A Unified Framework for Lineage Tracing and Trajectory Inference

Abstract

Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. New measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, they been approached from separate directions: methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa. We present a novel, unified framework for lineage tracing and trajectory inference. Specifically, we develop a method for reconstructing developmental trajectories from time courses with snapshots of both cell states and lineages, leveraging mathematical tools from graphical models and optimal transport. We find that lineage data helps disentangle complex state transitions with increased accuracy using fewer measured time points. Moreover, integrating lineage tracing with trajectory inference in this way enables accurate reconstruction of developmental pathways that are impossible to recover with state-based methods alone.