VeloTree: Inferring single-cell trajectories from RNA velocity fields with varifold distances

TL;DR

VeloTree is a new method that infers cell differentiation trajectories from RNA velocity fields using varifold distances, improving accuracy in reconstructing differentiation trees from single-cell data.

Contribution

It introduces a novel cell dissimilarity measure based on squared varifold distance for robust trajectory inference from RNA velocity fields.

Findings

Accurately recovers differentiation trees on simulated datasets.

Outperforms existing methods on real single-cell datasets.

Abstract

Trajectory inference is a critical problem in single-cell transcriptomics, which aims to reconstruct the dynamic process underlying a population of cells from sequencing data. Of particular interest is the reconstruction of differentiation trees. One way of doing this is by estimating the path distance between nodes -- labeled by cells -- based on cell similarities observed in the sequencing data. Recent sequencing techniques make it possible to measure two types of data: gene expression levels, and RNA velocity, a vector that quantifies variation in gene expression. The sequencing data then consist in a discrete vector field in dimension the number of genes of interest. In this article, we present a novel method for inferring differentiation trees from RNA velocity fields using a distance-based approach. In particular, we introduce a cell dissimilarity measure defined as the squared varifold distance between the integral curves of the RNA velocity field, which we show is a robust estimate of the path distance on the target differentiation tree. Upstream of the dissimilarity measure calculation, we also implement comprehensive routines for the preprocessing and integration of the RNA velocity field. Finally, we illustrate the ability of our method to recover differentiation trees with high accuracy on several simulated and real datasets, and compare these results with the state of the art.