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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Bryan Cheng
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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 ---
-license: apache-2.0
+language: en
+license: mit
+tags:
+  - biology
+  - single-cell
+  - perturbation-prediction
+  - flow-matching
+  - pytorch
 ---
+
+# PIVOT
+
+![PIVOT overview](assets/figure1.png)
+
+**Perturbation-Informed Vector-Field Optimization for Transcriptomic State Control**
+
+With PIVOT, we predict the expression response to a genetic intervention and nominate genes or combinations for a desired response. To this end, we train a perturbation-conditioned endpoint map and use its predictions to rank candidates or optimize intervention embeddings. We then project each optimized embedding to admissible interventions and rerank them by predicted endpoint reward.
+
+The initial Norman experiments recovered 20 of 26 held-out gene pairs within five nominations. A population loss reduced the recorded MMD from 0.3034 to 0.0507 while retaining effect correlation above 0.83. The manuscript and [result provenance](docs/results.md) describe those experiments, their preprocessing scope, and the independent-outcome evaluation.
+
+## Install and run
+
+Python 3.11 to 3.13 is supported. The small example runs on a CPU. CUDA is optional for full training.
+
+```bash
+python -m pip install -e '.[test]'
+python scripts/quickstart.py --output runs/example
+python -m pytest -q
+```
+
+The example starts from 920 measured Norman cells across 20 noncontrol conditions. It fits training-only expression features and PCA, trains for two epochs, evaluates PIVOT and ridge regression, predicts responses, runs three nomination procedures, and exports a comparison plot. It writes the cache, checkpoints, per-condition results, candidate lists, and plots under `runs/example/`. Use a new output directory for another run.
+
+The short run verifies the complete workflow. Full benchmark reproduction requires the original datasets and the configurations described below.
+
+## Full datasets and training
+
+The adapters use [scPerturb Zenodo record 10044268](https://zenodo.org/records/10044268), with fixed filenames and checksum verification. Input count matrices are normalized independently per cell and log transformed. Existing normalized matrices require `--input-scale log1p`.
+
+```bash
+python scripts/download_data.py norman --output data/raw
+python -m pivot.cli prepare \
+  --raw data/raw/NormanWeissman2019_filtered.h5ad \
+  --dataset norman --split combination --seed 0 --output data/norman_combination
+python -m pivot.cli train \
+  --cache data/norman_combination --config configs/full.json \
+  --device cuda --output runs/norman_full
+python -m pivot.cli evaluate \
+  --cache data/norman_combination --checkpoint runs/norman_full/best.pt \
+  --catalog combination --reward cosine --device cuda --output runs/norman_full/test.json
+```
+
+For Replogle, download `replogle_k562` and use `ReplogleWeissman2022_K562_essential.h5ad` with `--dataset replogle_k562 --batch-col batch --celltype-col cell_line` during preparation. Supported splits are `cell`, `perturbation`, `gene`, and, for Norman, `combination`. Gene holdouts exclude all training conditions containing the held-out gene. [Data documentation](docs/data.md) defines each partition and the independent response cells used to score nominated actions.
+
+Run the implemented baselines and loss ablations across three seeds:
+
+```bash
+python scripts/run_matrix.py \
+  --raw data/raw/NormanWeissman2019_filtered.h5ad \
+  --dataset norman --split combination --device cuda \
+  --ablations --output runs/norman_matrix
+```
+
+The [experiment matrix](docs/experiments.md) maps manuscript comparisons to commands and identifies external integrations that remain to be completed.
+
+## Prediction and nomination
+
+A query target is a NumPy array with shape `(n_target_cells, n_pca)` in the saved cache's PCA basis. The quickstart writes `runs/example/target.npy` from held-out measured cells. Prediction exports latent coordinates, reconstructed expression, gene order, and source-cell identifiers.
+
+```bash
+python -m pivot.cli nominate \
+  --cache runs/example/cache --checkpoint runs/example/model/best.pt \
+  --target runs/example/target.npy --catalog all --search guidance \
+  --output runs/example/new_nomination.json
+python scripts/plot_results.py runs/example/pivot.json runs/example/ridge.json \
+  --output runs/example/comparison
+```
+
+Use `--search exhaustive` to rank the complete catalog, `guidance` to optimize and project an embedding, or `greedy` to construct a gene set. Greedy output includes whether the constructed set occurs in the measured catalog. Scores are predictions; measured response evidence is returned by the evaluation workflow.
+
+For a control state $c_0$ and intervention embedding $e$, the endpoint map is $X_\theta(0,1,c_0,e)$. Guidance averages endpoint rewards over source cells and differentiates that average with respect to $e$. The [method notes](docs/method.md) connect the equations to the implementation.
+
+## Results, checkpoints, and validation
+
+`results/legacy/` and `checkpoints/legacy/` preserve the original recorded experiments. Their preprocessing and evaluation conventions differ from the corrected `split-first-v1` workflow. The corrected loader checks the cache identity and rejects historical checkpoints. The original source and loading instructions are retained for reproducing the old records.
+
+The corrected example checkpoint is included at `checkpoints/example/best.pt`, with its matching cache at `fixtures/example_cache/`. The saved target query is `fixtures/example_target.npy`. These files support immediate prediction and nomination without retraining.
+
+The corrected small measured-data run exercised preparation, training, reload, evaluation, prediction, exhaustive ranking, guidance, greedy selection, and plot export. [Validation details](docs/validation.md) record the executed configurations and remaining full-scale work. No corrected full-scale results are claimed in the blank manuscript tables.
+
+| Directory | Contents |
+|---|---|
+| `src/pivot/` | Data, model, training, inference, and evaluation modules |
+| `configs/` | Full model, population-loss, and ablation configurations |
+| `scripts/` | Downloads, complete example, experiment matrix, and figures |
+| `tests/` | Mathematical and data-isolation regression checks |
+| `fixtures/` | Small measured dataset and its attribution |
+| `results/` | Original measurements and executed workflow checks |
+| `checkpoints/` | Corrected example checkpoint and original trained models |
+| `assets/` | Shared Figure 1, editable vector source, fonts, and licenses |
+| `docs/` | Methods, experiments, provenance, and validation |
+| `reproducibility/` | Original source, upstream revision, bibliography records |
+
+## Attribution
+
+Original PIVOT code: Bryan Cheng, 2026, MIT license. The small Norman dataset derives from scPerturb, CC BY 4.0, and the Norman et al. CRISPRa screen. Font files retain their Ubuntu Font Licence. See `LICENSE`, `fixtures/provenance.json`, and `assets/fonts/UFL.txt`.
+
+The manuscript title is *PIVOT: Perturbation-Informed Vector-Field Optimization for Transcriptomic State Control*. Author order and the final archival citation should be added after the authors confirm the submission metadata.

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+UBUNTU FONT LICENCE Version 1.0
+-------------------------------
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+PREAMBLE
+This licence allows the licensed fonts to be used, studied, modified and
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+
+DEFINITIONS
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+as received under this licence.
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+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting -- in part or in whole -- any of the components of the
+Original Version, by changing formats or by porting the Font Software to
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+"Copyright Holder(s)" refers to all individuals and companies who have a
+copyright ownership of the Font Software.
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+To "Propagate" a work means to do anything with it that, without
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+a redistribution fee), making available to the public, and in some
+countries other activities as well.
+
+PERMISSION & CONDITIONS
+This licence does not grant any rights under trademark law and all such
+rights are reserved.
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of the Font Software, to propagate the Font Software, subject to
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checkpoints/legacy/ablations/norman_perturbation/frac_0.5/config.json

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checkpoints/legacy/ablations/norman_perturbation/match_random/config.json

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