Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease

Xing, J.; Staklinski, S. J.; Liu, Z.; Nowak, D.; Siepel, A.

doi:10.64898/2026.06.25.734628

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bioRxiv · 10.64898/2026.06.25.734628

Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease

Xing, J.·Staklinski, S. J.·Liu, Z.·Nowak, D.·Siepel, A.

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Abstract

Gene expression changes along cell lineages, but most single-cell RNA-seq analyses treat cells as independent snapshots and ignore their phylogenetic relationships. Here we present LaVOUS, a lineage-aware probabilistic framework for modeling sparse single-cell gene-expression counts on reconstructed lineage trees. LaVOUS couples Brownian motion and Ornstein-Uhlenbeck models of latent transcriptional dynamics with negative-binomial observation models and scalable variational inference, enabling likelihood-based tests for gene-expression heritability, branch-specific expression shifts, and ancestral expression reconstruction. In simulations, LaVOUS improved detection of lineage-associated expression changes over Gaussian phylogenetic models and accurately reconstructed expression histories across expression levels. Applied to lineage-resolved single-cell datasets from metastatic lung cancer, class-switching B cells, and the developing brain, LaVOUS identified expression changes associated with metastatic progression, isotype switching, and neuronal differentiation. LaVOUS provides a general framework for studying single-cell expression dynamics across development and disease.

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Xing, J., Staklinski, S. J., Liu, Z., Nowak, D., Siepel, A.. 2026-06-28. Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease. https://doi.org/10.64898/2026.06.25.734628

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