| --- |
| license: cc-by-4.0 |
| extra_gated_heading: Access C2DBench |
| extra_gated_prompt: >- |
| 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. |
| extra_gated_fields: |
| Full name: text |
| Email: text |
| Affiliation: text |
| Intended use: text |
| I will cite Norman et al 2019 and DGIdb alongside this dataset: checkbox |
| I understand drug_reference lists are computational nominations, not evidence of efficacy, and will not be used to inform clinical or treatment decisions: checkbox |
| extra_gated_button_content: Request access |
| task_categories: [feature-extraction, question-answering] |
| tags: [single-cell, perturb-seq, crispra, drug-discovery, agents, biology] |
| configs: |
| - config_name: default |
| data_files: |
| - split: eval |
| path: data/eval.parquet |
| - split: reference_library |
| path: data/reference_library.parquet |
| --- |
| |
| # 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](https://doi.org/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](https://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. |
|
|
