decoupled
scRNA-seq data
multi-view
clustering
GNNs
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing graph neural networks (GNNs) for cell clustering, the dependencies between cells expand exponentially with the number of layers. This results in high computational complexity, negatively impacting the model’s training efficiency. To address these challenges, we propose a novel approach, called decoupled GNNs, based on multi-view contrastive learning (scDeGNN), for scRNA-seq data clustering. Firstly, this method constructs two adjacency matrices to generate distinct views, and trains them using decoupled GNNs to derive the initial cell feature representations. These representations are then refined through a multilayer perceptron and a contrastive learning layer, ensuring the consistency and discriminability of the learned features. Finally, the learned representations are fused and applied to the cell clustering task. Extensive experimental results on nine real scRNA-seq datasets from various organisms and tissues show that the proposed scDeGNN method significantly outperforms other state-of-the-art scRNA-seq data clustering algorithms across multiple evaluation metrics.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbaf198