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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".