scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information
Bibliographic record
Abstract
Abstract While scRNA-seq offers gene expression snapshots, it misses the spatial context of chromatin organization crucial for cell cycle regulation. Single-cell Hi-C, capturing chromatin’s three-dimensional (3D) architecture, fills this void, revealing interactions between genomic regions that transcript-only data might overlook. We introduce scHiCyclePred, a model that utilizes single-cell Hi-C’s multi-scale interaction data to predict cell cycle phases by extracting chromatin’s 3D features. This fusion-prediction model integrates three feature sets into a unified vector. Remarkably, scHiCyclePred outperforms methods like NAGANO and CIRCLET and traditional machine learning techniques across various metrics. Our insights into 3D chromatin dynamics during the cell cycle further underscore its utility. By offering a more comprehensive view of cell cycle dynamics through chromatin structure, scHiCyclePred stands to significantly advance our understanding in cellular biology and holds potential to catalyze breakthroughs in disease research. Access scHiCyclePred at github.com/HaoWuLab-Bioinformatics/scHiCyclePred .
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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 | 0.001 |
| 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) | 0.003 | 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".