Additional file 1 of Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures
Bibliographic record
Abstract
Additional file 1: Fig S1. Single-cell RNA-seq statistics for 12 patient-derived cell lines. Fig S2. Impact of dose-response curves from in vitro cell viability assays on IC50 estimates. Fig S3. Training and validation loss. Fig S4. CaDRReS-Sc accurately estimates aggregate IC50 values in the presence of transcriptomic heterogeneity. Fig S5. Survival analysis for clusters based on bulk transcriptomic profiles. Fig S6. Boxplots comparing ITTH scores across clinical response categories for various cancer drugs. Fig S7. Additional performance evaluation per drug. Fig S8. Pairwise comparison of CaDRReS-SC’s performance on unseen cell types. Fig S9. Transcriptomic patterns of cells from HN120 and HN137. Fig S10. Detailed comparison between predicted and observed cell death percentages. Fig S11. Pharmacogenomic space of GDSC cell lines and HNSC patient-derived cell clusters. Fig S12. Comparison of observed and predicted drug response across 5 pooled PDCs and 8 drugs. Fig S13. Predictive performance of ElasticNet and RWEN based on cell clusters. Fig S14. Comparison of drug response between tumor types and pathway activity groups.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.748 | 0.153 |
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".