Inference of Gene Regulatory Networks from Single-Cell Transcriptomics by scATA: an All-to-All Approach

Gene regulatory networks (GRNs) model the controlling interactions between genes, where the ex- pression of some genes activate or inhibit the expression of other genes. In the study of biomedical systems, a better understanding of the system can be achieved by knowing the underlying GRN in different conditions (e.g. health/disease or control/mutant). Generally, the underlying GRN of the system is not known, and it is inferred from transcriptomic data by computational methods. Single- cell transcriptomic measurements have been developed and exponentially improved over the last decade. These recent experimental techniques can measure the expression of almost each gene for most of the individual cells in a sample and have been widely used to study the heterogeneity of biological systems. However, there are not many computational methods available to infer GRNs from this type of data and the existing ones suffer from major limitations. Thus, there is a need for the development of computational approaches to infer GRNs from single-cell transcriptomics. The aim of this thesis is to develop a simple and scalable method that can infer GRNs from single-cell transcriptomic time series data by studying pairwise regulations between genes. The presented method, named single-cell All-to-All (scATA), is based on estimating the parameters of a stochastic linear differential equation that describes the regulation between each pair of regulator and target genes, one pair at a time while ignoring other genes. The parameters are estimated by solving an optimization problem that minimizes the Wasserstein distance between the simulated distribution of the target gene and the corresponding time series data. The simulated distribution is obtained by numerically integrating a stochastic differential equation several times to obtain a distribution of the regulated gene trajectories. The developed method was tested on synthetic data simulated from different network models with different sizes and topologies up to 10 genes, with AUROC between 0.65 and 0.91 for 5- genes networks and between 0.54 and 0.71 for 10-genes networks. The shape of the ROC curves show that, with scATA, we are able to identify a few links with high confidence. To evaluate the applicability and performance of the algorithm on experimental data, the method was applied to infer the GRN of a publicly available, single-cell transcriptomic time series data, with a publicly available GRN compiled from literature. The use of this tool can provide new insights into the regulatory mechanism inside biological systems. It can propose novel key connections between genes to be validated experimentally, that, if verified, could be useful in better understanding the underlying system and in developing targeted treatments. This thesis is as proof of concept that dynamical model-based pairwise approaches, previously used in bulk transcriptomics, can also be used for GRN inference using single-cell time series.