RedFuMOS: A novel approach for multi -omics and clinical data-driven patient stratification
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Background
Patient stratification from multi -omics and clinical data is essential for uncovering disease heterogeneity and moving toward more personalized treatment strategies. However, integrating heterogeneous data layers while identifying robust patient strata remains challenging.
Methods
We introduce Reduced Fusion of Multi- Omics Stratification (RedFuMOS), a novel three-step approach for patient stratification based on mixed-type multi -omics data. RedFuMOS extends Similarity Network Fusion to accommodate mixed-type data layers and layer-specific similarity measures for data integration, includes a dimensionality reduction step to mitigate the curse of dimensionality, and performs patient stratification using density-based hierarchical clustering with HDBSCAN. It also implemented an automated optimization procedure to identify the best set of hyperparameters, minimizing the need for manual tuning.
Results
RedFuMOS outperformed six state-of-the-art tools for multi -omics patient stratification in a comprehensive simulated benchmarking study, which also confirmed that, although computationally expensive, the dimensionality reduction step is crucial for achieving good stratification performance. Additionally, RedFuMOS identified two clinically relevant patient strata in a small real-world cohort of patients with Philadelphia chromosome-positive chronic myeloid leukaemia.
Conclusion
RedFuMOS provides a flexible framework for integrating heterogeneous multi -omics and clinical data. RedFuMOS is available as an R package at http://github.com/delucasara/RedFuMOS .
