GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
This addresses the need for scalable and adaptable cell alignment methods in single-cell genomics, which is crucial for advancing treatments and precision medicine, though it appears incremental relative to existing neural OT solvers.
The paper tackles the problem of aligning single-cell genomics data by developing a flexible optimal transport method that learns stochastic maps with relaxed constraints, demonstrating its effectiveness in cell development studies, drug response modeling, and cross-modality translation.
Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However, single-cell sequencing technologies are inherently destructive and can only measure a limited array of data modalities simultaneously. This limitation underscores the need for new methods capable of realigning cells. Optimal transport (OT) has emerged as a potent solution, but traditional discrete solvers are hampered by scalability, privacy, and out-of-sample estimation issues. These challenges have spurred the development of neural network-based solvers, known as neural OT solvers, that parameterize OT maps. Yet, these models often lack the flexibility needed for broader life science applications. To address these deficiencies, our approach learns stochastic maps (i.e. transport plans), allows for any cost function, relaxes mass conservation constraints and integrates quadratic solvers to tackle the complex challenges posed by the (Fused) Gromov-Wasserstein problem. Utilizing flow matching as a backbone, our method offers a flexible and effective framework. We demonstrate its versatility and robustness through applications in cell development studies, cellular drug response modeling, and cross-modality cell translation, illustrating significant potential for enhancing therapeutic strategies.