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

doi:10.1093/bib/bbae562

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

2024-11-04

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DOI：10.1093/bib/bbae562

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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 (kNN) 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.

Keywords:

multi-level

multi-view

shallow views

deep views

graph laplacian filter

contrastive clustering

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

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