DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data

Motivation Single-cell RNA sequencing has substantially advanced our understanding of gene expression dynamics and cellular heterogeneity. In recent years, deep learning has emerged as a promising approach to infer genetic regulation. However, these methods still face challenges in representing complex regulatory mechanisms. Thus, it remains imperative to develop new algorithms to enhance both effectiveness and reliability. Results We propose DeepCE, a deep learning framework for correlation-enhanced gene regulatory network inference. DeepCE strengthens the extraction of dynamic regulation by integrating bidirectional gated recurrent units with convolutional neural networks. Specifically, bidirectional gated recurrent units captures dynamic temporal dependencies, while convolutional neural networks focuses on local spatial patterns within single-cell data, enabling the model to uncover complex gene-gene interactions and generate high-quality gene regulatory networks. This framework improves the accuracy and robustness of gene regulatory network inference by smoothing noisy gene expression data, extracting time-lagged regulatory signals, and filtering out spurious correlations. Experiments conducted on mouse and human datasets demonstrate the strong performance of DeepCE. Performance evaluations show that DeepCE outperforms existing methods, achieving the highest AUROC and AUPR scores. Availability and implementation Codes for DeepCE are free available in the GitHub https://github.com/sxiaodai/DeepCE. Supplementary information Supplementary data are available at Bioinformatics Advances online.