Gene expression inference based on graph neural networks using L1000 data

Gene expression profiles can serve as proxies for cellular states and provide valuable insights into the discovery of functional connections across diverse cellular contexts. A cost-effective method called L1000 has been developed to generate gene expression profiles for over a million different conditions. Since gene expression inference of this method relies on linear regression, nonlinear regression methods, including deep learning models, have been assessed. However, these approaches process gene expression data as a vector structure, motivating us to investigate whether nonlinear models based on a graph structure are more effective in capturing the relationships between genes underlying gene expression profiles. In this work, we show that the graph neural network (GNN) model with genes as nodes outperforms both linear and nonlinear non-GNN models in predicting gene expression values and expression-based gene rankings. Importantly, our GNN model requires ~10-fold less information than other models to achieve comparable performance. A strategic selection of input features, or incorporating an organ feature, from which the gene expression data are derived, further improves gene expression inference performance of the GNN model. Additionally, we evaluate the cross-platform generality of gene expression inference. Our study demonstrates that the transformation of RNA expression data into a graph structure effectively captures nonlinear correlations between genes, thereby enabling highly accurate and efficient prediction of gene expression profiles.