Prediction of gene co-expression from chromatin contacts with graph attention network

The technology of high-throughput chromatin conformation capture (Hi-C) allows genome-wide measurement of chromatin interactions. Several studies have shown statistically significant relationships between gene-gene spatial contacts and their co-expression. It is desirable to uncover epigenetic mechanisms of transcriptional regulation behind such relationships using computational modeling. Existing methods for predicting gene co-expression from Hi-C data use manual feature engineering or unsupervised learning, which either limits the prediction accuracy or lacks interpretability.

To address these issues, we propose HiCoEx (Hi-C predicts gene co-expression), a novel end-to-end framework for explainable prediction of gene co-expression from Hi-C data based on graph neural network. We apply graph attention mechanism to a gene contact network inferred from Hi-C data to distinguish the importance among different neighboring genes of each gene, and learn the gene representation to predict co-expression in a supervised and task-specific manner. Then, from the trained model, we extract the learned gene embeddings as a model interpretation to distill biological insights. Experimental results show that HiCoEx can learn gene representation from 3D genomics signals automatically to improve prediction accuracy, and make the black box model explainable by capturing some biologically meaningful patterns, e.g., in a gene contact network, the common neighbors of two central genes might contribute to the co-expression of the two central genes through sharing enhancers.

The source code is freely available at https://github.com/JieZheng-ShanghaiTech/HiCoEx.

Supplementary data are available at Bioinformatics online.