Heterophily-Aware Hypergraph Neural Networks for Cell Type Prediction Using Ligand-Receptor-Informed Single-Cell RNA-Seq Data
Bibliographic record
Abstract
Accurately predicting cell types from single-cell RNA sequencing (scRNA-seq) data requires modeling complex cellular interactions that extend beyond pairwise transcriptional similarity. Ligand—receptor-mediated signaling is a key driver of such interactions, often spanning diverse cell types and exhibiting both homophilic and heterophilic patterns. In this work, we introduce a biologically informed framework for cell type prediction based on heterophily-aware hypergraph neural networks (HGNNs), where hyperedges represent multi-cell communication events derived from curated ligand—receptor pairs. This construction enables higher-order modeling of intercellular signaling and captures the combinatorial nature of ligand—receptor communication. We evaluate nine state-of-the-art hypergraph-based models—including HGNN, HyperGCN, UniGCNII, HyperND, AllDeepSets, AllSetTransformer, ED-HNN, SheafHyperGNN, and HyperUFG—that encompass diverse message passing paradigms such as spectral convolutions, diffusion dynamics, permutationinvariant set operations, and sheaf-theoretic encoding. Experiments on six benchmark scRNA-seq datasets reveal that architectures tailored to heterophilic structure substantially outperform their homophily-oriented counterparts. Our results underscore the importance of both biologically grounded hypergraph design and heterophily-aware learning in advancing automated cell type annotation for complex tissue systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".