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

## TL;DR

DeepCE is a deep learning method that improves the accuracy of gene regulatory network inference from single-cell RNA sequencing data.

## Contribution

DeepCE introduces a novel framework combining bidirectional gated recurrent units and CNNs for enhanced GRN inference.

## Key findings

- DeepCE outperforms existing methods in GRN inference with higher AUROC and AUPR scores.
- The model effectively smooths noisy data and extracts time-lagged regulatory signals.
- Experiments on mouse and human datasets confirm its strong performance.

## Abstract

Single-cell RNA sequencing has substantially advanced our understanding of gene expression dynamics and cellular heterogeneity. In recent years, deep learning (DL) 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.

We propose DeepCE, a DL framework for correlation-enhanced gene regulatory network (GRN) inference. DeepCE strengthens the extraction of dynamic regulation by integrating bidirectional gated recurrent units with convolutional neural networks (CNNs). Specifically, bidirectional gated recurrent units captures dynamic temporal dependencies, while CNNs focuses on local spatial patterns within single-cell data, enabling the model to uncover complex gene-gene interactions and generate high-quality GRNs. This framework improves the accuracy and robustness of GRN 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.

Codes for DeepCE are free available in the GitHub https://github.com/sxiaodai/DeepCE.