Understanding how gene expression evolves along somatic cell lineages is central to processes such as development and tumor progression. Recent single-cell technologies now enable joint measurement of gene expression and lineage relationships, opening new opportunities to study temporal dynamics. Continuous trait models, such as Brownian Motion and Ornstein-Uhlenbeck processes, provide a natural framework, yet their applicability in single-cell contexts remains largely unexplored.

Here, we evaluate these models under realistic scenarios of somatic evolution and introduce an observation model that explicitly accounts for scRNA-seq noise. Using simulations based on age-dependent branching processes, we generated lineage trees across biologically motivated scenarios and assessed model recovery under different selective regimes.

We show that inference is sensitive to transition diagram structure, cell type uncertainty, and errors in tree reconstruction, often leading to overestimation of selection. Critically, methods that ignore the observation process are strongly biased by expression noise, often overestimating the effect of selection.

Overall, our results highlight the need for caution when interpreting the results of these models in single-cell settings, where technical noise, model misspecification, and uncertainty in lineage reconstruction can strongly influence inferred evolutionary dynamics. Explicitly modeling the observation process is essential for robust phylogenetic inference from single-cell gene expression data.

Co-authors: Antoine Zwaans, Daniele Silvestro, Tanja Stadler