Bibliographic record
Abstract
Single-cell RNA sequencing (scRNA-seq) measures gene expression in individual cells, offering deep insight into cellular heterogeneity, development, and disease. Transformer-based foundation models have become central to single-cell RNA-sequencing analysis, yet most rely on uniform random masking during pretraining, a strategy misaligned with the sparsity, heterogeneity, and zero inflation characteristic of scRNA-seq data. To assess how these models behave under realistic biological variation, we first perform a comprehensive evaluation of four widely used single-cell foundation models (Geneformer, scBERT, scFoundation, and scGPT) across three diverse datasets. This benchmarking reveals substantial variability in model performance, including systematic weaknesses on rare cell populations and degraded accuracy in clinically challenging conditions. Motivated by the broader limitations of random masking in Foundation models, we introduce Multinomial Attention Masking (MAM), a biologically informed masking strategy that leverages trainable latent representations and cross-attention to identify informative gene positions during pretraining. Across all datasets, models pretrained with MAM consistently achieve higher downstream cell-type classification accuracy than those trained with uniform masking and, in several cases, outperform the original pretrained backbones. Biological validation further demonstrates that MAM preferentially selects highly expressed and functionally meaningful genes, indicating that its improvements stem from capturing biologically relevant structure rather than from increased algorithmic complexity. This work improves the reliability and utility of single-cell foundation models for researchers and clinicians alike.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".