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C2DBench is derived from public sources (Norman et al. 2019, GEO GSE133344, via scPerturb; DGIdb 5.0). It contains no raw sequencing data and no human-subject data — K562 is an immortalised cell line. Please confirm the following before access.
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C2DBench — Cell Shift to Drug (Norman v1)
Given two cell populations — untouched controls and cells where genes were switched on — infer what was changed, what else is functionally involved, and what drugs might act on it.
Built from Norman et al. 2019, CRISPR activation in K562. Self-contained: no external files needed.
Task
| Stage | Target | Graded |
|---|---|---|
| List 1 | the activated gene(s) | ✅ list1_gold |
| List 2 | functionally related genes | ✅ list2_gold |
| Drug list | compounds acting on those pathways | ⚠️ drug_reference provided; no efficacy key |
Splits
There is no train/val/test. This is an agentic evaluation set — every row is scored. The perception model (soft-prompt adapter over cell embeddings) is trained elsewhere; nothing here is training data.
| Split | Rows | |
|---|---|---|
eval |
690 | every task item |
reference_library |
105 | one profile per single-gene condition — the agent may consult this |
Within eval, stratify on n_target_genes:
n_target_genes |
Rows | Difficulty |
|---|---|---|
| 2 | 340 | the real test — the pair's combined profile was never observed, only its two constituents, so it must be composed |
| 1 | 310 | near-lookup — the gene's own profile sits in reference_library |
| 0 | 40 | sham (control-vs-control), empty list1_gold — naming genes here is a hallucination |
Every row is the same full trace: cell pair → list 1 → list 2 → drug list.
The difficulty gap is the point. Nearest-centroid solves single-gene items at 0.905; the best trivial method on two-gene items reaches 0.335. Report the two strata separately — a headline number pooled across them mostly measures the easy half.
reference_library is study material, not leakage: composing those profiles into an
unseen pair is the task.
Columns
| Column | |
|---|---|
control_profile, perturbed_profile, delta |
1,983-dim log1p CP10K pseudobulk |
top_up_genes, top_down_genes |
top 25 movers with logFC (JSON) — for text/LLM arms |
list1_gold, list2_gold |
answer keys |
drug_reference |
specificity-ranked drug nominations (JSON, median 20) |
drug_gold |
null — Norman has no drug treatments |
n_target_genes |
2 = real test, 1 = near-lookup, 0 = sham |
effect_tier, snr |
strong / moderate / weak / marginal |
qc_energy_distance, qc_permutation_p |
per-item separation statistics |
leakage_control_target_logfc |
held-out target genes — do not add to features |
control_cell_idx, perturbed_cell_idx |
row offsets into the source h5ad |
Baselines (two-gene items, n=340)
| Method | top-1 | top-5 | top-20 |
|---|---|---|---|
| Additive composition — the bar | 0.335 | 0.603 | 0.732 |
| Chance (5,460 candidate pairs) | 0.00018 | — | — |
run_baseline.py reproduces this from the parquet files alone.
Three things to know
Targets are masked. CRISPRa raises the activated gene's own transcript, so reading
the top mover recovers a true gene 89.6% of the time. All 102 target genes are removed
from every visible field; that baseline then scores 0.000. Unmasked values are kept in
leakage_control_target_logfc for measuring the leak, not for features.
List 2 is measured, not looked up. Two genes are related if activating them produces similar shifts (top-10, cross-half reproducibility 0.847). A GO/Reactome key would be circular — solvers can query those databases. This one recovers known families 9× above chance.
Pairs are lane- and depth-matched. Perturbed cells run ~18% shallower than controls, and some encoders read depth almost perfectly, so unmatched a model can detect "perturbed" from read depth alone. After matching, 98.5% of real pairs separate at p<0.05 with a 0% sham false-positive rate.
Limits
One cell line, one lab, CRISPR activation only. 340 two-gene items ranks methods but won't resolve small differences. Scoring drug efficacy needs measured drug responses (sciPlex, LINCS, Tahoe) — not available in Norman.
Sources
Derived data only; no raw sequencing redistributed.
- Norman TM et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365:786–793 (2019). 10.1126/science.aax4438 · GEO GSE133344
- Peidli S et al. scPerturb: harmonized single-cell perturbation data. Nature Methods (2024) — source of the processed object.
- DGIdb 5.0, Nucleic Acids Research (2024) —
drug_reference, queried once and frozen. DGIdb aggregates upstream sources with varying licences; see dgidb.org before redistributing that column.
K562 is an immortalised cell line, not human-subject material. No embeddings included. Please cite Norman et al. and DGIdb alongside this dataset.
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