Inferring single-cell gene regulatory network by non-redundant mutual information.

Gene regulatory network plays a crucial role in controlling the biological processes of living creatures. Deciphering the complex gene regulatory networks from experimental data remains a major challenge in system biology. Recent advances in single-cell RNA sequencing technology bring massive high-resolution data, enabling computational inference of cell-specific gene regulatory networks (GRNs). Many relevant algorithms have been developed to achieve this goal in the past years. However, GRN inference is still less ideal due to the extra noises involved in pseudo-time information and large amounts of dropouts in datasets. Here, we present a novel GRN inference method named Normi, which is based on non-redundant mutual information. Normi manipulates these problems by employing a sliding size-fixed window approach on the entire trajectory and conducts average smoothing strategy on the gene expression of the cells in each window to obtain representative cells. To further alleviate the impact of dropouts, we utilize the mixed KSG estimator to quantify the high-order time-delayed mutual information among genes, then filter out the redundant edges by adopting Max-Relevance and Min Redundancy algorithm. Moreover, we determined the optimal time delay for each gene pair by distance correlation. Normi outperforms other state-of-the-art GRN inference methods on both simulated data and single-cell RNA sequencing (scRNA-seq) datasets, demonstrating its superiority in robustness. The performance of Normi in real scRNA-seq data further reveals its ability to identify the key regulators and crucial biological processes.