Inferring Gene Regulatory Networks from Single Cell RNA-seq Temporal Snapshot Data Requires Higher Order Moments
Single cell RNA-sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. To date, this aspiration remains unrealised due to technical- and computational challenges. In this work, we focus on the latter, which is under-represented in the literature. We took a systemic approach by subdividing the GRN inference into three fundamental components: the data pre-processing, the feature extraction, and the inference. We saw that the regulatory signature is captured in the statistical moments of scRNA-seq data, and requires computationally intensive minimisation solvers to extract. Furthermore, current data pre-processing might not conserve these statistical moments. Though our moment-based approach is a didactic tool for understanding the different compartments of GRN inference, this line of thinking-finding computationally feasible multi-dimensional statistics of data-is imperative for designing GRN inference methods.