MOH: A Novel Multilayer Multi-Omics Heterogeneous Graph for Single-Cell Clustering
Bibliographic record
Abstract
Cell clustering is crucial in single-cell multi-omics research for identifying distinct cellular populations. Although there has been progress in integrating multi-omics data for clustering, combining more than two types of omics data remains challenging due to the diversity and heterogeneity of these datasets. Traditional approaches typically use heterogeneous graphs that integrate only two types of omics data, constructing graphs with genes and cells as nodes and a single type of edge representing their relationships. However, this method has limitations as it overlooks cell-cell interactions and struggles to capture complex cellular dynamics. Additionally, the graph structure must be redesigned whenever new omics data are introduced, limiting the scalability of these models. To address these issues, we introduce MOH, a novel single-cell clustering algorithm based on a multilayer multi-omics heterogeneous graph. MOH integrates three key single-cell omics types: scRNA-seq, scATAC-seq, and spatial transcriptomics. It constructs a multilayer heterogeneous graph to simultaneously extract and enhance representations from all three omics layers, incorporating both intra-layer and inter-layer edges to capture association and similarity relationships. This enriched representation leads to an accurate clustering results. Extensive experiments show that MOH outperforms six state-of-the-art methods on unsupervised clustering metrics, offering a precise and comprehensive analysis with consistent improvements across all evaluation criteria. Moreover, downstream analyses validate the results, revealing novel biological insights into immune disorder complications in cancer, cancer drug repurposing, and new signaling pathways, which merit further investigation and validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".