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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.23917 |
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| _version_ | 1866915960555831296 |
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| author | Sarkar, Bitan Ni, Yang |
| author_facet | Sarkar, Bitan Ni, Yang |
| contents | Cell--cell communication (CCC) is commonly inferred from ligand--receptor co-expression, an associational paradigm that cannot distinguish causal signaling from shared regulation or confounding. We propose MR-CCC, a Bayesian Mendelian randomization framework that uses cis-eQTLs as instruments for ligand and receptor expression and explicitly models receptor-modulated ligand effects through an interaction term, so the causal effect of a ligand can vary with receptor abundance. A spike--and--slab prior yields posterior inclusion probabilities quantifying evidence for causal signaling, and an efficient Gibbs sampler provides scalable inference. Benchmarked against naive regression, MVMR, and MR-BMA, MR-CCC controls false discoveries under confounding while retaining high power, and uniquely estimates both the ligand main and receptor-modulated interaction effects. Applied to the OneK1K NK cells $\to$ monocytes axis, MR-CCC identifies eight discoveries across GABA, interferon, interleukin, and prostaglandin signaling, including a stoichiometry-dependent dissociation of the two IL-18 receptor chains and co-discovery of both obligate IFN-$γ$ receptor subunits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23917 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MR-CCC: Bayesian Mendelian Randomization for Causal Cell--Cell Communication Sarkar, Bitan Ni, Yang Methodology Cell--cell communication (CCC) is commonly inferred from ligand--receptor co-expression, an associational paradigm that cannot distinguish causal signaling from shared regulation or confounding. We propose MR-CCC, a Bayesian Mendelian randomization framework that uses cis-eQTLs as instruments for ligand and receptor expression and explicitly models receptor-modulated ligand effects through an interaction term, so the causal effect of a ligand can vary with receptor abundance. A spike--and--slab prior yields posterior inclusion probabilities quantifying evidence for causal signaling, and an efficient Gibbs sampler provides scalable inference. Benchmarked against naive regression, MVMR, and MR-BMA, MR-CCC controls false discoveries under confounding while retaining high power, and uniquely estimates both the ligand main and receptor-modulated interaction effects. Applied to the OneK1K NK cells $\to$ monocytes axis, MR-CCC identifies eight discoveries across GABA, interferon, interleukin, and prostaglandin signaling, including a stoichiometry-dependent dissociation of the two IL-18 receptor chains and co-discovery of both obligate IFN-$γ$ receptor subunits. |
| title | MR-CCC: Bayesian Mendelian Randomization for Causal Cell--Cell Communication |
| topic | Methodology |
| url | https://arxiv.org/abs/2604.23917 |