Subset-Contrastive Multi-Omics Network Embedding
This work addresses scalability and structural limitations in multi-omics network analysis for researchers in bioinformatics, though it appears incremental as it builds on existing contrastive learning techniques.
The authors tackled the problem of memory-intensive and structurally-limited network-based analyses in multi-omics data by proposing SCONE, a scalable subgraph contrastive learning method that achieves synergistic omics integration for cell type clustering and performs comparably to state-of-the-art in bulk multi-omics integration with limited data views.
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.