Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data
<|reference_start|>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.<|reference_end|>
@article{tong2024integrating,
    title={Integrating Optimal Transport and Structural Inference Models for GRN
  Inference from Single-cell Data},
    author={Tsz Pan Tong, Aoran Wang, George Panagopoulos, Jun Pang},
    journal={arXiv preprint arXiv:2409.15080},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15080},
    primaryClass={cs.CE}
}