Data from: Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations

This study applies a graph neural network (GNN)-based approach to
investigate metabolic perturbations in mouse liver transcriptomic data
following toxicant exposure. A mouse-specific metabolic reaction network
was constructed from Reactome, replacing the human network used in prior
models. Publicly available transcriptomic datasets (n = 7,903 control
samples across 26 tissues) were curated from Recount3 for model training
and validation. Test datasets (n = 299) included liver samples from mice
exposed to the environmental toxicant 2,3,7,8-tetrachlorodibenzo-p-dioxin
(TCDD). Gene counts were filtered to retain only those linked to known
metabolic reactions and transformed using DESeq2. Principal component
analysis (PCA) was applied to genes per reaction, with the first principal
component (PC1) used as node features. A GNN architecture using PyTorch
Geometric with GraphConv layers and global mean pooling was trained to
classify tissue type and later adapted via transfer learning for toxicant
response classification. Integrated Gradients were used to estimate the
importance of individual edges in the reaction network, and network
centrality measures identified key reactions. Comparative differential
gene expression and enrichment analyses were performed to contextualize
GNN findings. All data were obtained from public sources and all code is
available on GitHub.