scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data
Bibliographic record
Abstract
Abstract Cell clustering plays a crucial role in the analysis of single-cell multi-omics research. Despite many methods for multi-omics integrated clustering, challenges such as noise, data sparsity, and biointerpretability analysis hider effective clustering. Recent studies have demonstrated that anchor graph learning clustering in the multiview graph domain can help alleviate sparsity and high noise, reducing runtime costs. However, addressing the heterogeneity and high noise levels in multi-omics data is crucial for obtaining a more representative anchor graph. Furthermore, the existing methods often directly obtain surface information from multi-omics for clustering purposes, neglecting the mining and utilization of higher-order correlations among different features within shared information. In response to these challenges, we propose scAGCI, a cell clustering method based on anchor graphs that integrates both scRNA-seq and scATAC-seq data. Our method captures specific and shared anchor graphs representing the properties of omics data in the process of dynamic anchor unification, and mines high-order shared information to complete the omics representation. Subsequently, clustering results are obtained by integrating the specific and shared omics representation. Extensive experiments show that our method not only outperforms 13 state-of-the-art methods in terms of clustering metrics and running time, but also that the completed omics retains the biological meaning of the original omics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".