FlowGRN: Scalable and Dropout-Robust Gene Regulatory Network Inference via Flow Matching-Based Trajectory Reconstruction (Technical Report)

2026-08-10

AI Summary

Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing data is challenging due to the absence of temporal information and high levels of dropout noise. This work proposes a novel approach that, for the first time, integrates conditional flow matching with score matching to enable robust reconstruction of dynamic cellular trajectories. The method further incorporates a scalable dynGENIE3 framework for GRN inference and introduces a dropout-robust cell similarity metric. Evaluated on the BEELINE benchmark, the proposed method achieves state-of-the-art performance across both synthetic and real datasets. Ablation studies confirm the effectiveness of each core component, demonstrating their individual contributions to the overall improvement in GRN inference accuracy.

Abstract

Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data offers insights into cellular behavior, but is complicated by the lack of temporal information and the prevalence of dropout noise. To address these challenges, we present FlowGRN, a method that integrates conditional flow matching and score matching for robust trajectory reconstruction with dynGENIE3 for scalable GRN inference. FlowGRN incorporates a novel cell similarity measure that is resilient to dropout effects in high-dimensional scRNA-seq data. Evaluation on the BEELINE benchmark demonstrates that FlowGRN achieves state-of-the-art performance on both synthetic and experimental datasets. Ablation studies validate the importance of both the dropout-robust similarity measure and the trajectory reconstruction step, highlighting FlowGRN's ability to accurately model dynamic regulatory relationships.

Problem

• gene regulatory network
• single-cell RNA sequencing
• dropout noise
• temporal information
• trajectory reconstruction

Innovation

• flow matching
• gene regulatory network inference
• dropout-robust similarity
• trajectory reconstruction
• single-cell RNA-seq