Single-cell transcriptomics has transformed our ability to characterize cellular identity, but present-day gene expression profiles capture only a snapshot of a process that unfolds across cell division history. Recent single-cell lineage-tracing technologies make it possible to reconstruct cell division histories for thousands of cells, opening a window into how gene expression evolves. Yet observed gene expression is often redundant, with correlations among genes reflecting underlying latent biological programs and regulatory networks. To capture this structure, we introduce scPFA, a single-cell phylogenetic factor analysis framework that represents gene expression through a small set of latent factors that evolve along lineages under a phylogenetic prior, capturing correlated structure hidden in present-day observations alone. Simulations demonstrate accurate recovery of latent factors and covariance structure across conditions. Applied to developmental and cancer lineage-tracing datasets, the model uncovers biologically interpretable, lineage-associated expression programs. Together, these results demonstrate how lineage-informed factor analysis can reveal temporal biological structure hidden within high-dimensional single-cell data.
Staklinski, S. J., Siepel, A.
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