CellMentor: cell-type aware dimensionality reduction for single-cell RNA-sequencing data
Bibliographic record
Abstract
Single-cell RNA sequencing generates high-dimensional gene expression profiles for individual cells. A key step in its analysis is dimensionality reduction, which transforms these data into lower-dimensional representations for clustering and cell type identification. Current approaches often fail to balance technical noise removal with the preservation of biologically meaningful cell-type signals. Here, we present CellMentor, a fully supervised dimensionality reduction method based on non-negative matrix factorization that integrates cell type labels directly into its optimization objective. CellMentor minimizes variation within known populations while maximizing distinctions between types, producing low-dimensional embeddings optimized for cell type identification. Across diverse simulated and experimental datasets, CellMentor shows superior cell type separation, robust batch correction, and effective detection of rare cell populations, offering a valuable tool for integrative single-cell analyses across experiments. Single-cell analyses often blur biological structure during dimensionality reduction. Here, the authors introduce CellMentor, a supervised NMF framework that learns representations guided by cell-type labels, improving clustering accuracy and preserving meaningful biological variation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".