arXiv subjects

Tsz Pan Tong

Publications and source records attributed to Tsz Pan Tong.

4 recordsLinked to original sources

arXiv2026

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

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.

cs.CE↗

arXiv2026

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

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.

cs.CE↗

arXiv2025

Kolmogorov-Arnold Network for Gene Regulatory Network Inference

Gene regulation is central to understanding cellular processes and development, potentially leading to the discovery of new treatments for diseases and personalized medicine. Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data presents significant challenges due to its high dimensionality and complexity. Existing tree-based models, such as GENIE3 and GRNBOOST2, demonstrated scalability and explainability in GRN inference, but they cannot distinguish regulation types nor effectively capture continuous cellular dynamics. In this paper, we introduce scKAN, a novel model that employs a Kolmogorov-Arnold network (KAN) with explainable AI to infer GRNs from scRNA-seq data. By modeling gene expression as differentiable functions matching the smooth nature of cellular dynamics, scKAN can accurately and precisely detect activation and inhibition regulations through explainable AI and geometric tools. We conducted extensive experiments on the BEELINE benchmark, and scKAN surpasses and improves the leading signed GRN inference models ranging from 5.40\% to 28.37\% in AUROC and from 1.97\% to 40.45\% in AUPRC. These results highlight the potential of scKAN in capturing the underlying biological processes in gene regulation without prior knowledge of the graph structure.

cs.CE↗

arXiv2024

Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data

We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generation sequencing enable detailed yet destructive gene expression assays at the single-cell level, resulting in the loss of cell evolutionary trajectories. Due to technological and cost constraints, single-cell experiments often feature cells sampled at irregular and sparse time points with a small sample size. Although trajectory-based structural inference models can accurately reveal the underlying interaction graph from observed data, their efficacy depends on the inputs of thousands of regularly sampled trajectories. The irregularly-sampled nature of single-cell data precludes the direct use of these powerful models for reconstructing GRNs. Optimal transport, a classical mathematical framework that minimize transportation costs between distributions, has shown promise in multi-omics data integration and cell fate prediction. Utilizing OT, our method constructs mappings between consecutively sampled cells to form cell-level trajectories, which are given as input to a structural inference model that recovers the GRN from single-cell data. Through case studies in two synthetic datasets, we demonstrate the feasibility of our proposed method and its promising performance over eight state-of-the-art GRN inference methods.

cs.CE↗