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

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.