Three Ways to Integrate Multi-Omics, and When to Use Each

There is no single best way to integrate multi-omics data — there are three families, each strongest at a different job. Choosing well matters more than picking the newest method.

Linear / traditional

Methods like MOFA, iCluster, SNF, and DIABLO assume the shared signal lives in a low-dimensional subspace. They are interpretable and sample-efficient, which makes them the right default for bulk cohorts: factor and driver discovery, patient subtyping, and sparse biomarker panels. When you have a few hundred patients and want to know which axis of variation separates them and which omics layer drives it, this is where you start.

Knowledge- and graph-based

Methods like GLUE, PrimeKG/TxGNN, and PARADIGM inject known biology — regulatory links, pathways, knowledge graphs — so that integration is guided by mechanism rather than correlation alone. They excel when the modalities don't even share a feature space (genes vs. chromatin peaks vs. drugs), when you need a pathway-level or mechanistic readout, and for knowledge-driven drug repurposing.

Foundation-model / "virtual cell"

Models such as scGPT, Geneformer, UCE, and the Arc Institute's State model pretrain on hundreds of millions of cells and shine at atlas-scale single-cell integration and at predicting responses to unseen perturbations (GEARS, CPA). They are the frontier — but not a free lunch.

How they complement — and a discipline

The categories chain rather than compete: linear methods discover factors, subtypes, and candidate biomarkers; knowledge graphs anchor those signals to pathways and nominate targets; foundation models then simulate perturbations in silico to prioritize experiments — and the results loop back to refine the priors. Knowledge even flows into the learned models (GEARS uses a gene–gene graph; GLUE a regulatory prior), improving data efficiency.