  Spatial context is central to understanding health and disease. Yet reference
protein interaction networks lack such contextualization, thereby limiting the
study of where protein interactions likely occur in the human body and how they
may be altered in disease. Contextualized protein interactions could better
characterize genes with disease-specific interactions and elucidate diseases'
manifestation in specific cell types. Here, we introduce AWARE, a graph neural
message passing approach to inject cellular and tissue context into protein
embeddings. AWARE optimizes for a multi-scale embedding space, whose structure
reflects network topology at a single-cell resolution. We construct a
multi-scale network of the Human Cell Atlas and apply AWARE to learn protein,
cell type, and tissue embeddings that uphold cell type and tissue hierarchies.
We demonstrate AWARE's utility on the novel task of predicting whether a
protein is altered in disease and where that association most likely manifests
in the human body. To this end, AWARE outperforms generic embeddings without
contextual information by at least 12.5%, showing AWARE's potential to reveal
context-dependent roles of proteins in disease.
