A new gene regulatory network reconstruction method based on single cell sequencing and transfer entropy

Abstract

In systems biology, gene regulatory networks can well reveal complex biological systems and dynamic biological processes. Traditional gene regulatory network reconstruction methods ignore dynamic biological processes. A gene regulatory network reconstruction method based on transfer entropy and single-cell dynamic time series data is proposed to solve this problem. And from a theoretical point of view, the possibility and rationality of transfer entropy in the application of big data and causal reasoning are proved in detail. Firstly, the method construct pseudo-time series dynamic gene expression data based on trajectory inference. Secondly, The transfer entropy is used to calculate the directional transfer information between paired genes, and screen the major gene regulatory relationships . Finally, remove the indirect gene regulation relationship according to the data processing inequality, and construct the gene regulation network . This method takes the single cell sequencing data of early mouse embryo blood development as an example, and selects TENET and DynGENIE3 algorithms as comparisons, which proves the feasibility and effectiveness of this method from the theoretical and experimental perspectives. The experimental results not only identify the key cell development regulatory factors and regulatory relationships, but also consume less time. It can not only provide important reference value and assumptions for biological disturbance experiment, but also reduce the time cost and research cost of biological experiment.