Subset-Contrastive Multi-Omics Network Embedding

• URL: http://arxiv.org/abs/2504.11321v1
• Date: Tue, 15 Apr 2025 16:01:39 GMT
• Title: Subset-Contrastive Multi-Omics Network Embedding
• Authors: Pedro Henrique da Costa Avelar, Min Wu, Sophia Tsoka,
• Abstract summary: Subset-Contrastive multi-Omics Network Embedding employs contrastive learning techniques on large datasets through a scalable subgraph contrastive approach.<n>Our method demonstrates synergistic omics integration for cell type clustering in single-cell data.
• Score: 6.427316666427534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivation: Network-based analyses of omics data are widely used, and while many of these methods have been adapted to single-cell scenarios, they often remain memory- and space-intensive. As a result, they are better suited to batch data or smaller datasets. Furthermore, the application of network-based methods in multi-omics often relies on similarity-based networks, which lack structurally-discrete topologies. This limitation may reduce the effectiveness of graph-based methods that were initially designed for topologies with better defined structures. Results: We propose Subset-Contrastive multi-Omics Network Embedding (SCONE), a method that employs contrastive learning techniques on large datasets through a scalable subgraph contrastive approach. By exploiting the pairwise similarity basis of many network-based omics methods, we transformed this characteristic into a strength, developing an approach that aims to achieve scalable and effective analysis. Our method demonstrates synergistic omics integration for cell type clustering in single-cell data. Additionally, we evaluate its performance in a bulk multi-omics integration scenario, where SCONE performs comparable to the state-of-the-art despite utilising limited views of the original data. We anticipate that our findings will motivate further research into the use of subset contrastive methods for omics data.

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