CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Estévez, Silas Ruhrberg Huynh, Nicolas Liu, Tennison Kortlever, Roderik M. Evan, Gerard I. Bentley, David L. van der Schaar, Mihaela |
| author_facet | Estévez, Silas Ruhrberg Huynh, Nicolas Liu, Tennison Kortlever, Roderik M. Evan, Gerard I. Bentley, David L. van der Schaar, Mihaela |
| contents | Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive measurements preclude direct tracking of individual cells across time, making trajectory inference underdetermined. Optimal Transport (OT) provides a principled framework for snapshot alignment, but a long-standing modeling question is which cost functions yield biologically meaningful couplings. Standard OT approaches rely on gene-expression distances, implicitly treating cells as independent points and neglecting structured cell-cell communication mediated by ligand-receptor signaling. We introduce CellBRIDGE (Cell-Based Regularized Interaction-Driven Gene Expression), which augments feature-based OT with a directed, typed interaction cost derived from ligand-receptor activity. By explicitly modeling cell-cell communication, CellBRIDGE improves cross-snapshot couplings and downstream trajectory estimates across synthetic and real scRNA-seq datasets relative to feature-only baselines. Notably, CellBRIDGE enables mechanistically interpretable in silico perturbations: on lung cancer data, silencing specific ligand-receptor pairs induces trajectory shifts that recapitulate expected effects of targeted pathway inhibition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30635 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment Estévez, Silas Ruhrberg Huynh, Nicolas Liu, Tennison Kortlever, Roderik M. Evan, Gerard I. Bentley, David L. van der Schaar, Mihaela Machine Learning Genomics Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive measurements preclude direct tracking of individual cells across time, making trajectory inference underdetermined. Optimal Transport (OT) provides a principled framework for snapshot alignment, but a long-standing modeling question is which cost functions yield biologically meaningful couplings. Standard OT approaches rely on gene-expression distances, implicitly treating cells as independent points and neglecting structured cell-cell communication mediated by ligand-receptor signaling. We introduce CellBRIDGE (Cell-Based Regularized Interaction-Driven Gene Expression), which augments feature-based OT with a directed, typed interaction cost derived from ligand-receptor activity. By explicitly modeling cell-cell communication, CellBRIDGE improves cross-snapshot couplings and downstream trajectory estimates across synthetic and real scRNA-seq datasets relative to feature-only baselines. Notably, CellBRIDGE enables mechanistically interpretable in silico perturbations: on lung cancer data, silencing specific ligand-receptor pairs induces trajectory shifts that recapitulate expected effects of targeted pathway inhibition. |
| title | CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment |
| topic | Machine Learning Genomics |
| url | https://arxiv.org/abs/2605.30635 |