Trajectory inference for a branching SDE model of cell differentiation
Elias Ventre, Aden Forrow, Nitya Gadhiwala, Parijat Chakraborty, Omer, Angel, Geoffrey Schiebinger

TL;DR
This paper extends a trajectory inference method to account for cell proliferation and death using lineage trees, enabling more accurate reconstruction of cell differentiation pathways from high-dimensional gene expression data.
Contribution
It introduces a novel extension of gWOT that incorporates proliferation and death, leveraging lineage tracing data without prior knowledge of rates, with theoretical guarantees.
Findings
Successfully reconstructs differentiation landscapes from simulated data.
Outperforms benchmarks that require true branching rates.
Handles cases with and without cell death/subsampling.
Abstract
A core challenge for modern biology is how to infer the trajectories of individual cells from population-level time courses of high-dimensional gene expression data. Birth and death of cells present a particular difficulty: existing trajectory inference methods cannot distinguish variability in net proliferation from cell differentiation dynamics, and hence require accurate prior knowledge of the proliferation rate. Building on Global Waddington-OT (gWOT), which performs trajectory inference with rigorous theoretical guarantees when birth and death can be neglected, we show how to use lineage trees available with recently developed CRISPR-based measurement technologies to disentangle proliferation and differentiation. In particular, when there is neither death nor subsampling of cells, we show that we extend gWOT to the case with proliferation with similar theoretical guarantees and…
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Taxonomy
TopicsGene expression and cancer classification · Single-cell and spatial transcriptomics · Genomics and Phylogenetic Studies
