  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.
