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

TL;DR

This paper presents a novel method combining optimal transport with structural inference models to improve gene regulatory network inference from irregular, sparse single-cell data, demonstrating promising results over existing methods.

Contribution

The paper introduces a new approach that integrates optimal transport with structural inference for GRN reconstruction from challenging single-cell datasets.

Findings

Successfully reconstructs GRNs from synthetic single-cell data

Outperforms eight state-of-the-art GRN inference methods

Demonstrates feasibility and promising accuracy in case studies

Abstract

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.