Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data

Recent advances in single-cell sequencing and CRISPR/Cas9-based genome engineering have enabled the simultaneous profiling of single-cell lineage and transcriptomic state. Together, these simultaneous assays allow researchers to build comprehensive phylogenetic models relating all cells and infer transcriptomic determinants of subclonal behavior. Yet, these assays are limited by the fact that researchers only have access to direct observations at the leaves of these phylogenies and thus cannot rigorously form hypotheses about unobserved, or ancestral, states that gave rise to the observed population. Here, we introduce TreeVAE: a framework that jointly models the observed transcriptomic states using a variational autoencoder (VAE) and the correlations between observations specified by the tree. Using simulations, we demonstrate that TreeVAE outperforms benchmarks in reconstructing ancestral states on several metrics. Moreover, using real data from lung cancer metastasis single-cell lineage tracing, we show that TreeVAE outperforms state-of-the-art models for scRNA-seq data in terms of goodness of fit. TreeVAE appears as a promising model for taking into account correlations between samples within the framework of deep generative models for transcriptomics data, and produces rigorous reconstructions of unobserved cellular states.