Comparing the ability of embedding methods on metabolic hypergraphs for capturing taxonomy-based features.

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Metabolic networks model biochemical reactions using pairwise compound interactions.
• Traditional graph representations miss complex, multi-compound reactions.
• Hypergraphs provide a more accurate model for multi-participant metabolic reactions.

Purpose of the Study:

• To evaluate the impact of graph and hypergraph embedding methods on unsupervised clustering of metabolic hypergraphs.
• To identify embedding strategies that best capture taxonomy-based classes in metabolic data.
• To inform the selection of embedding methods for downstream biological analyses.

Main Methods:

• Applied 14 distinct embedding strategies to 8467 metabolic hypergraphs.
• Utilized hierarchical clustering with a fixed linkage method post-embedding.
• Compared clustering results against established taxonomic groupings for performance assessment.

Main Results:

• The choice of hypergraph embedding method significantly influences clustering outcomes.
• Bag of Hyperedges (Jaccard distance), Histogram Cosine Kernel, and Hypergraph Auto-Encoder demonstrated superior performance.
• Embedding method selection should be guided by the specific goals of the downstream task.

Conclusions:

• Hypergraph representations enhance the modeling of metabolic complexity.
• Effective embedding strategies are crucial for accurate unsupervised clustering of metabolic hypergraphs.
• The study provides a framework for selecting optimal embedding methods in metabolic network analysis.