Inferring gene regulatory network via fusing gene expression image and RNA-seq data

Abstract

Abstract Motivation Recently, with the development of high-throughput experimental technology, reconstruction of gene regulatory network (GRN) has ushered in new opportunities and challenges. Some previous methods mainly extract gene expression information based on RNA-seq data, but the associated information is very limited. With the establishment of gene expression image database, it is possible to infer GRN from image data with rich spatial information. Results First, we propose a new convolutional neural network (called SDINet), which can extract gene expression information from images and identify the interaction between genes. SDINet can obtain the detailed information and high-level semantic information from the images well. And it can achieve satisfying performance on image data (Acc: 0.7196, F1: 0.7374). Second, we apply the idea of our SDINet to build an RNA-model, which also achieves good results on RNA-seq data (Acc: 0.8962, F1: 0.8950). Finally, we combine image data and RNA-seq data, and design a new fusion network to explore the potential relationship between them. Experiments show that our proposed network fusing two modalities can obtain satisfying performance (Acc: 0.9116, F1: 0.9118) than any single data. Availability and implementation Data and code are available from https://github.com/guofei-tju/Combine-Gene-Expression-images-and-RNA-seq-data-For-infering-GRN.