HeteroGraphNet: A Ligand–Receptor Informed, Heterophily-Adapted Graph Neural Network for Cell Type Prediction in scRNA-Seq Data
Bibliographic record
Abstract
Graph Neural Networks (GNNs) have emerged as powerful tools for modeling complex relational data, yet most existing architectures assume homophily-where connected nodes share similar features-an assumption that does not hold in many biological systems. In single-cell RNA sequencing (scRNA-seq) data, intercellular communication networks often exhibit heterophily, with meaningful interactions occurring between dissimilar cell types. Moreover, conventional graph construction in this domain frequently relies on arbitrary similarity thresholds, overlooking biologically validated interaction pathways. We address these limitations with HeteroGraphNet, a heterophily-adapted GNN that incorporates ligand-receptor ($\mathbf{L}-\mathbf{R}$) interactions inferred from scRNA-seq data using LIANA to construct biologically grounded cell-cell graphs. Our model combines a bi-kernel aggregation mechanism-capable of capturing both homophilic and heterophilic signals-with cosine similarity-guided adaptive random walks that dynamically update neighborhoods during training. We further mitigate class imbalance through weighted loss functions, ensuring robust performance across underrepresented cell types. Across six scRNA-seq datasets, HeteroGraphNet consistently outperforms a multi-layer perceptron (MLP), four standard GNNs (GCN, GAT, GraphSAGE, MixHop), and two heterophily-specific GNNs (H2GCN, GBK-GNN), with especially strong gains on low-homophily graphs. These results demonstrate that incorporating ligand-receptor-informed connectivity with adaptive neighborhood exploration enables more accurate and biologically meaningful cell type prediction in heterogeneous single-cell interaction networks, offering a scalable framework for broader biological network analysis.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".