pivot: code + trained checkpoints (norman, replogle k562)

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  1. .gitattributes +2 -1
  2. .gitignore +31 -0
  3. LICENSE +21 -0
  4. README.md +90 -3
  5. experiments/results/gears_norman.json +56 -0
  6. experiments/results/gears_ranking.json +19 -0
  7. experiments/results/norman_ablation_components.json +75 -0
  8. experiments/results/norman_ablation_cost.json +9 -0
  9. experiments/results/norman_ablation_datascale.json +75 -0
  10. experiments/results/norman_ablation_guidance_steps.json +41 -0
  11. experiments/results/norman_ablation_inverse_search.json +41 -0
  12. experiments/results/norman_ablation_matching.json +75 -0
  13. experiments/results/norman_ablation_representation.json +75 -0
  14. experiments/results/norman_ablation_reward.json +35 -0
  15. experiments/results/norman_benchmark.json +231 -0
  16. experiments/results/norman_combinatorial.json +271 -0
  17. experiments/results/norman_distloss.json +27 -0
  18. experiments/results/norman_extra_ablations.json +44 -0
  19. experiments/results/norman_forward_cell.json +0 -0
  20. experiments/results/norman_forward_combination.json +1669 -0
  21. experiments/results/norman_forward_perturbation.json +2845 -0
  22. experiments/results/norman_nom_cell.json +2773 -0
  23. experiments/results/norman_nom_improved.json +295 -0
  24. experiments/results/norman_nom_perturbation.json +1639 -0
  25. experiments/results/norman_timing_scaling.json +89 -0
  26. experiments/results/pivot_vs_gears.json +7 -0
  27. experiments/results/replogle_k562_forward_cell.json +0 -0
  28. experiments/results/replogle_k562_forward_gene.json +0 -0
  29. experiments/results/replogle_k562_forward_perturbation.json +0 -0
  30. experiments/results/replogle_k562_nom_cell.json +2773 -0
  31. experiments/results/replogle_k562_nom_improved.json +981 -0
  32. experiments/results/replogle_k562_nom_perturbation.json +2773 -0
  33. models/norman/config.json +30 -0
  34. models/norman/model.ptw +3 -0
  35. models/norman/train_info.json +427 -0
  36. models/replogle_k562/config.json +30 -0
  37. models/replogle_k562/model.ptw +3 -0
  38. models/replogle_k562/train_info.json +427 -0
  39. requirements.txt +12 -0
  40. scripts/extra_ablations.py +110 -0
  41. scripts/figures.py +172 -0
  42. scripts/gears_compare.py +58 -0
  43. scripts/gears_ranking.py +144 -0
  44. scripts/setup_gears_env.sh +25 -0
  45. scripts/timing_and_scaling.py +115 -0
  46. src/__init__.py +0 -0
  47. src/data/__init__.py +0 -0
  48. src/data/embeddings.py +86 -0
  49. src/data/perturb_data.py +223 -0
  50. src/data/preprocess.py +225 -0
.gitattributes CHANGED
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  *.pb filter=lfs diff=lfs merge=lfs -text
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  *.pickle filter=lfs diff=lfs merge=lfs -text
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  *.pkl filter=lfs diff=lfs merge=lfs -text
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- *.pt filter=lfs diff=lfs merge=lfs -text
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  *.pth filter=lfs diff=lfs merge=lfs -text
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  *.rar filter=lfs diff=lfs merge=lfs -text
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  *.safetensors filter=lfs diff=lfs merge=lfs -text
@@ -33,3 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.pb filter=lfs diff=lfs merge=lfs -text
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  *.pickle filter=lfs diff=lfs merge=lfs -text
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  *.pkl filter=lfs diff=lfs merge=lfs -text
 
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  *.pth filter=lfs diff=lfs merge=lfs -text
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  *.rar filter=lfs diff=lfs merge=lfs -text
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  *.safetensors filter=lfs diff=lfs merge=lfs -text
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ models/norman/model.ptw filter=lfs diff=lfs merge=lfs -text
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+ models/replogle_k562/model.ptw filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # large data & downloads (regenerated from public sources via src/data/preprocess.py)
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+ /data/
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+ /gears_data/
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+ /logs/
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+
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+ # generated artifacts kept out of the repo
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+ /paper/
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+ /figures/
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+ *.h5ad
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+ *.npz
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+ *.npy
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+
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+ # model checkpoints / large binaries
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+ *.pt
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+ *.ckpt
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+
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+ # python
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+ __pycache__/
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+ *.pyc
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+ *.pyo
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+ .ipynb_checkpoints/
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+
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+ # latex build artifacts
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+ paper/*.aux
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+ paper/*.bbl
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+ paper/*.blg
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+ paper/*.out
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+ paper/*.log
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+
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+ # os
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+ .DS_Store
LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ MIT License
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+
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+ Copyright (c) 2026 Bryan Cheng
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+
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
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+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
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+
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+ The above copyright notice and this permission notice shall be included in all
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+ copies or substantial portions of the Software.
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+
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ SOFTWARE.
README.md CHANGED
@@ -1,3 +1,90 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # PIVOT
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+
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+ Perturbation-Informed Vector-field Optimization for Transcriptomic state control.
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+
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+ PIVOT learns a perturbation-conditioned flow map over single-cell state embeddings and
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+ uses its Jacobian for differentiable inverse design: given a control cell state and a
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+ desired target state, it nominates gene-level interventions that move cells toward the
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+ target. The same model also does ordinary forward response prediction.
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+
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+ ## Layout
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+
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+ ```
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+ src/
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+ data/ loading + preprocessing of perturb-seq data, splits
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+ models/ perturbation encoder, flow map, the PIVOT module
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+ training/ training loop and losses
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+ evaluation/ inference, rewards, metrics, baselines
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+ experiments/ drivers for the result tables, ablations, figures
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+ utils/
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+ scripts/ figure generation, extra ablations, GEARS comparison
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+ experiments/ saved result json
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+ ```
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+
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+ ## Setup
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+
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+ ```bash
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+ pip install -r requirements.txt
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+ ```
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+
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+ Data is not committed. Download and preprocess from the public sources first (Norman 2019
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+ and Replogle 2022 are pulled from scPerturb):
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+
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+ ```bash
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+ python -m src.data.preprocess norman
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+ python -m src.data.preprocess replogle_k562
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+ ```
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+
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+ This writes a PCA(50) embedding over 2000 highly variable genes plus the held-out splits to
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+ `data/processed/<dataset>/`.
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+
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+ ## Running things
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+
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+ ```bash
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+ # train one model
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+ python -m src.training.train --dataset norman --split perturbation
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+
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+ # forward + nomination tables
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+ python -m src.experiments.run_tables --dataset norman --tables forward_cell forward_perturbation
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+
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+ # ablations
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+ python -m src.experiments.run_ablations --dataset norman
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+
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+ # figures
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+ python scripts/figures.py
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+ ```
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+
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+ The GEARS head-to-head runs in its own conda env (older torch + pyg), since the package is
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+ finicky about versions:
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+
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+ ```bash
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+ bash scripts/setup_gears_env.sh
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+ conda run -n pivot_gears python scripts/gears_ranking.py
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+ ```
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+
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+ ## Models
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+
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+ Trained PIVOT checkpoints live under `models/`:
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+
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+ - `models/norman/` - trained on Norman 2019 (CRISPRa K562)
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+ - `models/replogle_k562/` - trained on Replogle 2022 (CRISPRi K562)
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+
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+ each folder has `model.ptw` (a plain torch state dict), `config.json` (the training config),
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+ and `train_info.json` (history + run info). loading needs the matching preprocessed dataset,
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+ since the perturbation encoder vocabulary comes from the data:
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+
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+ ```python
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+ import json, torch
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+ from src.data.perturb_data import load_dataset
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+ from src.training.train import TrainConfig, make_model
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+
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+ cfg = TrainConfig(**json.load(open("models/norman/config.json")))
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+ data = load_dataset(cfg.dataset)
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+ model = make_model(data, cfg, device="cpu")
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+ model.load_state_dict(torch.load("models/norman/model.ptw", map_location="cpu"))
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+ model.eval()
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+ ```
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+
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+ ## License
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+
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+ MIT, Bryan Cheng 2026. See `LICENSE`.
experiments/results/gears_norman.json ADDED
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+ {
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+ "n_test_perts": 45,
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+ "test_perts": [
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+ "ARID1A+ctrl",
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+ "ARRDC3+ctrl",
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+ "ATL1+ctrl",
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+ "BAK1+ctrl",
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+ "BCL2L11+BAK1",
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+ "BCL2L11+TGFBR2",
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+ "BCL2L11+ctrl",
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+ "BPGM+ZBTB1",
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+ "C3orf72+FOXL2",
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+ "CBFA2T3+ctrl",
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+ "CBL+TGFBR2",
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+ "CBL+ctrl",
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+ "CEBPB+OSR2",
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+ "CEBPE+PTPN12",
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+ "CKS1B+ctrl",
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+ "CNNM4+ctrl",
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+ "DUSP9+IGDCC3",
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+ "DUSP9+ctrl",
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+ "EGR1+ctrl",
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+ "ETS2+CEBPE",
24
+ "FOXA1+FOXF1",
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+ "FOXF1+HOXB9",
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+ "FOXF1+ctrl",
27
+ "GLB1L2+ctrl",
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+ "HOXC13+ctrl",
29
+ "IGDCC3+PRTG",
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+ "IRF1+SET",
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+ "KLF1+CEBPA",
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+ "KLF1+FOXA1",
33
+ "KMT2A+ctrl",
34
+ "MAP2K3+IKZF3",
35
+ "MAP2K3+MAP2K6",
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+ "MAPK1+ctrl",
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+ "NCL+ctrl",
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+ "PLK4+STIL",
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+ "PTPN12+UBASH3A",
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+ "RREB1+ctrl",
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+ "SNAI1+DLX2",
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+ "TBX3+TBX2",
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+ "TGFBR2+ETS2",
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+ "TGFBR2+ctrl",
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+ "TSC22D1+ctrl",
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+ "UBASH3B+PTPN12",
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+ "ZBTB10+DLX2",
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+ "ZNF318+ctrl"
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+ ],
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+ "metrics": {
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+ "mse": 0.003074304576471655,
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+ "mse_de": 0.10127744511701167,
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+ "pearson": 0.9916384220123291,
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+ "pearson_de": 0.8981071008576287
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+ }
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+ }
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353
+ {
354
+ "map": 9.659211703709193,
355
+ "tan": 60.230314973112826,
356
+ "semi": 0.5836410588258273,
357
+ "reg": 0.19984227941407787,
358
+ "total": 70.1813699301187
359
+ },
360
+ {
361
+ "map": 9.695229802812849,
362
+ "tan": 60.008484233509414,
363
+ "semi": 0.5898574017859125,
364
+ "reg": 0.199969258401301,
365
+ "total": 69.99866515320618
366
+ },
367
+ {
368
+ "map": 9.731371854806875,
369
+ "tan": 60.117312245554736,
370
+ "semi": 0.5865367480686733,
371
+ "reg": 0.20019410569946489,
372
+ "total": 70.14197520466594
373
+ },
374
+ {
375
+ "map": 9.704108560240114,
376
+ "tan": 60.16391328093293,
377
+ "semi": 0.5837481083808007,
378
+ "reg": 0.20012537322261117,
379
+ "total": 70.15991874793907
380
+ },
381
+ {
382
+ "map": 9.649337966720779,
383
+ "tan": 59.84723182777306,
384
+ "semi": 0.595232380675031,
385
+ "reg": 0.2003737132657658,
386
+ "total": 69.7942084770698
387
+ },
388
+ {
389
+ "map": 9.67079952784947,
390
+ "tan": 59.99238888629071,
391
+ "semi": 0.5785262170550111,
392
+ "reg": 0.20029796698650756,
393
+ "total": 69.95247412347175
394
+ },
395
+ {
396
+ "map": 9.637403655361819,
397
+ "tan": 59.95936525022829,
398
+ "semi": 0.5868523740149164,
399
+ "reg": 0.20019558710711344,
400
+ "total": 69.89021766959847
401
+ },
402
+ {
403
+ "map": 9.705964162752226,
404
+ "tan": 59.901117597307476,
405
+ "semi": 0.5755069797689264,
406
+ "reg": 0.20033776256945227,
407
+ "total": 69.8948581249683
408
+ },
409
+ {
410
+ "map": 9.650553046882926,
411
+ "tan": 60.12031802883396,
412
+ "semi": 0.5841203432578546,
413
+ "reg": 0.20030619087931398,
414
+ "total": 70.06295439484832
415
+ },
416
+ {
417
+ "map": 9.67817362252768,
418
+ "tan": 60.0385335947012,
419
+ "semi": 0.5809869928793474,
420
+ "reg": 0.20038042672268755,
421
+ "total": 70.00722394670758
422
+ }
423
+ ],
424
+ "duration_s": 134.94324254989624,
425
+ "device": "cuda:3",
426
+ "n_train_cells": 78128
427
+ }
requirements.txt ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ torch>=2.0
2
+ numpy
3
+ scipy
4
+ scikit-learn
5
+ pandas
6
+ pyarrow
7
+ scanpy
8
+ anndata
9
+ matplotlib
10
+
11
+ # gears is only needed for the head-to-head baseline and lives in a separate env
12
+ # (see scripts/setup_gears_env.sh) because it pins older torch/pyg versions.
scripts/extra_ablations.py ADDED
@@ -0,0 +1,110 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """extra experiments to fill in the runnable table cells. external methods we
2
+ can't reproduce stay unreported; only real computed values get emitted."""
3
+ import sys, os, json, time
4
+ sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
5
+ import numpy as np, torch
6
+ from src.data.perturb_data import load_dataset
7
+ from src.data.splits import load_split
8
+ from src.training.train import TrainConfig, train
9
+ from src.experiments.predictors import PivotPredictor, BaselinePredictor
10
+ from src.experiments.forward_eval import evaluate_forward
11
+ from src.experiments.nomination_eval import evaluate_nomination
12
+ from src.evaluation.baselines import build_baseline
13
+ from src.utils.common import save_json
14
+
15
+ gpu = int(os.environ.get("PIVOT_GPU", "3"))
16
+ data = load_dataset("norman")
17
+ out = {}
18
+
19
+ # ===== core-ablation extras (centroid reward, held-out perturbation) =====
20
+ sp = load_split(data.dir, "perturbation")
21
+ cands = [p for p in data.perturbations if len(data.parse(p)) == 1]
22
+ targets = [p for p in sp["test_perts"] if len(data.parse(p)) == 1 and p in cands][:30]
23
+ gc = data.functional_clusters(seed=0)
24
+
25
+ def fwd_inv(model, reward="centroid"):
26
+ dev = next(model.parameters()).device
27
+ pred = PivotPredictor(model, data, dev)
28
+ f = evaluate_forward(pred, data, list(sp["test_perts"])[:50], data.control_idx, max_perts=50)
29
+ r = evaluate_nomination(pred, data, targets, cands, data.control_idx, reward_kind=reward,
30
+ method="ranking", gene_cluster=gc, device=dev)
31
+ g = evaluate_nomination(pred, data, targets, cands, data.control_idx, reward_kind=reward,
32
+ method="guidance", guidance_init="warm", rerank=False,
33
+ gene_cluster=gc, model=model, device=dev)
34
+ return f, r, g
35
+
36
+ # velocity-only: train with only the tangent (velocity) loss
37
+ mv, _ = train(TrainConfig(dataset="norman", split="perturbation", epochs=60, device_index=gpu,
38
+ components=["tan"]), data=data, verbose=False)
39
+ fv, rv, _ = fwd_inv(mv)
40
+ out["velocity_only"] = {"mse": fv["mse"], "de_corr": fv["de_corr"], "mmd": fv["mmd"],
41
+ "endpoint_dist": rv["endpoint_dist"], "top5": rv["top5"], "ndcg": rv["ndcg"]}
42
+ print("velocity_only", out["velocity_only"], flush=True)
43
+
44
+ # full model: ranking-only and guidance-without-reranking inverse rows
45
+ mf, info = train(TrainConfig(dataset="norman", split="perturbation", epochs=60, device_index=gpu),
46
+ data=data, verbose=False)
47
+ dev = next(mf.parameters()).device
48
+ ff, rf, gf = fwd_inv(mf)
49
+ out["ranking_only"] = {"mse": ff["mse"], "de_corr": ff["de_corr"], "mmd": ff["mmd"],
50
+ "endpoint_dist": rf["endpoint_dist"], "top5": rf["top5"], "ndcg": rf["ndcg"]}
51
+ out["guidance_no_rerank"] = {"mse": ff["mse"], "de_corr": ff["de_corr"], "mmd": ff["mmd"],
52
+ "endpoint_dist": gf["endpoint_dist"], "top5": gf["top5"], "ndcg": gf["ndcg"]}
53
+ print("ranking_only", out["ranking_only"], flush=True)
54
+ print("guidance_no_rerank", out["guidance_no_rerank"], flush=True)
55
+
56
+ # ===== inverse-table baseline: average perturbation effect + ranking (cosine) =====
57
+ bl = BaselinePredictor(build_baseline("AvgPerturbationEffect").fit(data, sp["train_perts"], sp["train_idx"]))
58
+ ra = evaluate_nomination(bl, data, targets, cands, data.control_idx, reward_kind="cosine",
59
+ method="ranking", gene_cluster=gc, device=dev)
60
+ out["avg_effect_ranking"] = {k: ra[k] for k in ["top1", "top5", "ndcg", "func_top5"]}
61
+ out["avg_effect_ranking"]["med_rank"] = float(np.median(ra["_per"]["rank"]))
62
+ print("avg_effect_ranking", out["avg_effect_ranking"], flush=True)
63
+
64
+ # ===== gpu memory for compute table =====
65
+ torch.cuda.reset_peak_memory_stats(dev)
66
+ c0 = torch.as_tensor(data.emb[data.control_idx[:256]], dtype=torch.float32, device=dev)
67
+ from src.evaluation import inference as inf
68
+ _ = inf.endpoint_ranking(mf, data, cands, c0, __import__("src.evaluation.rewards", fromlist=["Reward"]).Reward(
69
+ "centroid", target_c=data.emb[data.pert_to_idx[targets[0]]].mean(0), device=dev), device=dev)
70
+ out["gpu_mem_mb"] = round(torch.cuda.max_memory_allocated(dev) / 1e6, 1)
71
+ print("gpu_mem_mb", out["gpu_mem_mb"], flush=True)
72
+
73
+ # ===== combination table: additive + random + pivot guidance (combination split) =====
74
+ spc = load_split(data.dir, "combination")
75
+ combo_cands = data.combos
76
+ ctgt = [p for p in spc["test_perts"] if len(data.parse(p)) == 2][:26]
77
+ mc, _ = train(TrainConfig(dataset="norman", split="combination", epochs=60, device_index=gpu),
78
+ data=data, verbose=False)
79
+ devc = next(mc.parameters()).device
80
+ predc = PivotPredictor(mc, data, devc)
81
+ from src.experiments.nomination_eval import rank_candidates
82
+ import src.evaluation.metrics as M
83
+ rng = np.random.default_rng(0)
84
+ # additive baseline ranking over observed combos
85
+ addb = build_baseline("Additive").fit(data, spc["train_perts"], spc["train_idx"])
86
+ addp = BaselinePredictor(addb)
87
+ def combo_eval(predictor):
88
+ e1=e5=ov=0.0
89
+ for p in ctgt:
90
+ cstar = data.emb[data.pert_to_idx[p]].mean(0)
91
+ c0n = data.emb[rng.choice(data.control_idx, 128, replace=False)]
92
+ sk = dict(kind="centroid", c_star=cstar, target_sample=data.emb[data.pert_to_idx[p]],
93
+ device=devc, control_ref=data.emb[data.control_idx].mean(0))
94
+ ranked,_ = rank_candidates(predictor, combo_cands, c0n, sk)
95
+ e1 += M.top_k_accuracy(ranked,p,1); e5 += M.top_k_accuracy(ranked,p,5)
96
+ ov += M.partial_overlap(data.parse(ranked[0]), set(data.parse(p)))
97
+ n=len(ctgt); return e1/n, e5/n, ov/n
98
+ ae1, ae5, aov = combo_eval(addp)
99
+ out["combo_additive"] = {"exact1": ae1, "exact5": ae5, "overlap": aov}
100
+ # random combos
101
+ r_e1=r_e5=r_ov=0.0
102
+ for p in ctgt:
103
+ pick = list(rng.choice(combo_cands, 5, replace=False))
104
+ r_e1 += float(p==pick[0]); r_e5 += float(p in pick)
105
+ r_ov += M.partial_overlap(data.parse(pick[0]), set(data.parse(p)))
106
+ n=len(ctgt); out["combo_random"]={"exact1":r_e1/n,"exact5":r_e5/n,"overlap":r_ov/n}
107
+ print("combo_additive", out["combo_additive"], "combo_random", out["combo_random"], flush=True)
108
+
109
+ save_json(out, "experiments/results/norman_extra_ablations.json")
110
+ print("FILL_DONE", flush=True)
scripts/figures.py ADDED
@@ -0,0 +1,172 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """figures for the paper, helvetica-ish font (nimbus sans).
2
+ figure 1: effect-axis embedding panels (control / observed / pivot-predicted).
3
+ figure 2: quantitative results (forward bars, gears head-to-head, dist-loss, reward)."""
4
+ import sys, os, glob, json
5
+ sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
6
+ import numpy as np
7
+ import matplotlib
8
+ matplotlib.use("Agg")
9
+ from matplotlib import font_manager as fm
10
+ import matplotlib.pyplot as plt
11
+ from matplotlib.patches import Patch
12
+ from matplotlib.lines import Line2D
13
+
14
+ # helvetica-family font (nimbus sans = urw helvetica clone)
15
+ for patt in ["/usr/share/fonts/opentype/urw-base35/NimbusSans-*.otf",
16
+ "/usr/share/fonts/truetype/liberation2/LiberationSans-*.ttf"]:
17
+ for f in glob.glob(patt):
18
+ try: fm.fontManager.addfont(f)
19
+ except Exception: pass
20
+ plt.rcParams.update({
21
+ "font.family": "sans-serif",
22
+ "font.sans-serif": ["Nimbus Sans", "Helvetica", "Arial", "Liberation Sans", "DejaVu Sans"],
23
+ "mathtext.fontset": "dejavusans",
24
+ "font.size": 11, "axes.labelsize": 12, "axes.titlesize": 12.5,
25
+ "xtick.labelsize": 10, "ytick.labelsize": 10, "legend.fontsize": 10,
26
+ "axes.linewidth": 0.9, "axes.edgecolor": "#444444",
27
+ "xtick.color": "#444444", "ytick.color": "#444444",
28
+ "axes.labelcolor": "#222222", "text.color": "#222222",
29
+ "figure.dpi": 150, "savefig.dpi": 320, "savefig.bbox": "tight",
30
+ "axes.spines.top": False, "axes.spines.right": False,
31
+ "axes.grid": True, "grid.color": "#E6E8EB", "grid.linewidth": 0.8,
32
+ "axes.axisbelow": True, "legend.frameon": False,
33
+ })
34
+ # palette
35
+ C_CTRL, C_OBS, C_PRED, C_PIVOT, C_BASE, C_ACC = "#B9C2CC", "#2D6FB3", "#E4572E", "#1B9E77", "#9AA6B2", "#6A4C93"
36
+ FIG = "figures"
37
+ RES = "experiments/results"
38
+ os.makedirs(FIG, exist_ok=True)
39
+ def L(n): return json.load(open(f"{RES}/{n}.json"))
40
+ def save(fig, name):
41
+ fig.savefig(f"{FIG}/{name}.png"); fig.savefig(f"{FIG}/{name}.pdf"); plt.close(fig)
42
+ print("wrote", name)
43
+
44
+
45
+ def kde_contour(ax, X, color, levels=4):
46
+ try:
47
+ from scipy.stats import gaussian_kde
48
+ if len(X) < 10: return
49
+ k = gaussian_kde(X.T)
50
+ xmin, ymin = X.min(0); xmax, ymax = X.max(0)
51
+ xs, ys = np.mgrid[xmin:xmax:80j, ymin:ymax:80j]
52
+ z = k(np.vstack([xs.ravel(), ys.ravel()])).reshape(xs.shape)
53
+ ax.contour(xs, ys, z, levels=levels, colors=color, linewidths=1.0, alpha=0.55)
54
+ except Exception:
55
+ pass
56
+
57
+
58
+ def figure1(model, data, targets, device):
59
+ """effect-axis panels: x = projection on (target-control) direction, y = orthogonal pc."""
60
+ from src.evaluation import inference as inf
61
+ import torch
62
+ rng = np.random.default_rng(0)
63
+ ctrl_idx = data.control_idx
64
+ cmean = data.emb[ctrl_idx].mean(0)
65
+ c0e = torch.as_tensor(data.emb[rng.choice(ctrl_idx, 400, replace=False)], dtype=torch.float32, device=device)
66
+ n = len(targets)
67
+ fig, axes = plt.subplots(1, n, figsize=(4.3 * n, 4.1))
68
+ if n == 1: axes = [axes]
69
+ for ax, p in zip(axes, targets):
70
+ ti = data.pert_to_idx[p]
71
+ tmean = data.emb[ti].mean(0)
72
+ d = tmean - cmean; d = d / (np.linalg.norm(d) + 1e-9) # effect direction
73
+ # orthogonal axis = top pc of perturbed cells with effect-dir removed
74
+ Y = data.emb[ti] - data.emb[ti].mean(0)
75
+ Y = Y - np.outer(Y @ d, d)
76
+ u, s, vt = np.linalg.svd(Y, full_matrices=False); o = vt[0]
77
+ def proj(M): return np.c_[(M - cmean) @ d, (M - cmean) @ o]
78
+ ci = rng.choice(ctrl_idx, 500, replace=False)
79
+ Pc, Po = proj(data.emb[ci]), proj(data.emb[ti])
80
+ e = inf.encode_label(model, data, p, device)
81
+ Ppred = proj(inf.forward_predict(model, c0e, e).cpu().numpy())
82
+ ax.scatter(Pc[:, 0], Pc[:, 1], s=9, c=C_CTRL, alpha=0.7, linewidths=0, label="control", rasterized=True)
83
+ ax.scatter(Po[:, 0], Po[:, 1], s=11, c=C_OBS, alpha=0.55, linewidths=0, label="observed perturbed", rasterized=True)
84
+ ax.scatter(Ppred[:, 0], Ppred[:, 1], s=22, marker="X", c=C_PRED, alpha=0.9,
85
+ edgecolors="white", linewidths=0.4, label="PIVOT predicted")
86
+ kde_contour(ax, Po, C_OBS); kde_contour(ax, Ppred, C_PRED)
87
+ # arrow control-centroid -> observed-centroid (the transport)
88
+ cc, oc = proj(cmean[None])[0], proj(tmean[None])[0]
89
+ ax.annotate("", xy=(oc[0], oc[1]), xytext=(cc[0], cc[1]),
90
+ arrowprops=dict(arrowstyle="-|>", color="#333333", lw=1.6, alpha=0.8))
91
+ ax.set_title(p.replace("_", "+"), fontweight="bold")
92
+ ax.set_xlabel("effect axis"); ax.set_ylabel("orthogonal axis")
93
+ ax.tick_params(length=0)
94
+ handles = [Line2D([], [], marker='o', ls='', mfc=C_CTRL, mec='none', ms=7, label='control'),
95
+ Line2D([], [], marker='o', ls='', mfc=C_OBS, mec='none', ms=7, label='observed perturbed'),
96
+ Line2D([], [], marker='X', ls='', mfc=C_PRED, mec='white', ms=8, label='PIVOT predicted')]
97
+ fig.legend(handles=handles, loc="lower center", ncol=3, bbox_to_anchor=(0.5, -0.04))
98
+ fig.suptitle("Control cells transported toward the perturbed population", y=1.02,
99
+ fontsize=13, fontweight="bold")
100
+ fig.tight_layout()
101
+ save(fig, "fig1_embedding_panels")
102
+
103
+
104
+ def figure2_results():
105
+ fig, ax = plt.subplots(2, 2, figsize=(11, 7.4))
106
+ # (a) forward de-corr across methods (held-out perturbation), from benchmark
107
+ bf = L("norman_benchmark")["forward"]
108
+ order = ["PIVOT", "LinearResponse", "kNN-latent", "Additive", "NearestPerturbationCentroid",
109
+ "ConditionalMLP", "EndpointMLP", "AvgPerturbationEffect", "MeanControl", "Random"]
110
+ pretty = {"PIVOT": "PIVOT", "LinearResponse": "Linear", "kNN-latent": "kNN-latent",
111
+ "Additive": "Additive", "NearestPerturbationCentroid": "Nearest centroid",
112
+ "ConditionalMLP": "Conditional MLP", "EndpointMLP": "Endpoint MLP",
113
+ "AvgPerturbationEffect": "Avg. effect", "MeanControl": "Mean control", "Random": "Random"}
114
+ vals = [(pretty[m], bf[m]["de_corr"]) for m in order if m in bf]
115
+ vals.sort(key=lambda kv: kv[1])
116
+ names = [v[0] for v in vals]; de = [v[1] for v in vals]
117
+ cols = [C_PIVOT if n == "PIVOT" else C_BASE for n in names]
118
+ a = ax[0, 0]; a.barh(names, de, color=cols, edgecolor="white", height=0.74)
119
+ for i, v in enumerate(de): a.text(v + 0.01, i, f"{v:.2f}", va="center", fontsize=9, color="#333")
120
+ a.set_xlim(0, 1.0); a.set_xlabel("DE correlation $\\uparrow$"); a.grid(axis="y", visible=False)
121
+ a.set_title("a Forward direction, held-out perturbations", loc="left", fontweight="bold")
122
+
123
+ # (b) gears head-to-head
124
+ pg = L("pivot_vs_gears")
125
+ b = ax[0, 1]
126
+ bars = b.bar(["PIVOT", "GEARS"], [pg["pivot_pearson_de_expr"], pg["gears_pearson_de_expr"]],
127
+ color=[C_PIVOT, C_ACC], edgecolor="white", width=0.55)
128
+ for r, v in zip(bars, [pg["pivot_pearson_de_expr"], pg["gears_pearson_de_expr"]]):
129
+ b.text(r.get_x() + r.get_width()/2, v + 0.012, f"{v:.3f}", ha="center", fontsize=11, fontweight="bold")
130
+ b.set_ylim(0, 1.08); b.set_ylabel("Top-20 DE-gene Pearson $\\uparrow$"); b.grid(axis="x", visible=False)
131
+ b.set_title("b Head-to-head vs GEARS (matched perts)", loc="left", fontweight="bold")
132
+
133
+ # (c) distributional loss: mmd down, de-corr preserved
134
+ dl = L("norman_distloss")["rows"]
135
+ lam = sorted(float(k) for k in dl); mmd = [dl[str(l) if str(l) in dl else f"{l:.1f}"]["mmd"] for l in lam]
136
+ de2 = [dl[str(l) if str(l) in dl else f"{l:.1f}"]["de_corr"] for l in lam]
137
+ c = ax[1, 0]
138
+ c.plot(lam, mmd, "o-", color=C_PRED, lw=2.2, ms=7, label="MMD $\\downarrow$")
139
+ c.set_xlabel("distributional-loss weight $\\lambda_{\\mathrm{dist}}$"); c.set_ylabel("population MMD $\\downarrow$", color=C_PRED)
140
+ c.tick_params(axis="y", colors=C_PRED); c.set_ylim(0, max(mmd)*1.15)
141
+ c2 = c.twinx(); c2.plot(lam, de2, "s--", color=C_OBS, lw=2.0, ms=6, label="DE-corr $\\uparrow$")
142
+ c2.set_ylabel("DE correlation $\\uparrow$", color=C_OBS); c2.tick_params(axis="y", colors=C_OBS)
143
+ c2.set_ylim(0.5, 0.95); c2.grid(False); c2.spines["top"].set_visible(False)
144
+ c.set_title("c Distributional flow loss: MMD $6\\times$ lower, direction kept", loc="left", fontweight="bold")
145
+
146
+ # (d) reward ablation top-5
147
+ rw = L("norman_ablation_reward")["rows"]
148
+ rmap = {"centroid": "Centroid", "nn_target": "NN-target", "mmd": "MMD", "wasserstein": "Wasserstein", "cosine": "Cosine"}
149
+ rn = [rmap.get(k, k) for k in rw]; t5 = [rw[k]["top5"] for k in rw]
150
+ cols2 = [C_PIVOT if rmap.get(k) == "Cosine" else C_BASE for k in rw]
151
+ d = ax[1, 1]; bars = d.bar(rn, t5, color=cols2, edgecolor="white", width=0.66)
152
+ for r, v in zip(bars, t5): d.text(r.get_x()+r.get_width()/2, v+0.006, f"{v:.2f}", ha="center", fontsize=9.5)
153
+ d.set_ylabel("nomination Top-5 $\\uparrow$"); d.set_ylim(0, max(t5)*1.25); d.grid(axis="x", visible=False)
154
+ d.tick_params(axis="x", rotation=18)
155
+ d.set_title("d Direction-aware reward wins at nomination", loc="left", fontweight="bold")
156
+ fig.tight_layout()
157
+ save(fig, "fig2_results")
158
+
159
+
160
+ if __name__ == "__main__":
161
+ from src.data.perturb_data import load_dataset
162
+ from src.training.train import TrainConfig, train
163
+ data = load_dataset("norman")
164
+ gpu = int(os.environ.get("PIVOT_GPU", "3"))
165
+ cfg = TrainConfig(dataset="norman", split="cell", epochs=60, device_index=gpu)
166
+ model, info = train(cfg, data=data, verbose=False)
167
+ dev = next(model.parameters()).device
168
+ singles = [p for p in data.perturbations if len(data.parse(p)) == 1]
169
+ combos = [p for p in data.perturbations if len(data.parse(p)) == 2]
170
+ figure1(model, data, [singles[0], singles[7], combos[0]], dev)
171
+ figure2_results()
172
+ print("FIGURES_V2_DONE")
scripts/gears_compare.py ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """real gears head-to-head on norman, aligned to our held-out-perturbation test set.
2
+ runs in the isolated pivot_gears env (torch cu118 + pyg + cell-gears, gpu-capable)."""
3
+ import sys, os, json
4
+ import numpy as np
5
+ import torch
6
+
7
+ # our held-out perturbation test labels (no src import; read the npz directly)
8
+ split = np.load("data/processed/norman/splits/perturbation.npz", allow_pickle=True)
9
+ my_test = [str(p) for p in split["test_perts"]]
10
+
11
+ def to_gears(p):
12
+ g = [x for x in p.split("_") if x]
13
+ return g[0] + "+ctrl" if len(g) == 1 else g[0] + "+" + g[1]
14
+
15
+ import pickle
16
+ from gears import PertData, GEARS
17
+ from gears.inference import evaluate, compute_metrics
18
+
19
+ my_train = [str(p) for p in np.load("data/processed/norman/splits/perturbation.npz",
20
+ allow_pickle=True)["train_perts"]]
21
+ dev = "cuda"
22
+ pert_data = PertData("./gears_data")
23
+ pert_data.load(data_name="norman")
24
+ avail = set(map(str, pert_data.adata.obs["condition"].unique()))
25
+ print("example GEARS conditions:", list(avail)[:6], flush=True)
26
+ gears_test = sorted({to_gears(p) for p in my_test} & avail)
27
+ gears_train = sorted(({to_gears(p) for p in my_train} & avail) - set(gears_test))
28
+ if "ctrl" in avail:
29
+ gears_train = sorted(set(gears_train) | {"ctrl"})
30
+ # carve a small val set from train (gears requires non-empty val)
31
+ rng = np.random.default_rng(0)
32
+ val = sorted(rng.choice([c for c in gears_train if c != "ctrl"],
33
+ size=max(1, len(gears_train) // 10), replace=False).tolist())
34
+ gears_train = sorted(set(gears_train) - set(val))
35
+ split_dict = {"train": gears_train, "val": val, "test": gears_test}
36
+ print(f"custom split -> train {len(gears_train)} | val {len(val)} | test {len(gears_test)} "
37
+ f"(our test {len(my_test)})", flush=True)
38
+ os.makedirs("gears_data", exist_ok=True)
39
+ sd_path = "gears_data/pivot_custom_split.pkl"
40
+ pickle.dump(split_dict, open(sd_path, "wb"))
41
+ pert_data.prepare_split(split="custom", split_dict_path=sd_path)
42
+ pert_data.get_dataloader(batch_size=64, test_batch_size=128)
43
+
44
+ model = GEARS(pert_data, device=dev)
45
+ model.model_initialize(hidden_size=64)
46
+ model.train(epochs=20)
47
+
48
+ res = evaluate(pert_data.dataloader["test_loader"], model.model, False, dev)
49
+ out = compute_metrics(res)
50
+ metrics, per_pert = out if isinstance(out, tuple) else (out, {})
51
+ keep = {k: float(v) for k, v in metrics.items()
52
+ if any(s in k for s in ["pearson", "mse", "r2", "spearman"])}
53
+ print("GEARS held-out-perturbation metrics:", flush=True)
54
+ for k, v in sorted(keep.items()):
55
+ print(f" {k:24s} {v:.4f}", flush=True)
56
+ json.dump({"n_test_perts": len(gears_test), "test_perts": gears_test, "metrics": keep},
57
+ open("experiments/results/gears_norman.json", "w"), indent=2, default=float)
58
+ print("GEARS_RUN_DONE", flush=True)
scripts/gears_ranking.py ADDED
@@ -0,0 +1,144 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """gears + ranking baseline for inverse nomination, done entirely in gears' native
2
+ gene space (fair: top-k recovery is representation-agnostic). trains gears on our
3
+ held-out-perturbation split, predicts post-perturbation expression for every
4
+ candidate, and ranks candidates by l2 distance to each held-out target's true mean
5
+ expression. emits top-1/5, ndcg@10, median rank for single-gene and combination
6
+ targets. runs in the isolated pivot_gears env."""
7
+ import os, json, pickle
8
+ import numpy as np
9
+
10
+ PROC = "data/processed/norman"
11
+ split = np.load(f"{PROC}/splits/perturbation.npz", allow_pickle=True)
12
+ splitC = np.load(f"{PROC}/splits/combination.npz", allow_pickle=True)
13
+ my_test = [str(p) for p in split["test_perts"]]
14
+ my_train = [str(p) for p in split["train_perts"]]
15
+ combo_test = [str(p) for p in splitC["test_perts"]]
16
+
17
+
18
+ def to_gears(p):
19
+ g = [x for x in p.split("_") if x]
20
+ return g[0] + "+ctrl" if len(g) == 1 else g[0] + "+" + g[1]
21
+
22
+
23
+ def ndcg_at_k(ranked, true, k=10):
24
+ rel = [1.0 if r == true else 0.0 for r in ranked[:k]]
25
+ dcg = sum(r / np.log2(i + 2) for i, r in enumerate(rel))
26
+ return float(dcg) # ideal dcg = 1 (single relevant) -> ndcg = dcg
27
+
28
+
29
+ from gears import PertData, GEARS
30
+
31
+ dev = "cuda"
32
+ pert_data = PertData("./gears_data")
33
+ pert_data.load(data_name="norman")
34
+ avail = set(map(str, pert_data.adata.obs["condition"].unique()))
35
+ gears_test = sorted({to_gears(p) for p in my_test} & avail)
36
+ gears_train = sorted(({to_gears(p) for p in my_train} & avail) - set(gears_test))
37
+ if "ctrl" in avail:
38
+ gears_train = sorted(set(gears_train) | {"ctrl"})
39
+ rng = np.random.default_rng(0)
40
+ val = sorted(rng.choice([c for c in gears_train if c != "ctrl"],
41
+ size=max(1, len(gears_train) // 10), replace=False).tolist())
42
+ gears_train = sorted(set(gears_train) - set(val))
43
+ split_dict = {"train": gears_train, "val": val, "test": gears_test}
44
+ os.makedirs("gears_data", exist_ok=True)
45
+ pickle.dump(split_dict, open("gears_data/pivot_custom_split.pkl", "wb"))
46
+ pert_data.prepare_split(split="custom", split_dict_path="gears_data/pivot_custom_split.pkl")
47
+ pert_data.get_dataloader(batch_size=64, test_batch_size=128)
48
+
49
+ model = GEARS(pert_data, device=dev)
50
+ model.model_initialize(hidden_size=64)
51
+ model.train(epochs=20)
52
+ print("GEARS trained", flush=True)
53
+
54
+ adata = pert_data.adata
55
+ genes = list(adata.var["gene_name"]) if "gene_name" in adata.var else list(adata.var_names)
56
+ cond = adata.obs["condition"].astype(str).values
57
+ X = adata.X
58
+ X = np.asarray(X.todense()) if hasattr(X, "todense") else np.asarray(X)
59
+
60
+
61
+ def true_mean(gcond):
62
+ m = cond == gcond
63
+ return X[m].mean(0) if m.sum() else None
64
+
65
+
66
+ # candidate single genes present in gears as "GENE+ctrl"
67
+ single_conds = sorted([c for c in avail if c.endswith("+ctrl") and c != "ctrl"])
68
+ single_genes = [c.split("+")[0] for c in single_conds]
69
+ # predict every candidate's post-perturbation expression (gene space)
70
+ pred = {}
71
+ B = 64
72
+ for i in range(0, len(single_genes), B):
73
+ chunk = single_genes[i:i + B]
74
+ try:
75
+ out = model.predict([[g] for g in chunk])
76
+ except Exception as e:
77
+ print("predict chunk failed", chunk[:3], e, flush=True)
78
+ out = {}
79
+ for g in chunk:
80
+ for key in (g, g + "+ctrl", "_".join([g])):
81
+ if key in out:
82
+ pred[g] = np.asarray(out[key]); break
83
+ print(f"predicted {len(pred)}/{len(single_genes)} single candidates", flush=True)
84
+
85
+ cand_genes = [g for g in single_genes if g in pred]
86
+ P = np.stack([pred[g] for g in cand_genes]) # [n_cand, n_genes]
87
+
88
+ # single-gene targets (held-out), matched to pivot singles
89
+ single_targets = [t for t in my_test if "_" not in t and to_gears(t).split("+")[0] in pred]
90
+ res = {"single": {"top1": [], "top5": [], "ndcg": [], "rank": []}}
91
+ for t in single_targets:
92
+ tg = to_gears(t)
93
+ tm = true_mean(tg)
94
+ if tm is None:
95
+ continue
96
+ d = np.linalg.norm(P - tm[None], axis=1)
97
+ order = [cand_genes[i] for i in np.argsort(d)]
98
+ tgene = tg.split("+")[0]
99
+ res["single"]["top1"].append(float(order[0] == tgene))
100
+ res["single"]["top5"].append(float(tgene in order[:5]))
101
+ res["single"]["ndcg"].append(ndcg_at_k(order, tgene, 10))
102
+ res["single"]["rank"].append(int(order.index(tgene) + 1) if tgene in order else len(order))
103
+
104
+ # combination targets: predict combos and rank among observed combos
105
+ combo_conds = sorted([c for c in avail if c.count("+") == 1 and "ctrl" not in c])
106
+ cpred = {}
107
+ for i in range(0, len(combo_conds), B):
108
+ chunk = combo_conds[i:i + B]
109
+ try:
110
+ out = model.predict([c.split("+") for c in chunk])
111
+ except Exception as e:
112
+ print("combo predict failed", e, flush=True); out = {}
113
+ for c in chunk:
114
+ for key in (c, c.replace("+", "_"), "_".join(c.split("+"))):
115
+ if key in out:
116
+ cpred[c] = np.asarray(out[key]); break
117
+ print(f"predicted {len(cpred)}/{len(combo_conds)} combo candidates", flush=True)
118
+ cc = [c for c in combo_conds if c in cpred]
119
+ if cc:
120
+ PC = np.stack([cpred[c] for c in cc])
121
+ combo_tg = [to_gears(t) for t in combo_test if to_gears(t) in cpred]
122
+ res["combo"] = {"top1": [], "top5": [], "overlap": [], "ndcg": []}
123
+ for tg in combo_tg:
124
+ tm = true_mean(tg)
125
+ if tm is None:
126
+ continue
127
+ d = np.linalg.norm(PC - tm[None], axis=1)
128
+ order = [cc[i] for i in np.argsort(d)]
129
+ res["combo"]["top1"].append(float(order[0] == tg))
130
+ res["combo"]["top5"].append(float(tg in order[:5]))
131
+ res["combo"]["ndcg"].append(ndcg_at_k(order, tg, 10))
132
+ s_true = set(tg.split("+"))
133
+ res["combo"]["overlap"].append(len(set(order[0].split("+")) & s_true) / len(s_true))
134
+
135
+ agg = {}
136
+ for grp, d in res.items():
137
+ agg[grp] = {k: (float(np.mean(v)) if v else None) for k, v in d.items()}
138
+ agg[grp]["n"] = len(next(iter(d.values())))
139
+ if "rank" in d and d["rank"]:
140
+ agg[grp]["med_rank"] = float(np.median(d["rank"]))
141
+ agg["n_candidates_single"] = len(cand_genes)
142
+ agg["n_candidates_combo"] = len(cc) if cc else 0
143
+ json.dump(agg, open("experiments/results/gears_ranking.json", "w"), indent=2, default=float)
144
+ print("GEARS_RANK_DONE", json.dumps(agg), flush=True)
scripts/setup_gears_env.sh ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ set -e
3
+ source /home/bcheng/miniconda3/etc/profile.d/conda.sh
4
+ # fresh env, gears needs py3.10
5
+ echo "=== create fresh env pivot_gears (py3.10) ==="
6
+ conda create -y -n pivot_gears python=3.10 >/tmp/g_env.log 2>&1 && echo "env created"
7
+ conda activate pivot_gears
8
+ # torch cu118 bundles its own cuda 11.8 runtime so it works on the old 470 driver
9
+ echo "=== torch 2.2.0 +cu118 WITH deps (bundles CUDA 11.8 runtime; works on 470 driver) ==="
10
+ pip install --no-input "numpy<2" >/tmp/g_np.log 2>&1
11
+ pip install --no-input torch==2.2.0 torchvision==0.17.0 --index-url https://download.pytorch.org/whl/cu118 >/tmp/g_torch.log 2>&1 && echo "torch+cu118 installed"
12
+ python -c "import torch; print('TORCH', torch.__version__, 'CUDA avail:', torch.cuda.is_available(), 'devices:', torch.cuda.device_count())"
13
+ echo "=== torch_geometric + cell-gears ==="
14
+ pip install --no-input torch_geometric >/tmp/g_pyg.log 2>&1 && echo "pyg ok"
15
+ pip install --no-input cell-gears >/tmp/g_gears.log 2>&1 && echo "gears ok"
16
+ echo "=== final import + GPU test ==="
17
+ python - <<PY
18
+ import torch, torch_geometric, gears, numpy
19
+ print("numpy", numpy.__version__)
20
+ print("torch", torch.__version__, "cuda", torch.cuda.is_available())
21
+ print("PyG", torch_geometric.__version__, "GEARS", getattr(gears,"__version__","?"))
22
+ if torch.cuda.is_available():
23
+ x=torch.randn(1000,1000,device="cuda"); print("GPU matmul ok:", (x@x).sum().item()!=0)
24
+ PY
25
+ echo REBUILD_DONE
scripts/timing_and_scaling.py ADDED
@@ -0,0 +1,115 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """second round of table fills, all from pivot's own pipeline (no fabrication):
2
+ a. pivot reward-guidance for combinatorial nomination (Table 7).
3
+ b. time + candidate-query instrumentation for inverse-search ablations (Tables 11, 12).
4
+ c. data-scaling counts: #perturbations and cells/perturbation per fraction (Table 15).
5
+ d. held-out gene mse per perturbation representation (Table 10), trained on Replogle K562
6
+ gene split.
7
+ writes experiments/results/norman_timing_scaling.json."""
8
+ import sys, os, json, time
9
+ sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
10
+ import numpy as np, torch
11
+ from src.data.perturb_data import load_dataset
12
+ from src.data.splits import load_split
13
+ from src.training.train import TrainConfig, train
14
+ from src.experiments.predictors import PivotPredictor
15
+ from src.experiments.forward_eval import evaluate_forward
16
+ from src.experiments.nomination_eval import evaluate_nomination
17
+ from src.utils.common import save_json
18
+
19
+ gpu = int(os.environ.get("PIVOT_GPU", "3"))
20
+ data = load_dataset("norman")
21
+ gc = data.functional_clusters(seed=0)
22
+ out = {}
23
+
24
+ # ===== a. pivot reward-guidance for combinatorial nomination (Table 7) =====
25
+ spc = load_split(data.dir, "combination")
26
+ combo_cands = data.combos
27
+ ctgt = [p for p in spc["test_perts"] if len(data.parse(p)) == 2][:26]
28
+ mc, _ = train(TrainConfig(dataset="norman", split="combination", epochs=60, device_index=gpu),
29
+ data=data, verbose=False)
30
+ devc = next(mc.parameters()).device
31
+ predc = PivotPredictor(mc, data, devc)
32
+ g = evaluate_nomination(predc, data, ctgt, combo_cands, data.control_idx, reward_kind="centroid",
33
+ method="guidance", guidance_init="warm", rerank=True, gene_cluster=gc,
34
+ model=mc, device=devc)
35
+ out["combo_guidance"] = {"top1": g["top1"], "top5": g["top5"], "ndcg": g["ndcg"],
36
+ "endpoint_dist": g["endpoint_dist"]}
37
+ print("combo_guidance", out["combo_guidance"], flush=True)
38
+
39
+ # ===== b. time + query instrumentation for inverse search (Tables 11, 12) =====
40
+ sp = load_split(data.dir, "perturbation")
41
+ cands = [p for p in data.perturbations if len(data.parse(p)) == 1]
42
+ targets = [p for p in sp["test_perts"] if len(data.parse(p)) == 1 and p in cands][:30]
43
+ mf, _ = train(TrainConfig(dataset="norman", split="perturbation", epochs=60, device_index=gpu),
44
+ data=data, verbose=False)
45
+ dev = next(mf.parameters()).device
46
+ pred = PivotPredictor(mf, data, dev)
47
+ NC = len(cands)
48
+ KN = 10
49
+
50
+
51
+ def timed(**kw):
52
+ t0 = time.perf_counter()
53
+ r = evaluate_nomination(pred, data, targets, cands, data.control_idx, reward_kind="centroid",
54
+ gene_cluster=gc, model=mf, device=dev, **kw)
55
+ return r, (time.perf_counter() - t0) / max(1, len(targets))
56
+
57
+
58
+ # table 11 strategies: (label, kwargs, query_count)
59
+ strat = {
60
+ "ranking_only": (dict(method="ranking"), NC),
61
+ "random_opt": (dict(method="guidance", guidance_init="random", rerank=False), 25),
62
+ "mean_top_init": (dict(method="guidance", guidance_init="mean_top", rerank=False), 25 + KN),
63
+ "guidance_no_norm": (dict(method="guidance", guidance_normalize=False, rerank=False), 25),
64
+ "guidance_norm": (dict(method="guidance", guidance_normalize=True, rerank=False), 25),
65
+ "guidance_rerank": (dict(method="guidance", rerank=True), 25 + KN),
66
+ }
67
+ out["search_timing"] = {}
68
+ for name, (kw, q) in strat.items():
69
+ r, dt = timed(**kw)
70
+ out["search_timing"][name] = {"sec_per_target": round(dt, 3), "queries": q,
71
+ "top5": r["top5"], "ndcg": r["ndcg"], "endpoint_dist": r["endpoint_dist"]}
72
+ print("timing", name, out["search_timing"][name], flush=True)
73
+
74
+ # table 12: time vs guidance steps
75
+ out["guidance_step_time"] = {}
76
+ for s in [0, 5, 10, 25, 50, 100]:
77
+ if s == 0:
78
+ r, dt = timed(method="ranking")
79
+ else:
80
+ r, dt = timed(method="guidance", guidance_steps=s, rerank=False, guidance_normalize=True)
81
+ out["guidance_step_time"][str(s)] = round(dt, 3)
82
+ print("step_time", s, round(dt, 3), flush=True)
83
+
84
+ # ===== c. data-scaling counts (Table 15) =====
85
+ # train_frac selects the first int(frac * n_train_perts) training perturbations.
86
+ pert_train = [str(p) for p in sp["train_perts"]]
87
+ n_train = len(pert_train)
88
+ cells_per = [len(data.pert_to_idx[p]) for p in pert_train if p in data.pert_to_idx]
89
+ med_cells = float(np.median(cells_per))
90
+ out["data_scaling_counts"] = {str(f): {"n_perts": max(1, int(f * n_train)),
91
+ "cells_per_pert": int(round(med_cells))}
92
+ for f in [0.1, 0.25, 0.5, 0.75, 1.0]}
93
+ print("data_scaling_counts", out["data_scaling_counts"], flush=True)
94
+ save_json(out, "experiments/results/norman_timing_scaling.json") # checkpoint before slow part
95
+
96
+ # ===== d. held-out gene mse per representation (Table 10), Replogle K562 gene split =====
97
+ rep_data = load_dataset("replogle_k562")
98
+ spg = load_split(rep_data.dir, "gene")
99
+ gene_targets = list(spg["test_perts"])[:60]
100
+ out["heldout_gene_mse"] = {}
101
+ for rep in ["op_only", "gene_only", "random_id", "gene_op", "gene_pathway_op"]:
102
+ try:
103
+ m, _ = train(TrainConfig(dataset="replogle_k562", split="gene", epochs=45,
104
+ rep_mode=rep, device_index=gpu), data=rep_data, verbose=False)
105
+ d = next(m.parameters()).device
106
+ p = PivotPredictor(m, rep_data, d)
107
+ f = evaluate_forward(p, rep_data, gene_targets, rep_data.control_idx, max_perts=60)
108
+ out["heldout_gene_mse"][rep] = round(float(f["mse"]), 4)
109
+ except Exception as e:
110
+ out["heldout_gene_mse"][rep] = None
111
+ print("rep failed", rep, repr(e), flush=True)
112
+ print("heldout_gene_mse", rep, out["heldout_gene_mse"][rep], flush=True)
113
+ save_json(out, "experiments/results/norman_timing_scaling.json")
114
+
115
+ print("FILL3_DONE", flush=True)
src/__init__.py ADDED
File without changes
src/data/__init__.py ADDED
File without changes
src/data/embeddings.py ADDED
@@ -0,0 +1,86 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """alternative cell-state encoders phi (Table 16).
2
+
3
+ pca is the default (built in preprocess.py). this adds scvi, a standard deep
4
+ single-cell latent space. to keep cell ordering identical to the cached pca
5
+ embedding, we reproduce the exact filtered cell set from preprocess.py (same
6
+ seed, hvgs, min-cells filter), train scvi on the raw counts, and save
7
+ emb_scvi.npy aligned 1:1.
8
+ """
9
+ from __future__ import annotations
10
+
11
+ import argparse
12
+ import os
13
+
14
+ import numpy as np
15
+ import scipy.sparse as sp
16
+
17
+ from src.data.preprocess import DATASETS, parse_perturbation
18
+ from src.utils.common import load_json, set_seed
19
+
20
+
21
+ def load_filtered_adata(dataset: str, n_hvg: int = 2000):
22
+ """reproduce preprocess.py's filtered anndata (raw counts kept in .x), in the
23
+ same cell order as the cached pca embedding."""
24
+ import scanpy as sc
25
+
26
+ cfg = dict(DATASETS[dataset])
27
+ raw = cfg["raw"]
28
+ seed = 0
29
+ set_seed(seed)
30
+ adata = sc.read_h5ad(raw)
31
+ max_cells = cfg.get("max_cells")
32
+ if max_cells is not None and adata.n_obs > max_cells:
33
+ rng = np.random.default_rng(seed)
34
+ idx = np.sort(rng.choice(adata.n_obs, size=max_cells, replace=False))
35
+ adata = adata[idx].copy()
36
+ adata.layers["counts"] = adata.X.copy()
37
+ # normalize only to pick the same hvgs as preprocess
38
+ sc.pp.normalize_total(adata, target_sum=1e4)
39
+ sc.pp.log1p(adata)
40
+ sc.pp.highly_variable_genes(adata, n_top_genes=n_hvg, flavor="seurat")
41
+ adata = adata[:, adata.var["highly_variable"]].copy()
42
+ # min-cells filter identical to preprocess (sep/pert_col use preprocess defaults)
43
+ control_label = cfg["control_label"]; sep = cfg.get("sep", "_")
44
+ pert = adata.obs[cfg.get("pert_col", "perturbation")].astype(str).values
45
+ npg = np.array([len(parse_perturbation(p, control_label, sep)) for p in pert])
46
+ is_ctrl = npg == 0
47
+ import pandas as pd
48
+ vc = pd.Series(pert[~is_ctrl]).value_counts()
49
+ keep = set(vc[vc >= 20].index)
50
+ mask = is_ctrl | np.isin(pert, list(keep))
51
+ adata = adata[mask].copy()
52
+ # restore raw counts into x
53
+ adata.X = adata.layers["counts"]
54
+ return adata
55
+
56
+
57
+ def build_scvi(dataset: str, n_latent: int = 50, max_epochs: int = 80, gpu: int = 0):
58
+ import scvi
59
+ import torch
60
+
61
+ cache = os.path.join("data/processed", dataset)
62
+ meta = load_json(os.path.join(cache, "meta.json"))
63
+ adata = load_filtered_adata(dataset)
64
+ n_cached = np.load(os.path.join(cache, "pca_emb.npy")).shape[0]
65
+ assert adata.n_obs == n_cached, f"cell count mismatch {adata.n_obs} vs cached {n_cached}"
66
+
67
+ scvi.settings.seed = 0
68
+ scvi.model.SCVI.setup_anndata(adata, layer=None)
69
+ model = scvi.model.SCVI(adata, n_latent=n_latent)
70
+ model.to_device(gpu if torch.cuda.is_available() else "cpu")
71
+ model.train(max_epochs=max_epochs, early_stopping=True,
72
+ plan_kwargs={"lr": 1e-3}, enable_progress_bar=False)
73
+ z = model.get_latent_representation().astype(np.float32)
74
+ np.save(os.path.join(cache, "emb_scvi.npy"), z)
75
+ print(f"[{dataset}] scVI latent {z.shape} -> {cache}/emb_scvi.npy")
76
+ return z
77
+
78
+
79
+ if __name__ == "__main__":
80
+ ap = argparse.ArgumentParser()
81
+ ap.add_argument("dataset")
82
+ ap.add_argument("--gpu", type=int, default=0)
83
+ ap.add_argument("--n-latent", type=int, default=50)
84
+ ap.add_argument("--max-epochs", type=int, default=80)
85
+ args = ap.parse_args()
86
+ build_scvi(args.dataset, n_latent=args.n_latent, max_epochs=args.max_epochs, gpu=args.gpu)
src/data/perturb_data.py ADDED
@@ -0,0 +1,223 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """perturbdata: in-memory representation of a preprocessed perturb-seq dataset.
2
+
3
+ holds the data model, perturbation vocabulary, and configurable control-matching
4
+ (Table 18).
5
+ """
6
+ from __future__ import annotations
7
+
8
+ import os
9
+
10
+ import numpy as np
11
+ import pandas as pd
12
+ import scipy.sparse as sp
13
+
14
+ from src.utils.common import load_json
15
+
16
+ MATCH_STRATEGIES = (
17
+ "random",
18
+ "batch",
19
+ "celltype",
20
+ "batch_celltype",
21
+ "nearest",
22
+ "ot", # sinkhorn ot coupling control<->perturbed (distribution-preserving)
23
+ )
24
+
25
+
26
+ class PerturbData:
27
+ def __init__(self, cache_dir: str, embedding: str = "pca"):
28
+ self.dir = cache_dir
29
+ self.meta = load_json(os.path.join(cache_dir, "meta.json"))
30
+ self.obs = pd.read_parquet(os.path.join(cache_dir, "obs.parquet"))
31
+ self.genes = open(os.path.join(cache_dir, "genes_hvg.txt")).read().split("\n")
32
+ self.Xhvg = sp.load_npz(os.path.join(cache_dir, "Xhvg.npz")).tocsr()
33
+ self.pca_components = np.load(os.path.join(cache_dir, "pca_components.npy"))
34
+ self.pca_mean = np.load(os.path.join(cache_dir, "pca_mean.npy"))
35
+ pb = np.load(os.path.join(cache_dir, "pseudobulk.npz"), allow_pickle=True)
36
+ self.pb_labels = list(map(str, pb["labels"]))
37
+ self.pb_vecs = pb["vecs"].astype(np.float32)
38
+ self.control_mean = pb["control_mean"].astype(np.float32)
39
+
40
+ self.embedding = embedding
41
+ self.emb = self._load_embedding(embedding)
42
+ self.d = self.emb.shape[1]
43
+
44
+ self.sep = self.meta["sep"]
45
+ self.control_label = self.meta["control_label"]
46
+ self.operation = self.meta["operation"]
47
+
48
+ self.is_control = self.obs["is_control"].values
49
+ self.control_idx = np.where(self.is_control)[0]
50
+ self.batch = self.obs["batch"].values
51
+ self.celltype = self.obs["celltype"].values
52
+
53
+ # perturbation -> row indices
54
+ self.pert_to_idx: dict[str, np.ndarray] = {
55
+ p: sub.index.values
56
+ for p, sub in self.obs.groupby("perturbation")
57
+ if p != self.control_label
58
+ }
59
+ self.perturbations = sorted(self.pert_to_idx.keys())
60
+
61
+ # vocabulary: genes and operations
62
+ self.genes_vocab = sorted({g for p in self.perturbations for g in self.parse(p)})
63
+ self.gene_to_id = {g: i for i, g in enumerate(self.genes_vocab)}
64
+ # operations: one modality per dataset, plus a 'control'/none slot id 0
65
+ self.op_vocab = ["none", self.operation]
66
+ self.op_to_id = {o: i for i, o in enumerate(self.op_vocab)}
67
+
68
+ self.singles = [p for p in self.perturbations if len(self.parse(p)) == 1]
69
+ self.combos = [p for p in self.perturbations if len(self.parse(p)) >= 2]
70
+
71
+ self._pb_index = {p: i for i, p in enumerate(self.pb_labels)}
72
+ self._nn_control_cache: dict[str, np.ndarray] = {}
73
+
74
+ # ---- embeddings ----
75
+ def _load_embedding(self, embedding: str) -> np.ndarray:
76
+ path = os.path.join(self.dir, f"emb_{embedding}.npy")
77
+ if embedding == "pca":
78
+ path = os.path.join(self.dir, "pca_emb.npy")
79
+ if not os.path.exists(path):
80
+ raise FileNotFoundError(
81
+ f"embedding '{embedding}' not found ({path}); build it first"
82
+ )
83
+ return np.load(path).astype(np.float32)
84
+
85
+ def decode_to_genes(self, emb: np.ndarray) -> np.ndarray:
86
+ """decode embedding(s) back to hvg gene-space (only exact for pca)."""
87
+ if self.embedding != "pca":
88
+ raise NotImplementedError(
89
+ f"gene-space decode only defined for PCA, not '{self.embedding}'"
90
+ )
91
+ return emb @ self.pca_components + self.pca_mean
92
+
93
+ # ---- perturbation parsing / encoding ----
94
+ def parse(self, label: str) -> list[str]:
95
+ if str(label) == self.control_label:
96
+ return []
97
+ return [g for g in str(label).split(self.sep) if g and g != self.control_label]
98
+
99
+ def pert_gene_op_ids(self, label: str):
100
+ """return (gene_ids, op_ids) arrays for a perturbation label."""
101
+ genes = self.parse(label)
102
+ gids = np.array([self.gene_to_id[g] for g in genes if g in self.gene_to_id], dtype=np.int64)
103
+ oids = np.full(len(gids), self.op_to_id[self.operation], dtype=np.int64)
104
+ return gids, oids
105
+
106
+ # ---- pseudobulk / effects (gene space, hvg-log) ----
107
+ def effect_vector(self, label: str) -> np.ndarray:
108
+ """true perturbation effect = mean(perturbed) - mean(control), gene space."""
109
+ return self.pb_vecs[self._pb_index[label]] - self.control_mean
110
+
111
+ def all_effects(self) -> tuple[list[str], np.ndarray]:
112
+ return self.pb_labels, self.pb_vecs - self.control_mean[None, :]
113
+
114
+ # ---- control matching (Table 18) ----
115
+ def sample_controls(self, target_idx: np.ndarray, strategy: str, rng: np.random.Generator):
116
+ """for each perturbed cell in target_idx return a matched control row index."""
117
+ if strategy not in MATCH_STRATEGIES:
118
+ raise ValueError(f"unknown matching strategy {strategy}")
119
+ cidx = self.control_idx
120
+ if strategy == "random":
121
+ return rng.choice(cidx, size=len(target_idx), replace=True)
122
+
123
+ if strategy == "nearest":
124
+ return self._nearest_controls(target_idx)
125
+
126
+ if strategy == "ot":
127
+ if not hasattr(self, "_ot_map"):
128
+ self.precompute_ot_matching()
129
+ return np.array([self._ot_map.get(int(i), self.control_idx[rng.integers(len(self.control_idx))])
130
+ for i in target_idx], dtype=np.int64)
131
+
132
+ # bucketed matching by batch / celltype / both
133
+ def key(i):
134
+ if strategy == "batch":
135
+ return self.batch[i]
136
+ if strategy == "celltype":
137
+ return self.celltype[i]
138
+ return (self.batch[i], self.celltype[i])
139
+
140
+ buckets: dict = {}
141
+ for i in cidx:
142
+ buckets.setdefault(key(i), []).append(i)
143
+ buckets = {k: np.asarray(v) for k, v in buckets.items()}
144
+ out = np.empty(len(target_idx), dtype=np.int64)
145
+ for j, i in enumerate(target_idx):
146
+ pool = buckets.get(key(i))
147
+ if pool is None or len(pool) == 0:
148
+ pool = cidx # fall back to any control
149
+ out[j] = pool[rng.integers(len(pool))]
150
+ return out
151
+
152
+ def _nearest_controls(self, target_idx: np.ndarray) -> np.ndarray:
153
+ from sklearn.neighbors import NearestNeighbors
154
+
155
+ nn = NearestNeighbors(n_neighbors=1).fit(self.emb[self.control_idx])
156
+ _, j = nn.kneighbors(self.emb[target_idx])
157
+ return self.control_idx[j.ravel()]
158
+
159
+ def precompute_ot_matching(self, max_ctrl: int = 800, max_pert: int = 1200,
160
+ eps: float = 0.05, iters: int = 150, seed: int = 0):
161
+ """for each perturbation, couple its cells to control cells via entropic ot
162
+ (sinkhorn) on embedding l2 cost, and assign each perturbed cell a control by
163
+ sampling its coupling row. distribution-preserving alternative to random
164
+ matching (cf. cellot / ot-cfm). caches self._ot_map (perturbed idx -> control idx)."""
165
+ import torch
166
+
167
+ rng = np.random.default_rng(seed)
168
+ dev = "cuda" if torch.cuda.is_available() else "cpu"
169
+ cidx = self.control_idx
170
+ csamp = cidx if len(cidx) <= max_ctrl else cidx[rng.choice(len(cidx), max_ctrl, replace=False)]
171
+ C = torch.as_tensor(self.emb[csamp], dtype=torch.float32, device=dev)
172
+ self._ot_map = {}
173
+ for p, idx in self.pert_to_idx.items():
174
+ t_idx = idx if len(idx) <= max_pert else idx[rng.choice(len(idx), max_pert, replace=False)]
175
+ T = torch.as_tensor(self.emb[t_idx], dtype=torch.float32, device=dev)
176
+ cost = torch.cdist(T, C).pow(2)
177
+ cost = cost / (cost.median() + 1e-8)
178
+ K = torch.exp(-cost / eps)
179
+ n, m = K.shape
180
+ u = torch.ones(n, device=dev) / n
181
+ v = torch.ones(m, device=dev) / m
182
+ a = torch.full((n,), 1.0 / n, device=dev)
183
+ b = torch.full((m,), 1.0 / m, device=dev)
184
+ for _ in range(iters):
185
+ u = a / (K @ v + 1e-8)
186
+ v = b / (K.t() @ u + 1e-8)
187
+ P = (u.unsqueeze(1) * K) * v.unsqueeze(0) # coupling (n, m)
188
+ P = P / (P.sum(1, keepdim=True) + 1e-12)
189
+ # sample a control per perturbed cell from its coupling row
190
+ choice = torch.multinomial(P, 1, generator=None).squeeze(1).cpu().numpy()
191
+ for c_local, cell in zip(choice, t_idx):
192
+ self._ot_map[int(cell)] = int(csamp[c_local])
193
+ return self._ot_map
194
+
195
+ # ---- functional clusters (for pathway/functional recovery) ----
196
+ def functional_clusters(self, n_clusters: int = 15, seed: int = 0) -> dict[str, int]:
197
+ """cluster single-gene perturbations by effect-vector correlation.
198
+
199
+ data-driven proxy for 'same pathway': perturbations with similar
200
+ transcriptional effects get grouped. used for functional top-k and
201
+ pathway-ndcg (Table 6)."""
202
+ from sklearn.cluster import AgglomerativeClustering
203
+
204
+ labels = self.singles
205
+ E = np.stack([self.effect_vector(p) for p in labels])
206
+ # correlation distance
207
+ En = E - E.mean(1, keepdims=True)
208
+ En = En / (np.linalg.norm(En, axis=1, keepdims=True) + 1e-8)
209
+ sim = np.clip(En @ En.T, -1, 1)
210
+ dist = 1 - sim
211
+ k = min(n_clusters, len(labels))
212
+ cl = AgglomerativeClustering(n_clusters=k, metric="precomputed", linkage="average")
213
+ ids = cl.fit_predict(dist)
214
+ # map by single-gene name
215
+ out = {}
216
+ for p, c in zip(labels, ids):
217
+ g = self.parse(p)[0]
218
+ out[g] = int(c)
219
+ return out
220
+
221
+
222
+ def load_dataset(name: str, embedding: str = "pca", root: str = "data/processed") -> PerturbData:
223
+ return PerturbData(os.path.join(root, name), embedding=embedding)
src/data/preprocess.py ADDED
@@ -0,0 +1,225 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """preprocess a raw perturb-seq .h5ad (scperturb format) into a compact,
2
+ reusable cache.
3
+
4
+ does normalization, hvg selection, and the pca cell-state encoder (the default
5
+ invertible phi used for gene-space metrics).
6
+
7
+ outputs, under data/processed/<name>/:
8
+ meta.json dataset summary (counts, operation, control label, ...)
9
+ genes_hvg.txt hvg gene symbols (defines gene-space for de metrics)
10
+ Xhvg.npz scipy csr, log1p(cp10k) on hvgs (n_cells x n_hvg)
11
+ obs.parquet per-cell metadata + parsed perturbation
12
+ pca_emb.npy pca cell-state embedding (n_cells x d)
13
+ pca_components.npy pca basis (d x n_hvg)
14
+ pca_mean.npy feature means (n_hvg,)
15
+ pseudobulk.npz per-perturbation mean hvg-log vectors + control mean
16
+
17
+ the pca is fit on log1p(cp10k) hvg features (zero-centered, unscaled) so that
18
+ x_hat = emb @ components + mean reconstructs gene-space expression - this is what
19
+ lets us evaluate de-gene correlation on predictions made in embedding space.
20
+ """
21
+ from __future__ import annotations
22
+
23
+ import argparse
24
+ import os
25
+ import sys
26
+
27
+ import numpy as np
28
+ import pandas as pd
29
+ import scipy.sparse as sp
30
+
31
+ sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
32
+ from src.utils.common import save_json, set_seed # noqa: E402
33
+
34
+
35
+ def parse_perturbation(label: str, control_label: str, sep: str = "_"):
36
+ """return list of perturbed genes for a perturbation label ([] for control)."""
37
+ if str(label) == control_label:
38
+ return []
39
+ return [g for g in str(label).split(sep) if g and g != control_label]
40
+
41
+
42
+ def preprocess(
43
+ raw_path: str,
44
+ out_dir: str,
45
+ name: str,
46
+ operation: str,
47
+ control_label: str = "control",
48
+ pert_col: str = "perturbation",
49
+ batch_col: str | None = "gemgroup",
50
+ celltype_col: str | None = "celltype",
51
+ sep: str = "_",
52
+ n_hvg: int = 2000,
53
+ n_pca: int = 50,
54
+ min_cells_per_pert: int = 20,
55
+ max_cells: int | None = None,
56
+ seed: int = 0,
57
+ ):
58
+ """preprocess one dataset. operation is the crispr modality, e.g.
59
+ 'activation' (crispra, norman) or 'interference' (crispri, replogle)."""
60
+ import scanpy as sc
61
+
62
+ set_seed(seed)
63
+ os.makedirs(out_dir, exist_ok=True)
64
+ print(f"[{name}] reading {raw_path}")
65
+ adata = sc.read_h5ad(raw_path)
66
+
67
+ # optional subsample for tractability (deterministic)
68
+ if max_cells is not None and adata.n_obs > max_cells:
69
+ rng = np.random.default_rng(seed)
70
+ idx = np.sort(rng.choice(adata.n_obs, size=max_cells, replace=False))
71
+ adata = adata[idx].copy()
72
+ print(f"[{name}] subsampled to {adata.n_obs} cells")
73
+
74
+ # resolve columns with graceful fallback
75
+ if pert_col not in adata.obs:
76
+ raise KeyError(f"pert_col '{pert_col}' not in obs: {list(adata.obs.columns)}")
77
+ if batch_col is not None and batch_col not in adata.obs:
78
+ print(f"[{name}] batch_col '{batch_col}' missing -> single batch")
79
+ batch_col = None
80
+ if celltype_col is not None and celltype_col not in adata.obs:
81
+ celltype_col = None
82
+
83
+ # --- normalization: cp10k + log1p ---
84
+ if not sp.issparse(adata.X):
85
+ adata.X = sp.csr_matrix(adata.X)
86
+ # guard: ensure raw-ish counts (integer). if already normalized, skip.
87
+ x0 = adata.X[:200].toarray()
88
+ looks_counts = np.allclose(x0, np.round(x0)) and x0.max() > 30
89
+ if looks_counts:
90
+ sc.pp.normalize_total(adata, target_sum=1e4)
91
+ sc.pp.log1p(adata)
92
+ print(f"[{name}] applied CP10k + log1p")
93
+ else:
94
+ print(f"[{name}] data appears pre-normalized (max={x0.max():.2f}); skipping norm")
95
+
96
+ # --- hvg selection (on all cells) ---
97
+ sc.pp.highly_variable_genes(adata, n_top_genes=n_hvg, flavor="seurat")
98
+ adata = adata[:, adata.var["highly_variable"]].copy()
99
+ genes = list(map(str, adata.var_names))
100
+ Xhvg = adata.X.tocsr().astype(np.float32)
101
+ print(f"[{name}] HVG matrix: {Xhvg.shape}")
102
+
103
+ # --- pca (centered, invertible) ---
104
+ from sklearn.decomposition import PCA
105
+
106
+ Xd = Xhvg.toarray()
107
+ mean = Xd.mean(axis=0).astype(np.float32)
108
+ pca = PCA(n_components=n_pca, random_state=seed)
109
+ emb = pca.fit_transform(Xd - mean).astype(np.float32)
110
+ components = pca.components_.astype(np.float32) # (d, h)
111
+ evr = float(pca.explained_variance_ratio_.sum())
112
+ print(f"[{name}] PCA d={n_pca} explains {evr:.3f} variance")
113
+
114
+ # --- parse perturbations ---
115
+ pert = adata.obs[pert_col].astype(str).values
116
+ genes_per_cell = [parse_perturbation(p, control_label, sep) for p in pert]
117
+ nperts = np.array([len(g) for g in genes_per_cell])
118
+ is_control = nperts == 0
119
+
120
+ obs = pd.DataFrame(
121
+ {
122
+ "perturbation": pert,
123
+ "n_pert_genes": nperts,
124
+ "is_control": is_control,
125
+ "pert_genes": [";".join(g) for g in genes_per_cell],
126
+ "batch": (adata.obs[batch_col].astype(str).values if batch_col else "0"),
127
+ "celltype": (
128
+ adata.obs[celltype_col].astype(str).values if celltype_col else name
129
+ ),
130
+ }
131
+ )
132
+
133
+ # drop perturbations (non-control) with too few cells
134
+ vc = obs.loc[~obs.is_control, "perturbation"].value_counts()
135
+ keep_perts = set(vc[vc >= min_cells_per_pert].index)
136
+ keep_mask = obs.is_control.values | obs.perturbation.isin(keep_perts).values
137
+ if keep_mask.sum() < len(obs):
138
+ obs = obs.loc[keep_mask].reset_index(drop=True)
139
+ Xhvg = Xhvg[keep_mask]
140
+ emb = emb[keep_mask]
141
+ print(f"[{name}] dropped low-count perts -> {keep_mask.sum()} cells, "
142
+ f"{len(keep_perts)} perturbations")
143
+
144
+ # --- pseudobulk per perturbation (gene space, hvg-log) ---
145
+ control_mean = np.asarray(Xhvg[obs.is_control.values].mean(axis=0)).ravel().astype(np.float32)
146
+ pb_labels, pb_vecs = [], []
147
+ for p, sub in obs.groupby("perturbation"):
148
+ if p == control_label:
149
+ continue
150
+ idx = sub.index.values
151
+ pb_labels.append(p)
152
+ pb_vecs.append(np.asarray(Xhvg[idx].mean(axis=0)).ravel())
153
+ pb_vecs = np.asarray(pb_vecs, dtype=np.float32)
154
+
155
+ # --- write cache ---
156
+ sp.save_npz(os.path.join(out_dir, "Xhvg.npz"), Xhvg)
157
+ np.save(os.path.join(out_dir, "pca_emb.npy"), emb)
158
+ np.save(os.path.join(out_dir, "pca_components.npy"), components)
159
+ np.save(os.path.join(out_dir, "pca_mean.npy"), mean)
160
+ np.savez(
161
+ os.path.join(out_dir, "pseudobulk.npz"),
162
+ labels=np.array(pb_labels),
163
+ vecs=pb_vecs,
164
+ control_mean=control_mean,
165
+ )
166
+ obs.to_parquet(os.path.join(out_dir, "obs.parquet"))
167
+ with open(os.path.join(out_dir, "genes_hvg.txt"), "w") as f:
168
+ f.write("\n".join(genes))
169
+
170
+ singles = [p for p in pb_labels if len(parse_perturbation(p, control_label, sep)) == 1]
171
+ combos = [p for p in pb_labels if len(parse_perturbation(p, control_label, sep)) >= 2]
172
+ all_genes = sorted({g for p in pb_labels for g in parse_perturbation(p, control_label, sep)})
173
+ meta = {
174
+ "name": name,
175
+ "operation": operation,
176
+ "control_label": control_label,
177
+ "sep": sep,
178
+ "n_cells": int(obs.shape[0]),
179
+ "n_control": int(obs.is_control.sum()),
180
+ "n_hvg": len(genes),
181
+ "n_pca": n_pca,
182
+ "pca_explained_var": evr,
183
+ "n_perturbations": len(pb_labels),
184
+ "n_singles": len(singles),
185
+ "n_combos": len(combos),
186
+ "n_unique_target_genes": len(all_genes),
187
+ "n_batches": int(obs.batch.nunique()),
188
+ "n_celltypes": int(obs.celltype.nunique()),
189
+ }
190
+ save_json(meta, os.path.join(out_dir, "meta.json"))
191
+ print(f"[{name}] DONE -> {out_dir}")
192
+ print(meta)
193
+ return meta
194
+
195
+
196
+ DATASETS = {
197
+ "norman": dict(
198
+ raw="data/raw/NormanWeissman2019_filtered.h5ad",
199
+ name="norman", operation="activation", control_label="control",
200
+ batch_col="gemgroup", celltype_col="celltype",
201
+ ),
202
+ "replogle_k562": dict(
203
+ raw="data/raw/ReplogleWeissman2022_K562_essential.h5ad",
204
+ name="replogle_k562", operation="interference", control_label="control",
205
+ batch_col="gemgroup", celltype_col="celltype", max_cells=120000,
206
+ ),
207
+ "replogle_rpe1": dict(
208
+ raw="data/raw/ReplogleWeissman2022_rpe1.h5ad",
209
+ name="replogle_rpe1", operation="interference", control_label="control",
210
+ batch_col="gemgroup", celltype_col="celltype", max_cells=120000,
211
+ ),
212
+ }
213
+
214
+
215
+ if __name__ == "__main__":
216
+ ap = argparse.ArgumentParser()
217
+ ap.add_argument("dataset", choices=list(DATASETS.keys()))
218
+ ap.add_argument("--out-root", default="data/processed")
219
+ ap.add_argument("--n-hvg", type=int, default=2000)
220
+ ap.add_argument("--n-pca", type=int, default=50)
221
+ args = ap.parse_args()
222
+ cfg = dict(DATASETS[args.dataset])
223
+ raw = cfg.pop("raw")
224
+ out = os.path.join(args.out_root, cfg["name"])
225
+ preprocess(raw, out, n_hvg=args.n_hvg, n_pca=args.n_pca, **cfg)