PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs
Bibliographic record
Abstract
Abstract Predicting the transcriptional response of chemical perturbations is crucial to understanding gene function and developing drug candidates, promising a streamlined drug development process. Single-cell sequencing has provided an ideal data basis for training machine learning models for this task. Recent advances in deep learning have led to significant improvements in predictions of chemical as well as genetic perturbations at the single cell level. Experiments have shown that different cell types exhibit distinct transcriptional patterns and responses to perturbation. This poses a fundamental problem for predicting transcriptional responses of drugs or cell types outside the training data. Accordingly, existing methods lack cell-type-specific modeling or do not explicitly provide an interpretable mechanism for the gene features. In this study, we introduce a novel approach that employs a network representation of various cell types as an inductive bias, improving prediction performance in scenarios with limited data while acknowledging cellular differences. We applied our framework to four small-scale single-cell perturbation datasets and one large-scale screening experiment, demonstrating that this representation can inherently generalize to previously unseen cell types. Furthermore, our method outperforms the state-of-the-art methods in predicting the post-perturbation response in unobserved cell types.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".