  Motivation: Molecular interaction networks summarize complex biological
processes as graphs, whose structure is informative of biological function at
multiple scales. Simultaneously, omics technologies measure the variation or
activity of genes, proteins, or metabolites across individuals or experimental
conditions. Integrating the complementary viewpoints of biological networks and
omics data is an important task in bioinformatics, but existing methods treat
networks as discrete structures, which are intrinsically difficult to integrate
with continuous node features or activity measures. Graph neural networks map
graph nodes into a low-dimensional vector space representation, and can be
trained to preserve both the local graph structure and the similarity between
node features.
  Results: We studied the representation of transcriptional, protein-protein
and genetic interaction networks in E. Coli and mouse using graph neural
networks. We found that such representations explain a large proportion of
variation in gene expression data, and that using gene expression data as node
features improves the reconstruction of the graph from the embedding. We
further proposed a new end-to-end graph feature auto-encoder which is trained
on the feature reconstruction task, and showed that it performs better at
predicting unobserved node features than auto-encoders that are trained on the
graph reconstruction task before learning to predict node features. When
applied to the problem of imputing missing data in single-cell RNAseq data, our
graph feature auto-encoder outperformed a state-of-the-art imputation method
that does not use protein interaction information, showing the benefit of
integrating biological networks and omics data using graph representation
learning.
