Predicting gene expression from network topology using graph neural networks

Motivation: It is known that the structure of transcription and protein interaction networks is informative of its biological function at multiple scales. However, thus far it has not been possible to systematically connect network topology to gene expression in a quantitative way. Results: We investigated whether there is a relationship between interaction networks and gene expression values by using a graph convolutional auto-encoder and two end-to-end learning approaches for three interaction networks and hundreds of experimental conditions in the model organism \textit{E.\ coli}. Graph neural networks use a message passing framework to learn an embedding of a graph in a continuous space, either using network topology alone, or including additional node features. We found that graph embeddings trained on transcription and PPI networks can explain more than 50 and 40 percent, respectively, of the variance in gene expression data, thus confirming the relationship between network structure and gene expression value. Additionally, for the task of predicting gene expression values using GNNs, with and without additional expression training data, we found that the message passing scheme of GNNs is able to obtain the lowest mean squared error between the tested models both in prediction of unseen test values, and in an auto-encoder scheme for reconstruction of the feature matrix of expression values.