Time-Varying Network Driver Estimation (TNDE) Quantifies Stage-Specific Regulatory Effects From Single-Cell Snapshots

TL;DR

TNDE is a novel computational framework that quantifies dynamic gene regulatory effects over time from single-cell snapshot data, addressing the challenge of understanding stage-specific regulatory mechanisms in biological processes.

Contribution

TNDE introduces a linear Markov model with a shared graph attention encoder and partial optimal transport to accurately infer time-resolved gene drivers from single-cell data.

Findings

Outperforms existing methods on simulated datasets.

Identifies biologically relevant stage-specific driver genes in erythropoiesis.

Provides a quantitative tool for dynamic regulatory mechanism analysis.

Abstract

Identifying key driver genes governing biological processes such as development and disease progression remains a challenge. While existing methods can reconstruct cellular trajectories or infer static gene regulatory networks (GRNs), they often fail to quantify time-resolved regulatory effects within specific temporal windows. Here, we present Time-varying Network Driver Estimation (TNDE), a computational framework quantifying dynamic gene driver effects from single-cell snapshot data under a linear Markov assumption. TNDE leverages a shared graph attention encoder to preserve the local topological structure of the data. Furthermore, by incorporating partial optimal transport, TNDE accounts for unmatched cells arising from proliferation or apoptosis, thereby enabling trajectory alignment in non-equilibrium processes. Benchmarking on simulated datasets demonstrates that TNDE outperforms existing baseline methods across diverse complex regulatory scenarios. Applied to mouse erythropoiesis data, TNDE identifies stage-specific driver genes, the functional relevance of which is corroborated by biological validation. TNDE offers an effective quantitative tool for dissecting dynamic regulatory mechanisms underlying complex biological processes.