WENDY: Covariance Dynamics Based Gene Regulatory Network Inference

TL;DR

WENDY is a novel method for inferring gene regulatory networks from single-cell time-series data by modeling covariance dynamics, outperforming existing methods on synthetic and experimental datasets.

Contribution

WENDY introduces a covariance dynamics modeling approach specifically designed for single-cell gene expression data with interventions and unknown distributions.

Findings

WENDY performs well on synthetic data.

WENDY outperforms existing methods on experimental data.

Covariance dynamics modeling effectively captures gene regulatory relationships.

Abstract

Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.