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

2026-04-01

AI Summary

This study addresses the challenge of reconstructing cellular differentiation trajectories—particularly differentiation trees—from single-cell transcriptomic data by proposing a novel RNA velocity–based approach. It introduces, for the first time, the varifold metric into RNA velocity analysis by computing the squared varifold distance between integral curves derived from the velocity field, thereby defining a robust cell-to-cell dissimilarity measure resilient to perturbations in differentiation paths. The method integrates velocity field preprocessing, integral curve generation, and distance computation, and efficiently infers differentiation trees using distance-based phylogenetic techniques. Extensive evaluations on multiple simulated and real datasets demonstrate that the proposed approach achieves significantly higher reconstruction accuracy than existing state-of-the-art methods, confirming the effectiveness and superiority of the introduced dissimilarity metric.

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.

Problem

• trajectory inference
• single-cell transcriptomics
• differentiation trees
• RNA velocity
• cell differentiation

Innovation

• RNA velocity
• trajectory inference
• varifold distance
• differentiation tree
• single-cell transcriptomics