FlowGRN+: Improving Gene Regulatory Network Inference by Spline Fitting and Manifold Projection in Conditional Flow Matching (Technical Report)

2026-08-10

AI Summary

This study addresses the challenges in inferring gene regulatory networks (GRNs) from single-cell RNA sequencing data, which arise from high dimensionality, dropout noise, and temporal discontinuities in dynamic trajectories. To overcome these issues, the authors propose a novel conditional flow matching framework that integrates spline fitting to construct a stable reference trajectory and introduces a manifold-based local tangent space projection to suppress overshooting. This approach significantly enhances temporal consistency in cellular dynamics modeling and improves GRN inference accuracy. By promoting trajectory smoothness and minimizing manual intervention, the method demonstrates superior performance and higher reproducibility on the BEELINE benchmark compared to existing approaches.

Abstract

Gene regulatory networks (GRNs) are fundamental in understanding cellular dynamics and underlying mechanisms during development and disease. Although scRNA-seq technologies have enabled the collection of vast numbers of gene expression profiles at single-cell resolution, inferring GRNs from scRNA-seq data remains a significant challenge due to high dimensionality and dropout. Recently, FlowGRN has shown promising results in reconstructing cell trajectories and inferring GRNs by applying conditional flow matching (CFM) to learn the cell dynamics. However, FlowGRN still faces limitations in the temporal coherence of reconstructed dynamics and relies on human inspection, which hinders downstream applications and reproducibility. In this paper, we propose FlowGRN+, an improved version of FlowGRN that integrates spline fitting into the CFM framework to generate more stable reference trajectories for training, thereby improving the temporal coherence of the learned dynamics. To address overshooting in spline fitting, we further introduce a projection scheme that projects spline tangents onto the local tangent space of the data manifold. We evaluate FlowGRN+ on the BEELINE benchmark and show improved trajectory smoothness with a competitive GRN inference performance. FlowGRN+ provides a practical framework for reconstructing cell trajectories and inferring GRNs from scRNA-seq data, and the insights from this work may also be useful for other CFM-based models of cellular dynamics.

Problem

• Gene Regulatory Network
• scRNA-seq
• Conditional Flow Matching
• Trajectory Inference
• Temporal Coherence

Innovation

• spline fitting
• manifold projection
• conditional flow matching
• gene regulatory network inference
• single-cell RNA-seq