Integration of multi-omics data is challenging due to high dimensionality and non-linear relationships. Here, authors develop an unsupervised method that leverages UMAP embeddings and density-based clustering to integrate diverse omics data types and identifies biologically meaningful patterns across multiple benchmarks.
- Pol Castellano-Escuder
- Derek K. Zachman
- Matthey D. Hirschey
