diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..dc1d3ffbb56ab21384e9bab4ce369b41bab3bc17 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +assets/figure1.png filter=lfs diff=lfs merge=lfs -text +assets/fonts/Ubuntu-Bold.ttf filter=lfs diff=lfs merge=lfs -text +assets/fonts/Ubuntu-Medium.ttf filter=lfs diff=lfs merge=lfs -text +assets/fonts/Ubuntu-Regular.ttf filter=lfs diff=lfs merge=lfs -text +fixtures/norman_small.h5ad filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..230d745ccffce48280b9d23c985a763058fd484a --- /dev/null +++ b/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. diff --git a/README.md b/README.md index 154df8298fab5ecf322016157858e08cd1bccbe1..f7bdd20901ce6be987c875989131afb601ff7e52 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,108 @@ --- -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. diff --git a/assets/figure1.pdf b/assets/figure1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2e51e8d8ef2817651602b4faa3c7c6c913dd4daa Binary files /dev/null and b/assets/figure1.pdf differ diff --git a/assets/figure1.png b/assets/figure1.png new file mode 100644 index 0000000000000000000000000000000000000000..1c7f05218fe3e2d177d4f33d0aa22cf1df51dd6f --- /dev/null +++ b/assets/figure1.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c810991d3a4d983edaf03e859fc8e9cb9b09ca049967f63eda9929d435c06820 +size 333957 diff --git a/assets/figure1.svg b/assets/figure1.svg new file mode 100644 index 0000000000000000000000000000000000000000..6b5970c017e53c0d930ea60a296875bcdb654a63 --- /dev/null +++ b/assets/figure1.svg @@ -0,0 +1,3951 @@ + + + + + + + + 2026-09-18T04:15:25.026366 + image/svg+xml + + + Matplotlib v3.10.8, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/assets/fonts/UFL.txt b/assets/fonts/UFL.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae78a8f94eae372cb1ca4e14b67244342fce604e --- /dev/null +++ b/assets/fonts/UFL.txt @@ -0,0 +1,96 @@ +------------------------------- +UBUNTU FONT LICENCE Version 1.0 +------------------------------- + +PREAMBLE +This licence allows the licensed fonts to be used, studied, modified and +redistributed freely. 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"weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 10.0, + "match": "batch", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/distribution_2.json b/configs/distribution_2.json new file mode 100644 index 0000000000000000000000000000000000000000..3f40811a30ad4852784677bc496ed998f6e85c3f --- /dev/null +++ b/configs/distribution_2.json @@ -0,0 +1,17 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 2.0, + "match": "batch", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/full.json b/configs/full.json new file mode 100644 index 0000000000000000000000000000000000000000..38acbf0c8bc6939fab54350a7d5bb5f6a1e58c46 --- /dev/null +++ b/configs/full.json @@ -0,0 +1,17 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "batch", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/gene_only.json b/configs/gene_only.json new file mode 100644 index 0000000000000000000000000000000000000000..f3c4d7e440c370eb304c9a3a080285ca1c7f5845 --- /dev/null +++ b/configs/gene_only.json @@ -0,0 +1,18 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "batch", + "seed": 0, + "device": "cpu", + "threads": 4, + "rep_mode": "gene_only" +} diff --git a/configs/map_only.json b/configs/map_only.json new file mode 100644 index 0000000000000000000000000000000000000000..e02d699da3c318ef970627b1d09fbd70c64c0602 --- /dev/null +++ b/configs/map_only.json @@ -0,0 +1,17 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 0.0, + "lam_semi": 0.0, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "batch", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/map_semigroup.json b/configs/map_semigroup.json new file mode 100644 index 0000000000000000000000000000000000000000..19934c6bbb463355b8a7b24a8539ff9e1eb95e46 --- /dev/null +++ b/configs/map_semigroup.json @@ -0,0 +1,17 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 0.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "batch", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/map_tangent.json b/configs/map_tangent.json new file mode 100644 index 0000000000000000000000000000000000000000..d02063e43e4cb7a44500ef6ba02416dc8b42b1f8 --- /dev/null +++ b/configs/map_tangent.json @@ -0,0 +1,17 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.0, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "batch", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/nearest_pairing.json b/configs/nearest_pairing.json new file mode 100644 index 0000000000000000000000000000000000000000..9b3a3f6ee276e4c0031a1aca794ac120af6e083a --- /dev/null +++ b/configs/nearest_pairing.json @@ -0,0 +1,17 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "nearest", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/random_pairing.json b/configs/random_pairing.json new file mode 100644 index 0000000000000000000000000000000000000000..ef08f3ee03676cee41d8b6c48d1927d4d470751d --- /dev/null +++ b/configs/random_pairing.json @@ -0,0 +1,17 @@ +{ + "d_pert": 64, + "hidden": 512, + "depth": 4, + "epochs": 60, + "batch_size": 1024, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "random", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/configs/small.json b/configs/small.json new file mode 100644 index 0000000000000000000000000000000000000000..9ebbe0dc8add27456e7cc5a708893c2945b60bd1 --- /dev/null +++ b/configs/small.json @@ -0,0 +1,17 @@ +{ + "d_pert": 16, + "hidden": 64, + "depth": 2, + "epochs": 2, + "batch_size": 64, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "match": "random", + "seed": 0, + "device": "cpu", + "threads": 4 +} diff --git a/docs/data.md b/docs/data.md new file mode 100644 index 0000000000000000000000000000000000000000..35c63d37ceaed1137eaae1587f7efe771858dcc0 --- /dev/null +++ b/docs/data.md @@ -0,0 +1,20 @@ +# Data and partitions + +The full adapters read count matrices from scPerturb record 10044268. Norman contains K562 CRISPR activation experiments with single-gene and two-gene conditions. The Replogle adapter selects the K562 essential-gene interference screen. The source files are approximately 0.70 GB and 1.55 GB, respectively. + +Required input: an AnnData `.h5ad` file with unique cell and feature IDs, a nonnegative count matrix, and `obs.perturbation`. The control label is `control`; underscore-separated gene names denote combinations. Optional context columns default to `gemgroup` and `celltype`; they can be set explicitly with CLI flags. For Replogle, pass `--batch-col batch --celltype-col cell_line` to preserve the source context columns. Missing context columns produce one documented dataset-wide context. A requested batch-matching operation fails if no allowed controls match a response cell. + +The cache contains normalized sparse expression, PCA coordinates, basis and mean, feature order, cell metadata, and checksums. PCA is an affine reconstruction of retained components and is lossy. Metadata-only gene identities may include held-out genes. Their learned embedding rows receive no response supervision when those genes are absent from training. + +| Partition regime | Unit assigned to a partition | +|---|---| +| `cell` | Cells within every condition, 70/15/15 | +| `perturbation` | Complete intervention labels, approximately 70/10/20 | +| `combination` | Two-gene conditions, approximately 70/10/20; singles remain in training | +| `gene` | Gene identities; every containing intervention follows the held-out gene | + +Controls are partitioned independently. Approximately 10% of each condition's cells are reserved as `reference` outcomes before fitting expression features. Reference cells do not fit the model, determine validation selection, or form target queries. They are used to measure the response quality of a nominated action. The independent experimental unit for summary intervals is a condition. Separate cells from one screen do not establish cross-study biological replication. + +The included fixture has 920 measured Norman cells, 20 perturbed conditions, 120 controls, and 33,694 source features. The selection uses seed 17. `provenance.json` records the source checksum and retained cell IDs. The example selects approximately 200 variable genes; the dispersion threshold can retain ties, so the tested cache retained 201 features and 10 principal components. + +For cross-study evaluation, freeze the source transformation and explicitly align genes before projection. No cross-cell-type or cross-study transfer result is reported in this release. diff --git a/docs/experiments.md b/docs/experiments.md new file mode 100644 index 0000000000000000000000000000000000000000..79bf3446629feea530ab1e93c33465c256341e80 --- /dev/null +++ b/docs/experiments.md @@ -0,0 +1,36 @@ +# Experiments for the ICLR draft + +The core question is whether a direct endpoint map improves intervention selection when the objective includes population structure and gene interactions. The initial results support a focused study of Norman combinations and population matching. + +| Comparison | Executable workflow | Status | +|---|---|---| +| Corrected Norman response and pair nomination | `scripts/run_matrix.py --dataset norman --split combination` | Implemented; full runs pending | +| Mean control, average effect, additive, ridge, endpoint MLP, conditional MLP | `python -m pivot.cli evaluate --baseline NAME` | Implemented | +| Map/tangent/composition ablations | `scripts/run_matrix.py --ablations` | Implemented; full runs pending | +| Population-loss weights 2 and 10 | `configs/distribution_2.json`, `configs/distribution_10.json` | Implemented; full runs pending | +| Guidance versus exhaustive ranking | `evaluate --initialization random` or `best` | Implemented with projection diagnostics | +| Reward sweep | `evaluate --reward centroid`, `cosine`, `mmd`, or `wasserstein` | Implemented; choose on validation targets | +| Replogle single-gene holdout | `--dataset replogle_k562 --split gene` | Adapter implemented; corrected full run pending | +| GEARS on common features and outcome cells | Original GEARS scripts in archived source | New shared-protocol adapter pending | +| CellFlow distributional comparison | Published implementation, matched data representation | External adapter pending | +| PAIRING/PDGrapher inverse comparison | Requires task and action-space alignment | Secondary external integration | +| Independent pair interaction residuals | Reserved single-gene and pair responses | Analysis implementation pending | +| Prospective new combinations | New perturbation experiment | Future experimental validation | + +Every `run_matrix.py` invocation also needs `--raw`, `--output`, and a device. See the README for the complete command. The matrix saves separate per-seed result files. `plot_results.py` exports each run's summaries as CSV and plots their measured values. Use the condition-level rows for paired comparisons; do not pool cells as independent replicates. + +## Priority for the remaining week + +1. Run the corrected Norman combination split with PIVOT, additive effects, ridge, and the matched cell-conditional MLP across three seeds. This is the main decision-quality comparison. +2. Repeat the map/tangent/composition and population-loss comparisons on those same splits. Select reward and hyperparameters on validation conditions. +3. Score selected actions against reserved measured responses. Report measured regret, Top-5, population discrepancy, and variation across seeds. Add the independent interaction-residual analysis. +4. Complete one strong external comparison. GEARS has an existing historical integration and is the first priority. CellFlow directly tests the population-model claim if a matched adapter is feasible. +5. Compare random-start guidance, warm-start guidance, and exhaustive ranking with initialization included in the budget. Preserve full target IDs and time both initial and amortized catalog scoring. + +A larger unrelated benchmark is lower priority than these comparisons. The manuscript's blank tables already contain the necessary rows. Full-scale results should replace those cells only after validation against the saved condition-level records. + +## Submission preparation + +The compiled draft uses the supplied ICLR 2027 style and an anonymous author block. The official limit is nine main-text pages. The draft AI-use section is intentionally blank. Complete an accurate disclosure and confirm the author order before submission. The public Hugging Face archive contains author attribution and must be anonymized separately if supplied directly to reviewers. + +Official requirements: [Author guidelines](https://iclr.cc/Conferences/2027/AuthorGuidelines), [AI policy](https://iclr.cc/Conferences/2027/AIPolicyForAuthors). diff --git a/docs/method.md b/docs/method.md new file mode 100644 index 0000000000000000000000000000000000000000..699723f20cd0018995f3b4f2766e788376eeb038 --- /dev/null +++ b/docs/method.md @@ -0,0 +1,24 @@ +# Equations and implementation + +The original PIVOT formulation is preserved: mean-pooled gene-operation embeddings, a residual endpoint map, map/tangent/composition losses, and projected endpoint-reward gradients. The optional population loss compares response distributions. + +| Paper quantity | Implementation | +|---|---| +| Cell-state representation and reconstruction | `data/preprocess.py`, `PerturbData.decode_to_genes` | +| Mean-pooled intervention embedding | `models/encoders.py`, `PerturbationEncoder` | +| Residual map and diagonal identity | `models/flow_map.py`, `FlowMap` | +| Map, tangent, and composition objectives | `training/losses.py`, `compute_losses` | +| Population MMD term | `training/train.py`, `evaluation/rewards.py` | +| Exhaustive catalog score | `evaluation/inference.py`, `endpoint_ranking` | +| Endpoint vector-Jacobian product | `evaluation/inference.py`, `reward_guidance` | +| Nearest embedding projection and reranking | `project_and_rerank` | +| Greedy gene-set construction | `greedy_combinatorial` | +| Independent measured candidate outcomes | `evaluation/runner.py`, `evaluate` | + +The cosine reward compares each predicted cell's control-relative effect with the target centroid effect. The centroid reward averages squared cell-to-centroid distances, so it includes predicted population spread. MMD and sliced-Wasserstein rewards operate on whole populations. + +Guidance uses normalized ascent with a fixed step size. Discrete projection can lower the continuous reward. Exhaustive ranking maximizes the same predictor score over a finite fixed catalog. Guidance therefore requires evaluation of computation, measured outcome quality, and the reward change after projection. The implementation records candidate-cell evaluations and query timings. Projection over embeddings also scans the catalog; its cost should be included in full runtime comparisons. + +The default full model uses a 64-dimensional intervention embedding, four 512-unit SiLU layers, Fourier time features, 60 epochs, batch size 1,024, and AdamW. `small.json` is a two-epoch workflow check with a 16-dimensional intervention embedding and two 64-unit layers. Configurations containing `distribution` activate population matching. The `map_*` configurations isolate the two auxiliary losses. + +Historical source files also contain expression-derived cluster features. They remain in the preserved original archive. The corrected training interface accepts metadata-only gene/operation representations and does not offer those outcome-derived clusters as independent pathway features. diff --git a/docs/results.md b/docs/results.md new file mode 100644 index 0000000000000000000000000000000000000000..fa09b06bfdceb860e639a1104fea66299c88a653 --- /dev/null +++ b/docs/results.md @@ -0,0 +1,32 @@ +# Result provenance and manuscript changes + +The original Hugging Face revision is recorded in `reproducibility/upstream.json`. Original experiment JSON files and trained checkpoints are preserved. The draft reports those measurements with their original preprocessing scope; corrected full-scale tables remain unfilled. + +| Manuscript comparison | Source result records | +|---|---| +| Response accuracy and baseline comparison | `norman_benchmark.json`, `norman_distloss.json` | +| Single-gene nomination | `norman_benchmark.json`, `norman_nom_improved.json` | +| Pair retrieval and residual correlation | `norman_combinatorial.json`, `norman_extra_ablations.json`, `norman_timing_scaling.json` | +| GEARS response and ranking | `pivot_vs_gears.json`, `gears_ranking.json` | +| Loss components | `norman_ablation_components.json` | +| Reward and guidance sweeps | `norman_ablation_reward.json`, `norman_ablation_guidance_steps.json` | +| Larger single-gene catalog | `replogle_k562_nom_improved.json` | + +Numerical records are in `results/legacy/`. The original source is in `reproducibility/original_source.zip`. It includes the historical GEARS workflows. Extract it into a separate directory when reproducing those experiments and use the corresponding original preprocessing. Historical model weights cannot be loaded with a corrected cache. + +The manuscript now leads with population matching and held-out pair nomination. It retains additive ranking and the cell-conditional MLP because they are necessary comparisons for those claims. Secondary experiments and detailed implementation differences appear in the appendix. + +Substantive corrections: + +1. PCA is a lossy projection with an affine reconstruction. Historical expression error and DE correlation used reconstructed observed expression as well as reconstructed predictions. +2. Feature selection and PCA were fitted before splitting. Some evaluation routines pooled all cells carrying a test label, creating training-cell overlap in the cell-holdout analysis. +3. The original semigroup branch used a state interpolated at a different starting time. The corrected branch recomputes the source state at its sampled time. +4. The default historical DE correlation used 20 selected coordinates. It differs from GEARS Pearson correlation on absolute expression. +5. The GEARS nomination task used 21 targets and 101 single-gene candidates; PIVOT used 22 and 105. Pair candidate pools were 128 and 131, respectively. +6. Several earlier significance statements referred to Top-1 while the underlying calculations used Top-5. Two summary records disagreed on a random-comparison bootstrap result. The draft uses recovery counts and the stored interval without those significance claims. +7. Effect clusters are expression-derived groups. They do not establish pathway recovery. Their high random-recovery rate appears in the paper. +8. Greedy search returned one set. The duplicated historical Top-5 proxy has been removed. +9. The recorded pair residual correlation subtracts the fitted additive baseline from both predicted and observed effects. A confirmatory residual analysis should use independent single-gene response cells, residual MSE, and zero/shuffled residual controls. +10. Guidance initialization, backward evaluations, and projection costs must be counted. A best-candidate initialization includes an exhaustive pass. + +The shared Figure 1 is schematic. No synthetic performance curves or invented experimental measurements are included. diff --git a/docs/validation.md b/docs/validation.md new file mode 100644 index 0000000000000000000000000000000000000000..c73fd2d559d051daeddedaa39c3023fab08bb90c --- /dev/null +++ b/docs/validation.md @@ -0,0 +1,25 @@ +# Executed validation + +Validation used Python 3.12 and a CPU. `reproducibility/environment.json` records the installed scientific packages. These checks verify implementation and data isolation; benchmark-scale accuracy is reported only where an existing experiment record supplies it. + +## Measured-data workflow + +The complete quickstart ran from the included Norman fixture: 920 measured cells, 20 noncontrol conditions, and 33,694 source genes. Training-only selection retained 201 variable genes and 10 principal components. The small model trained for two epochs and reloaded its validation-selected checkpoint. + +The run completed forward evaluation, independent-outcome nomination evaluation, response export, exhaustive catalog ranking, three-step embedding guidance, greedy gene selection, and CSV/PDF/PNG result export. Four held-out conditions were evaluated. The target query came from measured test cells. `results/validation/` contains the saved per-condition outputs and training history. `checkpoints/example/best.pt` and `fixtures/example_cache/` preserve the matching trained example. + +A separate one-epoch run exercised the population-loss branch with weight 2, two sampled conditions, and eight cells per condition. All six implemented baselines completed forward and nomination evaluation on the same small cache. Neural baselines used two epochs and 32 hidden units for this execution check. + +## Mathematical and data-isolation checks + +The regression suite checks diagonal identity, permutation-invariant intervention pooling, the composition loss's corresponding source time, finite-difference reward gradients, agreement of NumPy and PyTorch MMD, retrieval censoring, gene holdouts, disjoint partitions, training-only PCA fitting, invariance of fitted features to held-out expression changes, and invariance of model ranking to reserved outcome changes. All 11 checks passed. + +## Dataset and integration scope + +The Norman source download passed its published checksum. The Replogle source metadata and matrix shape were read from the published HDF5 file. Fetching response rows then stopped with HTTP 429, so Replogle training was not executed. Preparation supports its intervention labels and configurable context columns; use `--batch-col batch --celltype-col cell_line`. + +Corrected full-scale, multi-seed runs remain pending. The GEARS and CellFlow comparisons need shared-protocol adapters, and independent interaction-residual analysis needs its evaluation implementation. These are listed in `docs/experiments.md`. The example checkpoint is a workflow demonstration. Historical trained weights remain available with their original source and protocol metadata. + +## Archive checks + +The repository ZIP was extracted into a separate directory and installed into a virtual environment with `pip install --no-deps --no-build-isolation`. The environment used the recorded scientific dependencies already installed on the host. The imported package resolved to that environment's installed copy. All 11 tests passed there, and the complete quickstart ran again from the extracted measured-data fixture. The minimal Overleaf ZIP also compiled after extraction. 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"ACGATGTAGGGATACC", + "TTTCCTCGTTTAGGAA", + "TCGGTAAGTCCGTCAG", + "CCGGGATTCTACTCAT", + "CGGTTAAGTCAAAGCG", + "ACGATACTCCAACCAA", + "ATTCTACGTTTGGGCC", + "AGCAGCCGTTAAAGTG", + "CAGTCCTAGTCATCCA", + "TCAACGACAAAGCGGT", + "CTCGGGACACATGGGA", + "GTCAAGTGTAAGAGGA", + "TTAACTCCAGGGAGAG", + "TACTTGTCAGCTGTAT", + "GTTTCTAGTCCGACGT", + "GCATGATCAAGTAGTA", + "AACACGTCATCACCCT", + "TCTGGAAGTGGACGAT", + "GCCTCTATCTTCGGTC", + "AGAGCTTTCTTAGAGC", + "GGACGTCCAGCGTAAG", + "GAACGGAAGTTGAGTA", + "CCTACCAAGGATCGCA", + "ACCCACTGTGGTAACG", + "AAACCTGCAGTGAGTG", + "GGTGCGTAGAGTCGGT", + "TACGGTAGTCTCAACA", + "TAAACCGGTAATCACC", + "ATCCGAATCTGCTGTC", + "TCAATCTCATGGGACA", + "GTCCTCAAGACTAGGC", + "CGGAGTCCACTCTGTC", + "CGGAGCTCAGCCTTGG", + "TATCAGGTCACCGTAA", + "ATCATGGAGGACGAAA", + "CGTGTCTAGGGCTTCC", + "TTCTTAGGTACTCAAC", + "AACTTTCTCGCTAGCG", + "CAACCTCTCACAATGC", + "TCGGTAACATTCCTCG", + "CACATAGAGTGTGGCA", + "GACGCGTTCGTGGACC", + "GCGCGATCATACCATG-1", + "TCAGGATTCTTTCCTC", + "CGCGGTATCAGTTCGA", + "ACTTGTTCACAGCGTC", + "CAGCATACAGACACTT", + "GAGCAGACAACTGGCC", + "GCTTGAAAGACCGGAT", + "CACAGGCCACAAGACG", + "CTGATAGCACTCAGGC", + "AACTCCCAGAATAGGG", + "ACTTGTTCAAGTAATG", + "AGCGGTCAGCACGCCT", + "TGAGAGGTCCACGTTC", + "GCGAGAATCAGGTTCA", + "TTCGAAGCACTATCTT", + "CGGTTAAGTCTAGTCA", + "CATTCGCCACCATGTA", + "GTCACAAAGCACAGGT", + "TTCGAAGTCGATAGAA", + "ACGAGGAAGCGGCTTC", + "CTAGTGAAGTATCTCG", + "AGAATAGCATTCCTGC", + "TCTTCGGAGCCAGGAT", + "GTCGTAATCCAGATCA", + "CCACGGATCAACGGCC", + "GGGCACTCACCGAATT", + "AGCGTCGCACAAGACG", + "GACACGCGTAGCGTAG", + "GGCGACTCAAGCCCAC", + "AACTCAGGTAGGCATG", + "CAGATCAAGAGAGCTC", + "GGTGCGTCATACCATG", + "GGGTCTGAGTTTGCGT", + "TGGTTAGTCCAAGTAC", + "TGGGAAGGTCATCGGC-1", + "AGGTCCGAGACTGTAA", + "CCAGCGATCGCTAGCG", + "GGGACCTGTATGAAAC", + "CAGCAGCGTCCTAGCG", + "TAGTTGGAGGAATTAC", + "TGACTTTCATGGAATA", + "CACAAACAGACAGAGA", + "ACTTACTGTTAGGGTG", + "AAAGCAAGTCCCTACT", + "TCTGGAACAGCGTTCG", + "AGTAGTCTCCCATTAT", + "CGACTTCGTTCACGGC", + "CGATGTATCCTCAACC", + "TGCGGGTTCCTTGACC", + "CAGTCCTAGCGCTTAT", + "TTTATGCGTGAGTGAC", + "ACGGGTCGTCCCTACT", + "TGACTTTGTCAGTGGA", + "ATGAGGGTCCCAACGG", + "CGTCTACCAGCTCGCA", + "CTCGAAAGTCGACTAT", + "TATTACCAGACACGAC", + "GCAGCCAGTCACAAGG", + "ATCATGGTCTGCCAGG", + "AAAGCAAGTCTCCCTA", + "ACGTCAATCCATGCTC", + "ATTATCCAGACACTAA", + "AAGGTTCCAGGAATCG", + "ACTTACTGTTCAGTAC", + "CTAGAGTTCAACGGGA", + "GGGACCTGTATGGTTC", + "ATCTGCCCAGTCACTA", + "AACTGGTAGCCGCCTA", + "AGTGGGAGTTGCTCCT", + "CAGCCGATCTAAGCCA", + "CCGTGGAAGAATCTCC", + "CCTAAAGGTGAAAGAG", + "CTCGTACTCTCCCTGA", + "CATCGGGCATGCCTTC-1", + "GTTAAGCTCGTCCGTT", + "GAGCAGAGTCGAAAGC", + "CGGAGTCTCATGCTCC", + "CTCGGAGCACTACAGT", + "AAGCCGCTCAGAGACG", + "TTCTCCTCACGTCTCT", + "CTTTGCGGTGAACCTT", + "TTCTCCTCAATCTGCA", + "ATTGGACCAACAACCT", + "GTGCGGTCACTGAAGG", + "CGGCTAGCACGAGAGT", + "TGAGCCGGTCTCATCC", + "ACTTACTCAGCCTGTG", + "CTACACCGTCTGGAGA", + "GATTCAGAGATACACA", + "GTTAAGCCACTGTTAG", + "ACTTTCAAGTGGGTTG", + "AGAGCGACAGCTGTAT", + "CGGAGCTCAGACAGGT", + "GGATTACCAAGTAATG", + "GTCTCGTAGTGTTGAA", + "CGTCACTTCTGCCAGG", + "AGCGTATTCGCGTTTC", + "CGCTTCACAATCGGTT", + "TGCCCTAGTAACGACG", + "ACATGGTCACCTTGTC", + "CGAACATCAGATAATG", + "GTATTCTGTGTCGCTG-1", + "ACTGAACTCACTATTC", + "CACCTTGAGCGTGTCC", + "ATTCTACAGGTGTGGT", + "AGCTCTCCAGTGAGTG", + "TTAGGCACACCGGAAA", + "GGTATTGAGTGGACGT", + "GACGGCTCATAAGACA", + "CATCGAACATGCCTTC", + "TGACGGCCAAGAGGCT", + "TGATTTCCACACAGAG", + "GAAGCAGCAGATAATG", + "TCGCGAGTCGTGACAT", + "CTAACTTAGAGACGAA-1", + "TGTATTCCAGTTCCCT-1", + "ATTGGTGCAAAGAATC", + "ACACCGGGTTATCCGA", + "ACATACGTCCACGACG", + "ATGAGGGCAGTATCTG", + "CGTTCTGTCGGCTTGG", + "ATGCGATCAACGATCT", + "CGGACTGTCAGAGACG", + "TGTGGTACAGGGTATG", + "TGCCCATTCCCTTGTG", + "CGTGAGCGTCACTGGC", + "GGTGAAGTCTCAAACG", + "ACCCACTGTTCAGGCC", + "TTGAACGTCCGCGTTT", + "TGTCCCACAGCAGTTT", + "TAGTTGGGTTGGACCC", + "CGTGTAATCCATGCTC", + "TCGTACCCAGCTGTTA", + "GTACTTTTCGTGGGAA", + "GTATCTTGTCATCGGC", + "GTTCGGGTCTTGCATT", + "TTGCGTCGTTCGGCAC", + "CACTCCAGTGCAGGTA", + "ACACTGAAGAAACCGC", + "ATCCGAAGTTCTGAAC", + "GTACGTATCAACGCTA", + "GTTCATTCAGCTCCGA", + "AAGCCGCTCTCTTATG", + "GTGTTAGTCCACTCCA", + "GCGCAACCATTAGCCA", + "TCGCGAGCACCTCGGA", + "TCGGTAAAGCGCCTCA", + "CTAAGACGTCGAACAG", + "CAAGTTGGTGGCCCTA", + "TCGCGTTGTGTGCGTC", + "CTTACCGGTAAACGCG", + "TTGCCGTCACGGTTTA", + "CATCAGAAGTGCCATT", + "AGCATACTCAGCTCGG", + "AGGGTGAGTACCGCTG", + "TAGTTGGTCGTCCGTT", + "GTATCTTAGACAAAGG", + "GGGAGATGTCTCGTTC-1", + "GCGCAACAGTGGGCTA-1", + "GTCTCGTTCGCGTTTC", + "CTAGAGTTCCCTTGTG", + "GCAATCAAGCTCCCAG", + "CGCGTTTTCGCACTCT", + "GGAGCAATCCTATTCA", + "GAACCTATCTTCCTTC", + "GTACGTATCACAGTAC", + "TCTATTGTCGCAGGCT", + "CACCAGGAGGGAGTAA", + "TACTTGTTCGTAGATC", + "GCACTCTTCAAACGGG", + "ATTACTCAGGATGTAT", + "GCACATAAGCGGATCA", + "GCTGGGTAGCGAGAAA", + "CTACATTGTTAAGAAC", + "CGACTTCTCCTTCAAT", + "TGAGCCGAGAGTCTGG", + "ACGAGCCTCGCATGGC-1", + "CTACCCATCAGTGCAT", + "CCCATACCAGGTCCAC", + "TGTATTCTCGCCGTGA", + "ATCTACTGTGCACGAA", + "CAACCTCTCTTACCGC-1", + "TCAACGAAGTTTAGGA", + "ACGCCGACATGCCTTC", + "ATTCTACTCCGTTGCT", + "AGTGTCATCAGTACGT", + "TGCGGGTCACAAGTAA", + "TTCGAAGAGCATCATC", + "TCTGAGACACTTGGAT", + "GCATGCGGTCCTCTTG", + "GGGCATCGTTTGGGCC-1", + "ACACCCTTCAATAAGG", + "CGAGAAGGTAGCAAAT", + "TTGCGTCCACCTCGGA", + "ACTTACTAGAGACTTA", + "CTCGGGAAGTGAAGTT", + "CTTCTCTTCGGCGCAT", + "CAGAGAGTCGTGGGAA", + "CCATTCGAGTGCGATG", + "TGAGGGATCAATCACG", + "CGTTCTGAGATGCCAG-1", + "ATAACGCCAGGATCGA", + "CCGGGATTCTGACCTC", + "CGCTATCCATAGGATA", + "TTTGCGCTCATTCACT-1", + "GCACATATCTCTGCTG", + "AAACGGGAGCTCTCGG", + "GCCTCTAGTATCGCAT", + "GTGCAGCAGATGTGTA", + "TTGGAACAGTGAACGC", + "GTCACAAGTTACGCGC", + "CTCTGGTAGATATGCA", + "CCTACACAGTGCCAGA", + "GAGTCCGCAATCCGAT", + "AGGTCATAGAGGGATA", + "GAACCTATCTCTTGAT", + "TGGCCAGCAATGGTCT", + "GTCGGGTGTGCTTCTC", + "GATCAGTTCGCAAACT", + "GCGCCAAGTACCGAGA", + "CCGTACTTCAGTGCAT", + "CTGAAACCAATGTTGC", + "TGGCGCAAGAAAGTGG", + "AGGTCCGTCGTCCGTT", + "TGAGCCGTCACTCTTA-1", + "CAAGGCCAGTGACATA", + "CTGGTCTGTGCATCTA", + "GTATTCTGTGTTCGAT", + "TTGTAGGCAAGGTGTG", + "TGGTTCCTCTTGCCGT", + "CATATTCGTACCGTTA", + "TGGTTCCGTGGGTATG-1", + "AATCCAGTCCGCGTTT", + "CTACGTCCACTGAAGG", + "ATGAGGGTCGATAGAA", + "AGAATAGTCAGTTAGC", + "TACGGATAGTGCGTGA", + "TACGGTATCACAGTAC", + "CGTAGCGTCACGAAGG", + "TAAGTGCGTGGGTCAA", + "ATCATCTCAAGAAGAG", + "CAACTAGTCCCTAACC", + "CCGTACTTCGAATCCA", + "TAGACCAGTACGCACC", + "GTGAAGGAGGTGATTA", + "CTGATAGTCGACAGCC", + "GCGGGTTCAGTTAACC", + "CGGAGTCGTATGGTTC", + "CTTAACTGTCTACCTC", + "TTTCCTCGTCATTAGC", + "AGAGTGGGTCTTTCAT", + "AGTGGGATCACATGCA", + "AACACGTAGGAACTGC", + "GACTAACTCTACTTAC", + "TCATTTGAGTCCTCCT", + "TACACGATCCATTCTA", + "AAATGCCAGGCATTGG", + "AGCAGCCGTCGACTGC", + "CAAGATCGTACGCACC-1", + "CGTCCATTCTACTATC", + "TATGCCCGTCAACATC-1", + "CTCGAGGGTGCGATAG", + "TGAGAGGAGAATTGTG", + "CTCGTCATCGGAATCT", + "GTGCATAAGCCCAGCT", + "TAAGAGAGTCCCTTGT", + "TGTGGTAAGGACGAAA", + "GCGACCAAGGCTAGCA", + "CTAAGACAGTGATCGG", + "AGTGGGACAGGCTGAA", + "CACACCTAGCTTTGGT", + "AAAGATGGTTGTCGCG-1", + "GGGACCTCAACTGCTA", + "AGTCTTTGTGGGTATG", + "AGTAGTCGTGCACCAC", + "CGTGAGCTCAACTCTT", + "TACCTATTCTCGAGTA", + "TGCCAAAGTAAATGAC", + "AGAATAGGTCTCCACT", + "ATGCGATGTAGAAGGA", + "CATGGCGCACACTGCG", + "TGAAAGAAGCGTCAAG", + "ATCACGACACGTCAGC", + "CTGGTCTGTGCACTTA", + "CACCACTTCACAGTAC", + "CTCCTAGCACAGCGTC", + "AGATTGCGTCAACATC", + "AGTTGGTCACTTACGA", + "GCTTCCAAGCGATCCC", + "GATGCTAGTCATGCCG", + "TCGCGAGGTGACAAAT", + "TCTATTGTCCCTAATT", + "GTGTTAGTCTGGCGTG", + "GCTCCTAGTGTGGCTC", + "ACGCCGAGTGAAATCA", + "ACTTACTGTCTTTCAT", + "ATAAGAGGTGTTGAGG", + "GTTCATTCATTCCTCG", + "GATGAGGGTGACCAAG", + "GCGACCAAGACTTGAA", + "GCTGCTTAGAGCTTCT", + "AACTCAGAGCGGATCA", + "TCATTTGCATTATCTC", + "AATCCAGCAGTAGAGC", + "ACTATCTGTCCTGCTT", + "CTACACCAGCGGCTTC", + "GGACGTCTCATGCATG", + "TACGGGCTCCTAGTGA", + "GCATACAAGACCACGA", + "TAAACCGTCCGTACAA", + "TGCGTGGGTAGCCTAT", + "CGCTATCCAAGCGAGT", + "CCTACACAGAGCTGGT", + "CGACTTCGTAGCGTCC-1", + "CAACTAGAGGACGAAA", + "GACGGCTGTCTAGTGT", + "TCCACACAGTGGACGT", + "ACATGGTCACAACGTT", + "GAAGCAGTCCCTCAGT", + "TACTCGCCAGCCTTTC", + "CTAACTTAGACTGGGT", + "GGATTACGTTACGACT", + "AAACGGGCACCGGAAA", + "AGTAGTCAGCGAAGGG", + "GCATACATCGCCTGAG", + "TCTTTCCTCACTTACT", + "TACCTATGTGCACTTA", + "TTAACTCAGATCCTGT", + "GAAGCAGCAGCTGGCT", + "TAAGCGTAGGTAAACT", + "CCACGGAGTGACCAAG", + "TTCTCCTAGCGTGAAC", + "TTTATGCGTTACCGAT", + "ACATGGTGTTCCACGG", + "CGAGAAGCAAGCTGGA", + "CTCGTACAGCCACTAT", + "CTTCTCTTCTTAGAGC", + "CGAGAAGTCTACCAGA", + "AACTCTTCAGCTGTTA", + "CACAGTAAGCACAGGT", + "GGGACCTGTACCATCA", + "TGTATTCGTGCTGTAT", + "GTGTGCGAGCGTCAAG", + "CTAACTTCAAGTCTAC", + "CTACGTCTCAGTGTTG", + "CACAGGCGTAAATGTG", + "CTCTGGTAGAAACGAG", + "CCTACCAGTGGCGAAT", + "AAACGGGAGAATGTGT", + "ACACTGAGTGTGTGCC", + "GTGGGTCCAACGCACC", + "TGGGAAGCAAACAACA", + "AGCAGCCTCGGTCTAA", + "GCGCAACGTGTTTGGT", + "GAATGAATCGCCTGTT", + "CTGCTGTCACATGTGT", + "GGACAAGCAGATCGGA-1", + "AGTCTTTAGACTAGGC-1", + "TACGGATAGAGCAATT", + "ATCCACCCACGGACAA-1", + "AAGCCGCTCTATCCCG", + "GCGAGAAGTACTCAAC", + "TACTTACCATATACCG", + "CAGGTGCTCTTAGAGC-1", + "CCACGGAAGAGACTAT", + "CAGCCGACAGGGCATA", + "AAGGTTCCATCACCCT", + "TCATTACCATTCTCAT", + "GTCATTTGTCTAGAGG", + "ACGAGGACATGCTGGC", + "CACACTCCAGATCGGA", + "GTGAAGGCAAGAAGAG", + "TTCGGTCAGTGGTAAT", + "GTATCTTCAGCATACT", + "ACACCCTCAGGGAGAG", + "CCCTCCTTCTTGTACT", + "TCTTTCCTCCAAATGC", + "AGGTCATTCCACGAAT", + "TAAACCGGTCTAAAGA", + "AACACGTTCAAGCCTA", + "CCAATCCAGTGGGCTA", + "GTGCTTCAGGTGGGTT", + "TGACTAGTCCACGACG", + "CGAGAAGGTTGCTCCT", + "TTGCCGTCATCGGGTC", + "GGCTCGAGTCTCTTTA", + "CGTTCTGTCGCATGAT", + "TTATGCTCATTAGGCT", + "CGGCTAGTCAGTCAGT", + "TTTCCTCGTCTGATCA", + "AGCCTAATCTCTAAGG", + "TTATGCTTCCGTTGCT", + "CATCGAACACCCAGTG", + "CGAATGTCACGCCAGT", + "CCACGGAAGAGAGCTC", + "TACAGTGGTAGGCATG", + "CATCGGGAGAGTACAT", + "ATGGGAGTCTGCTGTC", + "CAGCTGGCACGAGGTA", + "CAAGATCCACAGTCGC-1", + "GTGCTTCCAGCGTCCA", + "GACGCGTGTCGAGTTT", + "TCAGGATAGAAACCTA", + "TTGTAGGAGTACGACG", + "GCTTCCAAGTGTTTGC", + "AGCTTGAGTTACGACT", + "TCAATCTAGAAGCCCA-1", + "ACGCCAGGTAATTGGA", + "CCTTTCTGTAAGAGAG", + "AGCTCTCAGCTGTCTA", + "CGTGAGCGTGAAATCA", + "ATTTCTGCATGCCTTC-1", + "CTTAGGATCAAGAAGT", + "GACTGCGTCGAATGGG", + "TGCGGGTTCAGCCTAA", + "GCATGTACACTGAAGG", + "TCGTAGATCCACTCCA", + "CCCAATCAGTGACATA", + "CAGCTGGGTCATACTG", + "CTAAGACGTCACTTCC", + "GTAACTGTCGCTTGTC", + "TAAACCGTCTTGTACT", + "GCACTCTGTTTGACAC", + "GTCGTAAAGAGGTTAT", + "TTCTACACATGTCGAT", + "AAGGAGCTCCCTCAGT", + "TTCTACATCGATGAGG", + "CATATTCCAGCGTTCG", + "TACTTGTAGCAATCTC", + "TCAGCTCCACGTCTCT", + "CTCCTAGCACACGCTG", + "TGAAAGAGTACTTCTT", + "ATAACGCGTACCGCTG", + "TTTGTCAAGCGATAGC", + "CATCAAGAGTAGGCCA", + "CTAACTTCAGTCTTCC-1", + "GCTCCTAGTCGCTTCT-1", + "GGTGCGTAGCTAAACA", + "GCGAGAACAAATTGCC", + "GTAACGTGTAAGTGTA", + "GTACGTAGTCACTTCC", + "GTTAAGCCAGGAACGT-1", + "AGTTGGTCACGCTTTC", + "GCATGATCAGACGCTC", + "GCCTCTAAGTGCCATT", + "CGATCGGAGGAGTCTG", + "TTGAACGCAGGATTGG", + "CTGAAGTCACTCGACG", + "ACACCAAAGTCGTTTG", + "GTGAAGGGTTAAGAAC", + "TAAACCGAGTTAGGTA", + "ACATGGTGTCTCCATC-2", + "CTTTGCGCATAACCTG", + "TACGGGCCAACGATGG", + "CTCTGGTAGCGTTCCG", + "GTGCAGCGTCCTAGCG", + "AAGTCTGGTACCGGCT", + "ATCTGCCCAAAGCAAT", + "ACACCGGGTGTCGCTG", + "TAGAGCTAGTACACCT", + "AGTGGGACAAGTTGTC", + "TAAGCGTTCTCCGGTT", + "CCGTACTTCTCTTGAT", + "GCTCCTATCGGCCGAT-1", + "GACGTTATCACGGTTA", + "GAACGGACATTCCTCG", + "GAGGTGATCGGGAGTA", + "AAATGCCCAGGAACGT", + "CCACGGATCAGGATCT", + "GCGCAACGTTAGATGA", + "ACATACGTCGGCATCG", + "ATAAGAGCACATCCGG", + "CTCACACTCAACACAC", + "GAATGAACAATAGCAA", + "CGTCTACTCGTCCGTT", + "GCTGGGTGTGAAGGCT", + "GGGTCTGGTAGCCTCG", + "AGCATACTCTACTTAC", + "TATCTCATCTTGTCAT", + "ACTGCTCGTGTGAATA", + "GACTAACCACGAGAGT", + "GCGACCATCTACTCAT", + "ATCACGATCGTCGTTC", + "ACACCCTAGACTCGGA", + "CCGGGATGTGACAAAT", + "CTCATTAGTTCAGGCC", + "GACGCGTGTTGCTCCT", + "CGACTTCTCTGCAAGT-1", + "CTCTACGCACGCGAAA", + "CGCCAAGCACGCCAGT", + "ACCCACTTCAAGATCC", + "CACAAACTCGTCCAGG", + "TCAACGATCAGTCAGT", + "GGAATAACAGCCTATA", + "GCAGCCATCGTTGCCT", + "TGGCTGGTCAGAGCTT", + "CCTACCAGTCTAGTCA", + "AGCTCCTCATCACGAT", + "AGGCCACGTAATCACC", + "CAGCTGGTCAGTTCGA", + "TCTGGAATCCTTTCGG", + "TCCACACGTTGTTTGG", + "GCCAAATAGTGCGTGA", + "TGACAACTCGTCGTTC", + "ACTGAACCACCTGGTG", + "CAAGTTGTCGCCTGAG", + "GACACGCCATGCAATC", + "ACGCCGACAGCTGTGC", + "AAACCTGTCCATTCTA", + "GTGCGGTAGCTGATAA-1", + "ATCACGAGTCTTGCGG-1", + "GGAATAATCGACCAGC", + "TGTGTTTGTAGGAGTC", + "TAAGAGAGTGTTTGGT", + "TAGACCACAATTGCTG", + "CTCGTCATCCGCGCAA", + "GACACGCGTACCGTAT", + "TTATGCTGTTCCCTTG", + "ACGAGCCAGCTAGTTC", + "GAATGAACAACACGCC", + "GACACGCGTCGATTGT", + "CAGAATCTCCGAAGAG-1", + "GGCGTGTGTGCAACGA", + "ATGAGGGAGCCACCTG", + "AACCGCGAGATAGCAT", + "TGCTGCTCACCTATCC", + "AGGCCACTCGACAGCC", + "CGTAGCGAGTGTCCCG", + "CATCAAGAGAGGACGG-1", + "TTGAACGTCCGTAGGC", + "CGGTTAAAGCTGAAAT", + "TGGCCAGTCTTTACAC", + "TACGGATAGCCTATGT-1", + "CACAGGCGTTGCCTCT", + "TGAGGGAAGGCGACAT", + "ACGAGCCGTCGAACAG", + "ACTATCTGTGGGTCAA", + "TGGCGCAAGTCTCAAC-1", + "TGGCTGGAGACCTAGG", + "GGAAAGCCACAGATTC", + "CCAATCCTCTAAGCCA-1", + "ACGATGTGTTCACCTC", + "CGATGGCTCAAAGTAG", + "GTCTCGTAGCCAACAG", + "GGAAAGCTCATGCTCC", + "CTGAAACCAAGCGTAG-1", + "AGCATACCACGTTGGC", + "TGGCGCACATAGACTC-1", + "TCTCTAAGTTGGAGGT", + "CAGTCCTTCGTTGACA-1", + "CACCACTCATCCAACA", + "ACATGGTAGCAATATG", + "TAGAGCTAGATAGCAT", + "CAGCAGCCACTGTGTA", + "TCCCGATCACTCGACG", + "GCATACACAATCGGTT", + "CATTCGCCACGACTCG", + "GTTTCTAGTAGGCATG", + "TACTTGTCATCTACGA", + "GATCGATTCGCGGATC", + "AAAGATGAGATGTAAC", + "ACAGCTAAGAGACGAA", + "ACGATGTTCCCAAGAT-1", + "TGGCTGGGTGCAGGTA", + "GTGCGGTCACTGTCGG", + "GGGAGATAGACTTGAA", + "GTAGTCACAGGCAGTA", + "CAGTCCTCAAGTAATG", + "TTGCCGTCACAACTGT", + "GACGGCTGTCCGAGTC", + "CAGCTAAAGCTACCGC", + "TGACAACGTCATACTG", + "TGGCGCAAGAGTACAT", + "ACTTACTCACCCTATC-1", + "TGGCTGGGTGTTCGAT", + "AACTTTCCAAGGACTG", + "GGGCACTAGTAATCCC", + "TGCGGGTAGTAAGTAC", + "TCGTACCTCTGGTGTA", + "TCAGATGAGCTAAGAT", + "TTTGCGCCAGCAGTTT", + "GGACAAGGTGAAATCA", + "CGTGAGCTCCCTCAGT", + "TCAGCAATCGGACAAG", + "CACAGTACATCCGGGT", + "CCTAAAGCACAGGCCT", + "TCGAGGCGTCTCTTTA", + "TGGCGCACATTTGCTT", + "CACAGGCGTCGCATAT", + "GACGTGCGTAAGAGGA-1", + "TGTGTTTTCATCGGAT", + "ATGCGATGTGTCAATC", + "AGTGTCAAGATGAGAG", + "CTCCTAGAGGAGTACC", + "AGTCTTTTCTGCTGTC", + "AGAGCGATCAACACAC", + "CTGTTTATCTGTCTCG-1", + "CAGATCAGTCGCCATG", + "GATTCAGCAACACCCG", + "TAGTTGGTCTTAACCT-1", + "GACTACACACCTCGTT", + "CCCATACGTCATATGC", + "CGTCAGGGTGGCTCCA", + "GTATTCTTCAGGCAAG", + "TCCACACAGAACTGTA", + "CTAAGACAGGGTTTCT", + "GGCGACTCATGACGGA", + "TAAGCGTCATGGATGG", + "TGCACCTGTGCCTGTG", + "GTACTCCTCTGGTATG", + "CTCGTACCAGCTGTGC", + "CTGGTCTTCTCCAACC", + "GTATCTTCATATACCG", + "CTAACTTGTGCACTTA-1", + "CACCAGGAGTACGACG", + "TCTATTGTCTCCAACC", + "GAACGGAAGGATGCGT-1", + "AGGTCATCACTCGACG", + "GGATGTTGTTGTGGAG", + "CATCAGATCTGGTGTA", + "CAACTAGAGAATGTGT", + "GATCGATAGGCTACGA", + "GGTATTGGTTGTCGCG", + "GCTCTGTAGAGTCGGT", + "GCATACAGTCGCTTCT", + "TGAGCCGGTCGCTTCT", + "CCGTTCAGTGCGAAAC", + "GGGCACTCAGACACTT", + "TCTTCGGGTTATCGGT", + "TTCGGTCCACCAGTTA", + "CTAATGGCAGCTTCGG", + "GGCGTGTCATGGGACA", + "CCTCAGTAGAGGTTAT", + "CTAGAGTCAGCTCGCA", + "CATGACATCAGAGCTT", + "CCCAATCAGAGCTGCA", + "ACACCAAGTACCCAAT", + "TGGTTAGGTTGTTTGG", + "CTTAACTCAATAGAGT", + "TGTATTCGTTGTGGCC-1", + "ACGGGTCAGCTGCAAG", + "CCCTCCTTCGGATGTT", + "GTGGGTCCACCAGATT", + "ATTCTACTCAAGGCTT", + "CGGACGTAGTCATGCT", + "GATTCAGGTACAGTGG", + "CCTATTATCGCTTGTC", + "AAAGTAGTCCAACCAA", + "AGCATACTCGCCAGCA", + "CACCACTGTTTGGCGC", + "GAACATCCAGTAACGG", + "GCTGCGACATCACCCT", + "TCATTTGAGACTACAA", + "TCGGTAATCGTCGTTC-1", + "CAGTCCTGTAGGGTAC", + "CTACATTAGCGCTCCA", + "CTGTGCTGTGTGGTTT", + "GAGCAGAGTAAGGATT", + "TAAGCGTGTACGACCC", + "TATTACCTCTTAGCCC-1", + "TCAATCTAGTATTGGA", + "AATCGGTAGAGGTAGA", + "ACAGCTATCGCCCTTA", + "ATTACTCTCGAATGGG", + "CAAGTTGGTTCGTGAT", + "CACACTCAGGTCGGAT", + "CTGCTGTCATCAGTAC", + "GGGCACTGTTACAGAA", + "GTGTTAGAGAAAGTGG", + "TTATGCTTCAGGCAAG", + "TTCTCAAAGCTCCTTC", + "ATGTGTGCACGGTAGA-1", + "CACAGTATCTCTAGGA", + "CCATTCGTCTGTCAAG-1", + "CGTGAGCGTTGTGGAG", + "CTCTACGAGAGTACCG", + "GCAAACTCAATGGAGC" + ] +} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..4ce6f1067face033e6ff5a80c7cf182df5bae116 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,25 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "pivot-transcriptomics" +version = "0.2.0" +description = "Perturbation-informed endpoint maps for transcriptomic state control" +readme = "README.md" +requires-python = ">=3.11,<3.14" +license = {file = "LICENSE"} +dependencies = ["numpy>=1.26,<3", "scipy>=1.12,<2", "pandas>=2.1,<4", "scikit-learn>=1.5,<2", "torch>=2.2,<3", "anndata>=0.10,<0.14", "scanpy>=1.10,<1.13", "pyarrow>=15", "matplotlib>=3.8,<4", "requests>=2.31"] + +[project.optional-dependencies] +test = ["pytest>=8"] +hub = ["huggingface-hub>=0.27"] + +[project.scripts] +pivot = "pivot.cli:main" + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.pytest.ini_options] 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b/reproducibility/references.json new file mode 100644 index 0000000000000000000000000000000000000000..263a8efe93bd9738414ee894914bf3ee77928160 --- /dev/null +++ b/reproducibility/references.json @@ -0,0 +1,296 @@ +[ + { + "key": "adamson2016multiplexed", + "title": "A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response", + "url": "https://doi.org/10.1016/j.cell.2016.11.048", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "adduri2025predicting", + "title": "Predicting cellular responses to perturbation across diverse contexts with State", + "url": "https://doi.org/10.1101/2025.06.26.661135", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "ahlmann2025deep", + "title": "Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines", + "url": "https://doi.org/10.1038/s41592-025-02772-6", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "albergo2024stochastic", + "title": "Stochastic interpolants with data-dependent couplings", + "url": "https://arxiv.org/abs/2310.03725", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "boffi2025flowmap", + "title": "Flow map matching with stochastic interpolants: A mathematical framework for consistency models", + "url": "https://arxiv.org/abs/2406.07507", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "bunne2023cellot", + "title": "Learning single-cell perturbation responses using neural optimal transport", + "url": "https://doi.org/10.1038/s41592-023-01969-x", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "cellflow2025", + "title": "CellFlow enables generative single-cell phenotype modeling with flow matching", + "url": "https://doi.org/10.1101/2025.04.11.648220", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "chi2026departures", + "title": "Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges", + "url": "https://doi.org/10.1609/aaai.v40i25.39190", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "cui2024scgpt", + "title": "scGPT: toward building a foundation model for single-cell multi-omics using generative AI", + "url": "https://doi.org/10.1038/s41592-024-02201-0", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "datlinger2017pooled", + "title": "eC-CLEM: flexible multidimensional registration software for correlative microscopies", + "url": "https://doi.org/10.1038/nmeth.4170", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "dixit2016perturbseq", + "title": "Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens", + "url": "https://doi.org/10.1016/j.cell.2016.11.038", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "efron1979bootstrap", + "title": "Bootstrap Methods: Another Look at the Jackknife", + "url": "https://doi.org/10.1214/aos/1176344552", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "frans2025shortcut", + "title": "One Step Diffusion via Shortcut Models", + "url": "https://arxiv.org/abs/2410.12557", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "geng2026mean", + "title": "Mean Flows for One-step Generative Modeling", + "url": "https://arxiv.org/abs/2505.13447", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "gilbert2014genome", + "title": "Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation", + "url": "https://doi.org/10.1016/j.cell.2014.09.029", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "gonzalez2025combinatorial", + "title": "Combinatorial prediction of therapeutic perturbations using causally inspired neural networks", + "url": "https://doi.org/10.1038/s41551-025-01481-x", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "gretton2012kernel", + "title": "A Kernel Two-Sample Test", + "url": "https://jmlr.org/papers/v13/gretton12a.html", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "han2025identifying", + "title": "Identifying an optimal perturbation to induce a desired cell state by generative deep learning", + "url": "https://doi.org/10.1016/j.cels.2025.101405", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "hao2024large", + "title": "Large-scale foundation model on single-cell transcriptomics", + "url": "https://doi.org/10.1038/s41592-024-02305-7", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "jaitin2016dissecting", + "title": "Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq", + "url": "https://doi.org/10.1016/j.cell.2016.11.039", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "jolliffe2016pca", + "title": "Principal component analysis: a review and recent developments", + "url": "https://doi.org/10.1098/rsta.2015.0202", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "kamimoto2023celloracle", + "title": "Dissecting cell identity via network inference and in silico gene perturbation", + "url": "https://doi.org/10.1038/s41586-022-05688-9", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "kim2024consistency", + "title": "Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion", + "url": "https://arxiv.org/abs/2310.02279", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "lipman2023flowmatching", + "title": "Flow Matching for Generative Modeling", + "url": "https://arxiv.org/abs/2210.02747", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "lopez2018scvi", + "title": "Deep generative modeling for single-cell transcriptomics", + "url": "https://doi.org/10.1038/s41592-018-0229-2", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "loshchilov2019adamw", + "title": "Decoupled Weight Decay Regularization", + "url": "https://arxiv.org/abs/1711.05101", + "source": "Primary article metadata", + "verified": "2026-09-18" + }, + { + "key": "lotfollahi2019scgen", + "title": "scGen predicts single-cell perturbation responses", + "url": "https://doi.org/10.1038/s41592-019-0494-8", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "lotfollahi2023predicting", + "title": "Predicting cellular responses to complex perturbations in 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"https://doi.org/10.1038/s41592-023-02144-y", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "replogle2020combinatorial", + "title": "Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing", + "url": "https://doi.org/10.1038/s41587-020-0470-y", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "replogle2022mapping", + "title": "Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq", + "url": "https://doi.org/10.1016/j.cell.2022.05.013", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "roohani2024predicting", + "title": "Predicting transcriptional outcomes of novel multigene perturbations with GEARS", + "url": "https://doi.org/10.1038/s41587-023-01905-6", + "source": "DOI registration metadata", + "verified": "2026-09-18" + }, + { + "key": "song2021scorebased", + "title": "Score-Based Generative Modeling 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index 0000000000000000000000000000000000000000..c9494d41a71bcc2d79be5916750b174d332c50fb --- /dev/null +++ b/results/legacy/gears_norman.json @@ -0,0 +1,56 @@ +{ + "n_test_perts": 45, + "test_perts": [ + "ARID1A+ctrl", + "ARRDC3+ctrl", + "ATL1+ctrl", + "BAK1+ctrl", + "BCL2L11+BAK1", + "BCL2L11+TGFBR2", + "BCL2L11+ctrl", + "BPGM+ZBTB1", + "C3orf72+FOXL2", + "CBFA2T3+ctrl", + "CBL+TGFBR2", + "CBL+ctrl", + "CEBPB+OSR2", + "CEBPE+PTPN12", + "CKS1B+ctrl", + "CNNM4+ctrl", + "DUSP9+IGDCC3", + "DUSP9+ctrl", + "EGR1+ctrl", + "ETS2+CEBPE", + "FOXA1+FOXF1", + "FOXF1+HOXB9", + "FOXF1+ctrl", + "GLB1L2+ctrl", + "HOXC13+ctrl", + "IGDCC3+PRTG", + "IRF1+SET", + "KLF1+CEBPA", + "KLF1+FOXA1", + "KMT2A+ctrl", + "MAP2K3+IKZF3", + "MAP2K3+MAP2K6", + "MAPK1+ctrl", + "NCL+ctrl", + "PLK4+STIL", + "PTPN12+UBASH3A", + "RREB1+ctrl", + "SNAI1+DLX2", + "TBX3+TBX2", + "TGFBR2+ETS2", + "TGFBR2+ctrl", + "TSC22D1+ctrl", + "UBASH3B+PTPN12", + "ZBTB10+DLX2", + "ZNF318+ctrl" + ], + "metrics": { + "mse": 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0.014712702482938766, + "initial_candidate": "IGDCC3", + "projection_reward_change": -0.00530409999191761 + } + ] +} \ No newline at end of file diff --git a/results/validation/ridge.json b/results/validation/ridge.json new file mode 100644 index 0000000000000000000000000000000000000000..7bffaf48c396fa6820aaa91699c2ad3bbe2205b7 --- /dev/null +++ b/results/validation/ridge.json @@ -0,0 +1,403 @@ +{ + "protocol": "split-first-v1", + "method": "ridge", + "data_meta": { + "reference_fraction": 0.1, + "protocol": "split-first-v1", + "dataset": "norman", + "name": "norman", + "operation": "activation", + "control_label": "control", + "sep": "_", + "split": "perturbation", + "seed": 0, + "input_scale": "counts", + "raw_sha256": "21bd3c00b7f243d998aa9ca616490b45bf1b4ba9934d501190a867ddad982dae", + "raw_file": "norman_small.h5ad", + "n_cells": 920, + "n_control": 120, + "n_hvg": 201, + "n_pca": 10, + "pca_explained_var": 0.5626983046531677, + "mmd_gamma": 0.06974897265608301, + 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{ + "target": "PTPN12", + "search": "exhaustive", + "top1": 0.0, + "top5": 0.0, + "ndcg10": 0.0, + "rank": 12, + "selected": "CBL_TGFBR2", + "predicted_reward": 0.3994004428386688, + "measured_reward": 0.47342777252197266, + "measured_regret": 0.16722410917282104, + "selected_gene_overlap": 0.0, + "catalog_size": 20, + "candidate_cell_evaluations": 320, + "seconds": 0.0002608797494758619, + "top_score_ties": 1 + } + ], + "baseline_fit": { + "kind": "ridge", + "alpha": 1.0, + "seed": 0, + "train_cell_count": 504 + } +} \ No newline at end of file diff --git a/results/validation/training.json b/results/validation/training.json new file mode 100644 index 0000000000000000000000000000000000000000..1661ada9639f904cff372ba868804bd7b199d784 --- /dev/null +++ b/results/validation/training.json @@ -0,0 +1,53 @@ +{ + "protocol": "split-first-v1", + "config": { + "d_pert": 16, + "hidden": 64, + "depth": 2, + "epochs": 2, + "batch_size": 64, + "lr": 0.001, + "weight_decay": 1e-05, + "lam_tan": 1.0, + "lam_semi": 0.5, + "lam_reg": 0.0001, + "lam_dist": 0.0, + "n_dist_perts": 4, + "dist_n": 64, + "grad_clip": 5.0, + "match": "random", + "rep_mode": "gene_op", + "seed": 0, + "device": "cpu", + "threads": 4, + "endpoint_weight": 0.0 + }, + "history": [ + { + "epoch": 0, + "validation_mse": 0.15263483114540577, + "map": 3.9591146111488342, + "tan": 24.06910252571106, + "semi": 2.5752618526553306e-06, + "reg": 0.010355364764109254, + "total": 28.028218746185303 + }, + { + "epoch": 1, + "validation_mse": 0.14963708445429802, + "map": 3.667565017938614, + "tan": 23.406102418899536, + "semi": 1.3794184610560478e-05, + "reg": 0.01302928978111595, + "total": 27.073675632476807 + } + ], + "duration_s": 0.0614291250021779, + "n_train_cells": 504, + "n_parameters": 8602, + "software": { + "python": "3.12.14", + "torch": "2.14.0+cpu", + "numpy": "2.3.5" + } +} \ No newline at end of file diff --git a/scripts/download_data.py b/scripts/download_data.py new file mode 100644 index 0000000000000000000000000000000000000000..135e6d4ba6a5a204b78bd663c71aa26b43267ec9 --- /dev/null +++ b/scripts/download_data.py @@ -0,0 +1,58 @@ +"""Download a versioned scPerturb count matrix and verify its source checksum.""" + +from pathlib import Path +import argparse, hashlib, os +import requests + +FILES = { + "norman": ("NormanWeissman2019_filtered.h5ad", "c870e6967d91c017d9da827bab183cd6"), + "replogle_k562": ( + "ReplogleWeissman2022_K562_essential.h5ad", + "d8cba17576d1a8afc0f7d71b79cad0f7", + ), +} + + +def md5(path): + h = hashlib.md5() + with open(path, "rb") as f: + for block in iter(lambda: f.read(8 * 1024**2), b""): + h.update(block) + return h.hexdigest() + + +def download(dataset, output): + name, digest = FILES[dataset] + out = Path(output) + out.mkdir(parents=True, exist_ok=True) + dest = out / name + if dest.exists(): + if md5(dest) != digest: + raise ValueError(f"Checksum mismatch: {dest}") + return dest + part = dest.with_suffix(".h5ad.partial") + offset = part.stat().st_size if part.exists() else 0 + url = f"https://zenodo.org/records/10044268/files/{name}?download=1" + with requests.get( + url, + stream=True, + headers={"Range": f"bytes={offset}-"} if offset else {}, + timeout=(30, 120), + ) as response: + response.raise_for_status() + append = offset > 0 and response.status_code == 206 + with open(part, "ab" if append else "wb") as f: + for chunk in response.iter_content(8 * 1024**2): + f.write(chunk) + if md5(part) != digest: + raise ValueError(f"Source checksum mismatch: {part}") + os.replace(part, dest) + return dest + + +if __name__ == "__main__": + p = argparse.ArgumentParser() + p.add_argument("dataset", choices=FILES) + p.add_argument("--output", default="data/raw") + a = p.parse_args() + print(download(a.dataset, a.output)) diff --git a/scripts/figure1.py b/scripts/figure1.py new file mode 100644 index 0000000000000000000000000000000000000000..037c610288a593d25af4aa4499d4b50402591446 --- /dev/null +++ b/scripts/figure1.py @@ -0,0 +1,136 @@ +"""Draw the PIVOT overview with editable vector paths and embedded fonts.""" + +from pathlib import Path +import numpy as np +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib import font_manager as fm +from matplotlib.patches import Ellipse, FancyArrowPatch, PathPatch, FancyBboxPatch +from matplotlib.path import Path as MPath + +ROOT = Path(__file__).resolve().parents[1] +for f in (ROOT / "assets/fonts").glob("*.ttf"): + fm.fontManager.addfont(str(f)) +plt.rcParams.update( + { + "font.family": "Ubuntu", + "mathtext.fontset": "cm", + "pdf.fonttype": 42, + "ps.fonttype": 42, + "font.size": 13, + } +) +blue = "#62AEDD" +navy = "#245E84" +red = "#9D2944" +ink = "#243746" +pale = "#E9F4FB" +gray = "#9EB0BD" +fig, ax = plt.subplots(figsize=(13.6, 4.8)) +ax.set(xlim=(0, 13.6), ylim=(0, 4.8)) +ax.axis("off") + + +def text(x, y, s, size=13, color=ink, **kw): + ax.text( + x, + y, + s, + fontsize=size, + color=color, + ha=kw.pop("ha", "center"), + va="center", + **kw, + ) + + +def arrow(start, end, color=blue, rad=0, lw=2.2): + ax.add_patch( + FancyArrowPatch( + start, + end, + arrowstyle="-|>", + mutation_scale=16, + connectionstyle=f"arc3,rad={rad}", + color=color, + lw=lw, + ) + ) + + +def cloud(x, y, w, h, color, seed): + rng = np.random.default_rng(seed) + ax.add_patch(Ellipse((x, y), w, h, facecolor=color, alpha=0.10, edgecolor="none")) + z = rng.normal(size=(36, 2)) + z = z[np.linalg.norm(z, axis=1) < 2] + ax.scatter( + x + z[:, 0] * w / 5, + y + z[:, 1] * h / 5, + s=rng.uniform(9, 24, len(z)), + color=color, + alpha=0.7, + linewidth=0, + ) + + +cloud(1.45, 3.08, 2.1, 1.72, blue, 2) +text(1.45, 4.19, "Control population", 18, navy, weight="bold") +text(1.45, 2.02, r"$c_0\sim\rho_0$", 18) +# One broad map across the figure, with faint parallel sample trajectories. +for off in [-0.32, -0.12, 0.12, 0.32]: + verts = [(2.55, 3.1 + off), (4.3, 3.8 + off), (6.7, 3.9 + off), (8.75, 3.15 + off)] + ax.add_patch( + PathPatch( + MPath(verts, [MPath.MOVETO, MPath.CURVE4, MPath.CURVE4, MPath.CURVE4]), + fill=False, + edgecolor=blue, + alpha=0.20, + lw=10, + ) + ) +arrow((2.6, 3.1), (8.8, 3.14), blue, 0.20, 3) +text(5.6, 3.72, r"$X_\theta(0,1,c_0,e_u)$", 23) +text(5.6, 3.17, "Predict the response", 18, navy, weight="bold") +text(5.6, 4.38, r"$u=\{(g_j,o_j)\}_{j=1}^{M}\quad\longmapsto\quad e_u$", 17) +arrow((5.6, 4.12), (5.6, 3.92), gray, 0, 1.2) +cloud(9.4, 3.18, 1.75, 1.5, blue, 9) +text(9.4, 4.19, "Predicted cells", 18, navy, weight="bold") +cloud(11.97, 3.7, 1.65, 1.18, red, 3) +text(11.97, 4.56, "Target cells", 18, red, weight="bold") +text(12, 2.88, r"$c^\star\sim\rho^\star$", 18) +arrow((10.2, 3.23), (11.22, 3.63), red, -0.08, 1.6) +text(11, 2.57, "Endpoint reward", 17, red) +# Reward gradients return to the intervention coordinates. +arrow((11.3, 2.31), (5.22, 1.89), red, -0.17, 2.6) +text(7.95, 1.36, "Optimize intervention embeddings", 17, red) +text(3.05, 1.72, "Rank admissible interventions", 17, navy, weight="bold") +arrow((5.01, 2.03), (3.13, 2.25), navy, -0.10, 1.5) +text(3.45, 2.48, r"$e^{(L)}\;\longrightarrow\;u_{1:K}$", 18) +ax.add_patch( + FancyBboxPatch( + (0.18, 0.12), + 13.22, + 1.03, + boxstyle="round,pad=0.02,rounding_size=.08", + facecolor=pale, + edgecolor="none", + ) +) +text(0.51, 0.78, "PIVOT", 18, navy, ha="left", weight="bold") +text( + 7.8, + 0.74, + r"$\hat c_1=X_\theta(0,1,c_0,e)\qquad g_e=J_eX_\theta^{\mathsf{T}}\nabla_{\hat c_1}r\qquad e^+=e+\gamma\,\dfrac{g_e}{\|g_e\|_2+\epsilon}$", + 20, +) +text( + 7.6, + 0.29, + "Endpoint prediction, reward gradients, and admissible interventions.", + 16, +) +fig.subplots_adjust(left=0, right=1, bottom=0, top=1) +for ext in ["pdf", "png", "svg"]: + fig.savefig(ROOT / "assets" / f"figure1.{ext}", dpi=250, facecolor="white") diff --git a/scripts/plot_results.py b/scripts/plot_results.py new file mode 100644 index 0000000000000000000000000000000000000000..7b7e22c4adc972cca0b6bb1d39a981bba192e9aa --- /dev/null +++ b/scripts/plot_results.py @@ -0,0 +1,71 @@ +"""Plot measured evaluation summaries and collect comparable seed-level results.""" + +from pathlib import Path +import argparse, json, csv +import numpy as np +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib import font_manager + +ROOT = Path(__file__).resolve().parents[1] +for f in (ROOT / "assets/fonts").glob("*.ttf"): + font_manager.fontManager.addfont(str(f)) +plt.rcParams.update( + {"font.family": "Ubuntu", "mathtext.fontset": "cm", "pdf.fonttype": 42} +) + + +def plot(paths, output): + records = [json.loads(Path(p).read_text()) for p in paths] + rows = [] + for d in records: + if d.get("protocol") != "split-first-v1": + raise ValueError("Use corrected evaluation outputs") + for search, metrics in d["summary"]["inverse"].items(): + rows.append( + { + "method": d["method"], + "search": search, + "seed": d["seed"], + **{k: v["mean"] for k, v in metrics.items()}, + } + ) + if not rows: + raise ValueError("No inverse results for this catalog and target split") + out = Path(output) + out.mkdir(parents=True, exist_ok=True) + with open(out / "summary.csv", "w") as f: + writer = csv.DictWriter(f, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + fig, axes = plt.subplots(1, 2, figsize=(10, 4), layout="constrained") + labels = [ + r["method"] + "\n" + r["search"] + "\nseed " + str(r["seed"]) for r in rows + ] + for ax, k, title in zip( + axes, + ["top5", "measured_regret"], + [ + "Exact Top-5 recovery (higher is better)", + "Measured regret (lower is better)", + ], + ): + ax.bar(np.arange(len(rows)), [r[k] for r in rows], color="#62AEDD", width=0.6) + ax.set_xticks(np.arange(len(rows)), labels, fontsize=8) + ax.set_title(title, fontsize=11) + ax.spines[["top", "right"]].set_visible(False) + ax.grid(axis="y", alpha=0.2) + ax.set_axisbelow(True) + for ext in ["pdf", "png"]: + fig.savefig(out / f"evaluation.{ext}", dpi=220) + plt.close(fig) + + +if __name__ == "__main__": + p = argparse.ArgumentParser() + p.add_argument("results", nargs="+") + p.add_argument("--output", default="plots") + a = p.parse_args() + plot(a.results, a.output) diff --git a/scripts/quickstart.py b/scripts/quickstart.py new file mode 100644 index 0000000000000000000000000000000000000000..2f2e3a685a85325efd62716e75378c237edf29e5 --- /dev/null +++ b/scripts/quickstart.py @@ -0,0 +1,136 @@ +"""Run the documented example from measured counts through nomination and plots.""" + +from pathlib import Path +import argparse, subprocess, sys +import numpy as np +from pivot.data.perturb_data import PerturbData + +ROOT = Path(__file__).resolve().parents[1] + + +def run(args): + subprocess.run([sys.executable, "-m", "pivot.cli", *map(str, args)], check=True) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--output", default="runs/example") + a = parser.parse_args() + out = Path(a.output).resolve() + if out.exists(): + raise FileExistsError("Choose a new example output directory") + cache = out / "cache" + model = out / "model" + run( + [ + "prepare", + "--raw", + ROOT / "fixtures/norman_small.h5ad", + "--dataset", + "norman", + "--split", + "perturbation", + "--n-hvg", + 200, + "--n-pca", + 10, + "--output", + cache, + ] + ) + run( + [ + "train", + "--cache", + cache, + "--config", + ROOT / "configs/small.json", + "--output", + model, + ] + ) + run( + [ + "evaluate", + "--cache", + cache, + "--checkpoint", + model / "best.pt", + "--catalog", + "all", + "--n-cells", + 16, + "--guidance-steps", + 3, + "--output", + out / "pivot.json", + ] + ) + run( + [ + "evaluate", + "--cache", + cache, + "--baseline", + "ridge", + "--catalog", + "all", + "--n-cells", + 16, + "--output", + out / "ridge.json", + ] + ) + data = PerturbData(str(cache)) + label = data.labels("test")[0] + ids = np.intersect1d(data.indices("test", False), data.pert_to_idx[label]) + np.save(out / "target.npy", data.emb[ids]) + run( + [ + "predict", + "--cache", + cache, + "--checkpoint", + model / "best.pt", + "--label", + label, + "--n-cells", + 16, + "--output", + out / "prediction.npz", + ] + ) + for search in ["exhaustive", "guidance", "greedy"]: + run( + [ + "nominate", + "--cache", + cache, + "--checkpoint", + model / "best.pt", + "--target", + out / "target.npy", + "--catalog", + "all", + "--search", + search, + "--steps", + 3, + "--n-cells", + 16, + "--output", + out / (search + ".json"), + ] + ) + subprocess.run( + [ + sys.executable, + str(ROOT / "scripts/plot_results.py"), + str(out / "pivot.json"), + str(out / "ridge.json"), + "--output", + str(out / "plots"), + ], + check=True, + ) + print("Example complete:", out) diff --git a/scripts/run_matrix.py b/scripts/run_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..022f791344c1e29110c1bb29b541a122de1243b4 --- /dev/null +++ b/scripts/run_matrix.py @@ -0,0 +1,117 @@ +"""Execute corrected response, nomination, baseline, and loss-ablation comparisons.""" + +import argparse, subprocess, sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + + +def run(*args): + subprocess.run([sys.executable, "-m", "pivot.cli", *map(str, args)], check=True) + + +if __name__ == "__main__": + p = argparse.ArgumentParser() + p.add_argument("--raw", required=True) + p.add_argument("--dataset", choices=["norman", "replogle_k562"], required=True) + p.add_argument( + "--split", + choices=["cell", "perturbation", "combination", "gene"], + default="combination", + ) + p.add_argument("--output", required=True) + p.add_argument("--seeds", type=int, nargs="+", default=[0, 1, 2]) + p.add_argument("--device", default="cuda") + p.add_argument("--ablations", action="store_true") + p.add_argument("--input-scale", choices=["counts", "log1p"], default="counts") + a = p.parse_args() + variants = ["full", "distribution_2", "distribution_10"] + if a.ablations: + variants += [ + "map_only", + "map_tangent", + "map_semigroup", + "gene_only", + "random_pairing", + "nearest_pairing", + ] + catalog = "combination" if a.split == "combination" else "single" + for seed in a.seeds: + out = Path(a.output) / f"seed_{seed}" + cache = out / "cache" + if not (cache / "meta.json").exists(): + run( + "prepare", + "--raw", + a.raw, + "--dataset", + a.dataset, + "--split", + a.split, + "--seed", + seed, + "--output", + cache, + "--batch-col", + "batch" if a.dataset == "replogle_k562" else "gemgroup", + "--celltype-col", + "cell_line" if a.dataset == "replogle_k562" else "celltype", + "--input-scale", + a.input_scale, + ) + for variant in variants: + model = out / variant + run( + "train", + "--cache", + cache, + "--config", + ROOT / "configs" / f"{variant}.json", + "--device", + a.device, + "--seed", + seed, + "--output", + model, + ) + for initialization in ["random", "best"]: + run( + "evaluate", + "--cache", + cache, + "--checkpoint", + model / "best.pt", + "--catalog", + catalog, + "--initialization", + initialization, + "--device", + a.device, + "--seed", + seed, + "--output", + out / f"{variant}_{initialization}.json", + ) + for baseline in [ + "mean_control", + "average_effect", + "additive", + "ridge", + "endpoint_mlp", + "conditional_mlp", + ]: + run( + "evaluate", + "--cache", + cache, + "--baseline", + baseline, + "--catalog", + catalog, + "--device", + a.device, + "--seed", + seed, + "--output", + out / f"{baseline}.json", + ) diff --git a/src/pivot/__init__.py b/src/pivot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3ad49735b190889a1fdbe8e275f48155ba945cfd --- /dev/null +++ b/src/pivot/__init__.py @@ -0,0 +1,3 @@ +"""PIVOT transcriptomic endpoint maps and intervention nomination.""" + +__version__ = "0.2.0" diff --git a/src/pivot/cli.py b/src/pivot/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..a82f137a4077cbc60eab61f0ad9392c967965552 --- /dev/null +++ b/src/pivot/cli.py @@ -0,0 +1,256 @@ +"""Command-line data preparation, fitting, prediction, nomination and evaluation.""" + +from __future__ import annotations +import argparse, json +from pathlib import Path +import numpy as np +import torch +from pivot.data.preprocess import prepare +from pivot.data.perturb_data import PerturbData +from pivot.training.train import TrainConfig, train, load_checkpoint +from pivot.evaluation.inference import ( + encode_label, + forward_predict, + endpoint_ranking, + reward_guidance, + project_and_rerank, + greedy_combinatorial, +) +from pivot.evaluation.rewards import Reward +from pivot.evaluation.runner import evaluate, save_json + + +def main(): + parser = argparse.ArgumentParser( + prog="pivot", + description="Transcriptomic endpoint prediction and intervention nomination", + ) + sub = parser.add_subparsers(dest="command", required=True) + p = sub.add_parser("prepare") + p.add_argument("--raw", required=True) + p.add_argument("--output", required=True) + p.add_argument("--dataset", choices=["norman", "replogle_k562"], required=True) + p.add_argument( + "--split", + dest="regime", + choices=["cell", "perturbation", "combination", "gene"], + default="perturbation", + ) + p.add_argument("--seed", type=int, default=0) + p.add_argument("--input-scale", choices=["counts", "log1p"], default="counts") + for key, default in ( + ("n-hvg", 2000), + ("n-pca", 50), + ("min-cells", 20), + ("max-cells", None), + ("max-per-group", None), + ("max-groups", None), + ): + p.add_argument("--" + key, type=int, default=default) + p.add_argument("--batch-col", default="gemgroup") + p.add_argument("--pert-col", default="perturbation") + p.add_argument("--celltype-col", default="celltype") + p = sub.add_parser("train") + p.add_argument("--cache", required=True) + p.add_argument("--config", required=True) + p.add_argument("--output", required=True) + p.add_argument("--resume") + p.add_argument("--device") + p.add_argument("--seed", type=int) + p = sub.add_parser("evaluate") + p.add_argument("--cache", required=True) + p.add_argument("--checkpoint") + p.add_argument("--output", required=True) + p.add_argument( + "--baseline", + choices=[ + "mean_control", + "average_effect", + "additive", + "ridge", + "endpoint_mlp", + "conditional_mlp", + ], + ) + p.add_argument("--partition", choices=["val", "test"], default="test") + p.add_argument( + "--catalog", choices=["single", "combination", "all"], default="single" + ) + p.add_argument( + "--reward", + dest="reward_kind", + choices=["cosine", "centroid", "mmd", "wasserstein"], + default="cosine", + ) + p.add_argument("--n-cells", type=int, default=128) + p.add_argument("--seed", type=int, default=0) + p.add_argument("--device", default="cpu") + p.add_argument("--guidance-steps", type=int, default=25) + p.add_argument("--step-size", type=float, default=0.5) + p.add_argument("--k-nearest", type=int, default=10) + p.add_argument("--initialization", choices=["best", "random"], default="best") + p.add_argument("--max-targets", type=int) + p.add_argument("--baseline-epochs", type=int, default=60) + p.add_argument("--baseline-hidden", type=int, default=512) + p.add_argument("--ridge-alpha", type=float, default=1.0) + p = sub.add_parser("predict") + p.add_argument("--cache", required=True) + p.add_argument("--checkpoint", required=True) + p.add_argument("--label", required=True) + p.add_argument("--output", required=True) + p.add_argument("--n-cells", type=int, default=128) + p.add_argument("--device", default="cpu") + p = sub.add_parser("nominate") + p.add_argument("--cache", required=True) + p.add_argument("--checkpoint", required=True) + p.add_argument( + "--target", + required=True, + help="Numpy (n,d) population in this cache's PCA basis", + ) + p.add_argument("--output", required=True) + p.add_argument( + "--catalog", choices=["single", "combination", "all"], default="single" + ) + p.add_argument( + "--reward", + choices=["centroid", "cosine", "mmd", "wasserstein"], + default="centroid", + ) + p.add_argument( + "--search", choices=["exhaustive", "guidance", "greedy"], default="exhaustive" + ) + p.add_argument("--steps", type=int, default=25) + p.add_argument("--max-size", type=int, default=2) + p.add_argument("--device", default="cpu") + p.add_argument("--n-cells", type=int, default=128) + p.add_argument("--seed", type=int, default=0) + a = vars(parser.parse_args()) + command = a.pop("command") + if command == "prepare": + print(json.dumps(prepare(**a), indent=2)) + return + data = PerturbData(a.pop("cache")) + if command == "train": + cfg = json.loads(Path(a.pop("config")).read_text()) + for k in ("device", "seed"): + v = a.pop(k) + if v is not None: + cfg[k] = v + train(data, TrainConfig(**cfg), **a) + return + if command == "evaluate": + ck = a.pop("checkpoint") + baseline = a.pop("baseline") + if bool(ck) == bool(baseline): + parser.error("Specify one of --checkpoint or --baseline") + epochs = a.pop("baseline_epochs") + hidden = a.pop("baseline_hidden") + alpha = a.pop("ridge_alpha") + if baseline: + from pivot.evaluation.baselines import Baseline + + b = Baseline(data, baseline, alpha, epochs, hidden, a["seed"], a["device"]) + result = evaluate(data, b.predict, method=baseline, **a) + result["baseline_fit"] = b.training_info + save_json(a["output"], result) + else: + model, cfg = load_checkpoint(ck, data, a["device"]) + + def predict(c0, label): + return ( + forward_predict( + model, + torch.as_tensor(c0, device=a["device"]), + encode_label(model, data, label, a["device"]), + ) + .cpu() + .numpy() + ) + + result = evaluate(data, predict, model=model, **a) + print(json.dumps(result["summary"], indent=2)) + return + model, cfg = load_checkpoint(a["checkpoint"], data, a["device"]) + rng = np.random.default_rng(a.get("seed", 0)) + ids = data.indices("test", True) + ids = rng.choice(ids, min(a["n_cells"], len(ids)), replace=False) + c0 = torch.as_tensor(data.emb[ids], device=a["device"]) + if command == "predict": + pred = ( + forward_predict( + model, c0, encode_label(model, data, a["label"], a["device"]) + ) + .cpu() + .numpy() + ) + Path(a["output"]).parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + a["output"], + latent=pred, + expression_reconstruction=data.decode_to_genes(pred), + genes=np.asarray(data.genes), + control_cell_ids=data.obs.iloc[ids].cell_id.to_numpy(dtype=str), + ) + return + target = np.load(a["target"]) + if target.ndim != 2 or target.shape[1] != data.d or not np.isfinite(target).all(): + raise ValueError("Target needs finite (n,d) PCA coordinates") + reward = Reward( + a["reward"], + target_sample=target, + control_ref=c0.mean(0), + gamma=data.meta["mmd_gamma"], + device=a["device"], + ) + labels = ( + data.singles + if a["catalog"] == "single" + else data.combos if a["catalog"] == "combination" else data.perturbations + ) + if a["search"] == "greedy": + genes, score, history = greedy_combinatorial( + model, data, data.genes_vocab, c0, reward, a["max_size"], device=a["device"] + ) + out = { + "genes": genes, + "score": score, + "history": history, + "catalog_membership": data.sep.join(sorted(genes)) in labels, + } + else: + ranked = endpoint_ranking(model, data, labels, c0, reward, device=a["device"]) + if a["search"] == "guidance": + es = reward_guidance( + model, + c0, + reward, + encode_label(model, data, ranked[0][0], a["device"]), + a["steps"], + ) + ranked = project_and_rerank( + model, data, labels, es, c0, reward, device=a["device"] + ) + out = { + "ranked": [ + { + "label": q, + "genes": data.parse(q), + "operation": data.operation, + "predicted_reward": v, + } + for q, v in ranked + ] + } + out.update( + search=a["search"], + reward=a["reward"], + target_file=Path(a["target"]).name, + control_cell_ids=data.obs.iloc[ids].cell_id.tolist(), + data_meta=data.meta, + ) + save_json(a["output"], out) + + +if __name__ == "__main__": + main() diff --git a/src/pivot/data/__init__.py b/src/pivot/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/pivot/data/perturb_data.py b/src/pivot/data/perturb_data.py new file mode 100644 index 0000000000000000000000000000000000000000..c45492eb288ba01361c63a7aed0a3f534a0080ad --- /dev/null +++ b/src/pivot/data/perturb_data.py @@ -0,0 +1,119 @@ +"""Expression cache with explicit partitions and a metadata-only gene vocabulary.""" + +from __future__ import annotations +import json +from pathlib import Path +import numpy as np +import pandas as pd +import scipy.sparse as sp +from .preprocess import parse + + +class PerturbData: + """Load a prepared cache. Arrays retain the row order of obs.parquet.""" + + def __init__(self, cache_dir: str, embedding="pca"): + self.dir = str(cache_dir) + p = Path(cache_dir) + self.meta = json.loads((p / "meta.json").read_text()) + if self.meta.get("protocol") != "split-first-v1": + raise ValueError( + "Use a split-first-v1 cache. Historical checkpoints require the archived code." + ) + if embedding != "pca": + raise ValueError( + "This release implements the manuscript PCA representation" + ) + self.obs = pd.read_parquet(p / "obs.parquet") + self.genes = (p / "genes_hvg.txt").read_text().splitlines() + self.Xhvg = sp.load_npz(p / "Xhvg.npz").tocsr() + self.emb = np.load(p / "pca_emb.npy") + self.d = self.emb.shape[1] + self.pca_components = np.load(p / "pca_components.npy") + self.pca_mean = np.load(p / "pca_mean.npy") + if len(self.obs) != len(self.emb) or self.Xhvg.shape != ( + len(self.obs), + len(self.genes), + ): + raise ValueError("Cache row or feature dimensions disagree") + self.sep = self.meta["sep"] + self.control_label = self.meta["control_label"] + self.operation = self.meta["operation"] + self.is_control = self.obs.is_control.to_numpy() + self.control_idx = np.flatnonzero(self.is_control) + self.batch = self.obs.batch.to_numpy() + self.celltype = self.obs.celltype.to_numpy() + self.pert_to_idx = { + p: s.index.to_numpy() + for p, s in self.obs.groupby("perturbation") + if p != self.control_label + } + self.perturbations = sorted(self.pert_to_idx) + self.genes_vocab = sorted( + {g for p in self.perturbations for g in self.parse(p)} + ) + self.gene_to_id = {g: i for i, g in enumerate(self.genes_vocab)} + self.op_vocab = ["none", self.operation] + self.op_to_id = {o: i for i, o in enumerate(self.op_vocab)} + self.singles = [p for p in self.perturbations if len(self.parse(p)) == 1] + self.combos = [p for p in self.perturbations if len(self.parse(p)) == 2] + + def parse(self, label: str) -> list[str]: + return parse(label, self.control_label, self.sep) + + def indices(self, partition: str, controls: bool | None = None) -> np.ndarray: + mask = self.obs.split.eq(partition).to_numpy(copy=True) + if controls is not None: + mask &= self.is_control if controls else ~self.is_control + return np.flatnonzero(mask) + + def labels(self, partition: str) -> list[str]: + return sorted(set(self.obs.iloc[self.indices(partition, False)].perturbation)) + + def decode_to_genes(self, emb: np.ndarray) -> np.ndarray: + """Rank-d affine reconstruction, shape (..., d) to (..., n_hvg).""" + return emb @ self.pca_components + self.pca_mean + + def pert_gene_op_ids(self, label): + genes = self.parse(label) + unknown = set(genes) - set(self.gene_to_id) + if unknown: + raise ValueError(f"Genes outside checkpoint vocabulary: {sorted(unknown)}") + ids = np.array([self.gene_to_id[g] for g in genes], dtype=np.int64) + return ids, np.full(len(ids), 1, dtype=np.int64) + + def sample_controls(self, target_idx, strategy, rng, control_pool): + """Sample only supplied controls, optionally within batch/cell type. + + Missing matched controls raise an error so cross-context substitutions + cannot change the conditioning distribution without an explicit choice. + """ + pool = np.asarray(control_pool, dtype=int) + if not len(pool) or not self.is_control[pool].all(): + raise ValueError("Invalid control pool") + if strategy == "random": + return rng.choice(pool, len(target_idx), replace=True) + if strategy == "nearest": + from sklearn.neighbors import NearestNeighbors + + j = ( + NearestNeighbors(n_neighbors=1) + .fit(self.emb[pool]) + .kneighbors(self.emb[target_idx], return_distance=False) + ) + return pool[j.ravel()] + if strategy not in ("batch", "celltype", "batch_celltype"): + raise ValueError(strategy) + out = [] + for i in target_idx: + m = np.ones(len(pool), bool) + if strategy in ("batch", "batch_celltype"): + m &= self.batch[pool] == self.batch[i] + if strategy in ("celltype", "batch_celltype"): + m &= self.celltype[pool] == self.celltype[i] + if not m.any(): + raise ValueError( + f"No matching control for cell {self.obs.iloc[i].cell_id}" + ) + out.append(rng.choice(pool[m])) + return np.asarray(out, dtype=np.int64) diff --git a/src/pivot/data/preprocess.py b/src/pivot/data/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..207f6e6501be425537462e3eb831ead464fb130a --- /dev/null +++ b/src/pivot/data/preprocess.py @@ -0,0 +1,275 @@ +"""Prepare one split before fitting expression features and the PCA basis. + +The cache preserves cell IDs, feature order, split assignments, input checksums, +and the fitted transformation. All operations that estimate parameters use +training cells. Library-size normalization acts independently on each cell. +""" + +from __future__ import annotations +import hashlib, json +from pathlib import Path +from importlib.metadata import version +import numpy as np +import pandas as pd +import scipy.sparse as sp +from sklearn.decomposition import PCA + +DATASETS = { + "norman": {"file": "NormanWeissman2019_filtered.h5ad", "operation": "activation"}, + "replogle_k562": { + "file": "ReplogleWeissman2022_K562_essential.h5ad", + "operation": "interference", + }, +} + + +def checksum(path: str | Path) -> str: + """SHA-256 of file bytes, read in bounded blocks.""" + h = hashlib.sha256() + with open(path, "rb") as f: + for b in iter(lambda: f.read(8 * 1024**2), b""): + h.update(b) + return h.hexdigest() + + +def parse(label: str, control: str = "control", sep: str = "_") -> list[str]: + """Return a sorted gene set, excluding the explicit control token.""" + genes = [g for g in str(label).split(sep) if g and g != control] + if len(genes) != len(set(genes)): + raise ValueError(f"Duplicate gene in perturbation {label!r}") + return sorted(genes) + + +def assign_splits( + obs: pd.DataFrame, regime: str, seed: int, control="control", sep="_" +) -> np.ndarray: + """Assign train/validation/test using cell, label, gene, or combination units. + + Target labels are split 70/10/20 for perturbation and combination regimes. + Each cell group and the controls are split 70/15/15 in the cell regime. + Gene holdouts exclude every combination containing a held-out gene. + """ + rng = np.random.default_rng(seed) + labels = sorted(set(obs.perturbation) - {control}) + split = np.full(len(obs), "train", dtype="U5") + ctrl = np.flatnonzero(obs.is_control.to_numpy()) + if len(ctrl) < 10: + raise ValueError("At least 10 control cells are required") + + def divide(ids): + ids = rng.permutation(ids) + n = len(ids) + if n < 5: + raise ValueError("At least five observations per split unit are required") + a, b = int(0.7 * n), int(0.85 * n) + split[ids[a:b]] = "val" + split[ids[b:]] = "test" + + divide(ctrl) + if regime == "cell": + for label in labels: + divide(np.flatnonzero(obs.perturbation.to_numpy() == label)) + else: + units = labels + if regime == "combination": + units = [p for p in labels if len(parse(p, control, sep)) == 2] + elif regime == "gene": + units = sorted({g for p in labels for g in parse(p, control, sep)}) + elif regime != "perturbation": + raise ValueError(f"Unsupported split {regime}") + if len(units) < 5: + raise ValueError(f"{regime} needs at least five independent units") + perm = rng.permutation(units) + nt = max(1, int(0.2 * len(units))) + nv = max(1, int(0.1 * len(units))) + test, val = set(perm[:nt]), set(perm[nt : nt + nv]) + for p in labels: + genes = set(parse(p, control, sep)) + s = ( + "test" + if (genes & test if regime == "gene" else p in test) + else ( + "val" + if (genes & val if regime == "gene" else p in val) + else "train" + ) + ) + split[obs.perturbation.to_numpy() == p] = s + if not all( + np.any((split == s) & ~obs.is_control.to_numpy()) + for s in ["train", "val", "test"] + ): + raise ValueError("Every partition requires perturbed cells") + return split + + +def prepare( + raw: str, + output: str, + dataset: str, + regime="perturbation", + seed=0, + input_scale="counts", + n_hvg=2000, + n_pca=50, + min_cells=20, + max_cells=None, + max_per_group=None, + max_groups=None, + pert_col="perturbation", + batch_col="gemgroup", + celltype_col="celltype", + control="control", + sep="_", +) -> dict: + """Read h5ad, split cells, fit training HVGs/PCA, and save a self-contained cache. + + `input_scale` is explicit: counts receive CP10k and log1p; log1p values are + used directly. The optional group cap selects labels without examining + expression. Backed input avoids loading a whole atlas for a small run. + """ + import anndata as ad + import scanpy as sc + + out = Path(output) + if (out / "meta.json").exists(): + raise FileExistsError( + f"Cache already exists: {out}; choose a new output directory" + ) + a = ad.read_h5ad(raw, backed="r") + if pert_col not in a.obs: + raise KeyError(f"Missing {pert_col}; available columns: {list(a.obs.columns)}") + labels = np.asarray( + [ + sep.join(parse(p, control, sep)) or control + for p in a.obs[pert_col].astype(str) + ] + ) + rng = np.random.default_rng(seed) + keep = np.arange(a.n_obs) + if max_groups: + groups = sorted(set(labels) - {control}) + selected = rng.choice(groups, min(max_groups, len(groups)), replace=False) + keep = keep[np.isin(labels, list(selected) + [control])] + if max_cells and len(keep) > max_cells: + keep = np.sort(rng.choice(keep, max_cells, replace=False)) + if max_per_group: + keep = np.sort( + np.concatenate( + [ + rng.choice(ids, min(max_per_group, len(ids)), replace=False) + for p in sorted(set(labels[keep])) + if len(ids := keep[labels[keep] == p]) + ] + ) + ) + vc = pd.Series(labels[keep]).value_counts() + permitted = set(vc[vc >= min_cells].index) | {control} + keep = keep[np.isin(labels[keep], list(permitted))] + b = a[keep].to_memory() + a.file.close() + if not b.obs_names.is_unique or not b.var_names.is_unique: + raise ValueError("Cell and feature identifiers must be unique") + obs = pd.DataFrame( + { + "cell_id": b.obs_names.astype(str), + "perturbation": labels[keep], + "batch": ( + b.obs[batch_col].astype(str).to_numpy() if batch_col in b.obs else "0" + ), + "celltype": ( + b.obs[celltype_col].astype(str).to_numpy() + if celltype_col in b.obs + else dataset + ), + } + ) + obs["is_control"] = obs.perturbation.eq(control) + obs["split"] = assign_splits(obs, regime, seed, control, sep) + # Reserve outcome cells for every candidate before fitting any features. + # These cells allow evaluation of a nominated action with measured responses + # independent of the target query and every model-training outcome. + for label in sorted(set(obs.perturbation)): + ids = obs.index[obs.perturbation.eq(label)].to_numpy() + n_ref = max(2, int(0.1 * len(ids))) + # Preserve all three control partitions when selecting reference cells. + if label == control: + ids = ids[obs.loc[ids, "split"].eq("train").to_numpy()] + ref = rng.choice(ids, min(n_ref, max(0, len(ids) - 3)), replace=False) + obs.loc[ref, "split"] = "reference" + train = obs.split.eq("train").to_numpy() + X = sp.csr_matrix(b.X, dtype=np.float32) + if not np.isfinite(X.data).all() or (X.data < 0).any(): + raise ValueError("Expression must be finite and nonnegative") + if input_scale == "counts": + if not np.allclose(X.data, np.round(X.data), atol=1e-5): + raise ValueError( + "Counts mode requires integer counts; specify log1p for normalized data" + ) + totals = np.asarray(X.sum(axis=1)).ravel() + if np.any(totals <= 0): + raise ValueError("Cells with zero total counts are unsupported") + X = sp.diags(1e4 / totals) @ X + X = X.tocsr() + X.data = np.log1p(X.data) + elif input_scale != "log1p": + raise ValueError("input_scale must be counts or log1p") + # Feature statistics are estimated without validation or test expression. + ta = ad.AnnData(X[train].copy(), var=b.var.copy()) + if n_hvg < X.shape[1]: + sc.pp.highly_variable_genes(ta, n_top_genes=n_hvg, flavor="seurat") + hv = np.flatnonzero(ta.var.highly_variable.to_numpy()) + else: + hv = np.arange(X.shape[1]) + X = X[:, hv].tocsr().astype(np.float32) + d = min(n_pca, X.shape[1] - 1, int(train.sum()) - 1) + if d < 1: + raise ValueError("Insufficient dimensions for PCA") + # ARPACK centers sparse input internally and avoids a dense full-atlas copy. + pca = PCA(n_components=d, svd_solver="arpack", random_state=seed).fit(X[train]) + emb = pca.transform(X).astype(np.float32) + out.mkdir(parents=True, exist_ok=True) + sp.save_npz(out / "Xhvg.npz", X) + np.save(out / "pca_emb.npy", emb) + np.save(out / "pca_components.npy", pca.components_.astype(np.float32)) + np.save(out / "pca_mean.npy", pca.mean_.astype(np.float32)) + obs.to_parquet(out / "obs.parquet", index=False) + (out / "genes_hvg.txt").write_text("\n".join(b.var_names[hv].astype(str))) + # A fixed evaluation bandwidth supports comparisons across predicted populations. + tr_emb = emb[train] + samp = tr_emb[rng.choice(len(tr_emb), min(512, len(tr_emb)), replace=False)] + from scipy.spatial.distance import pdist + + bandwidth = float(np.median(pdist(samp) ** 2)) + gamma = 1 / max(bandwidth, 1e-8) + meta = { + "reference_fraction": 0.1, + "protocol": "split-first-v1", + "dataset": dataset, + "name": dataset, + "operation": DATASETS.get(dataset, {}).get("operation", "interference"), + "control_label": control, + "sep": sep, + "split": regime, + "seed": seed, + "input_scale": input_scale, + "raw_sha256": checksum(raw), + "raw_file": Path(raw).name, + "n_cells": len(obs), + "n_control": int(obs.is_control.sum()), + "n_hvg": len(hv), + "n_pca": d, + "pca_explained_var": float(pca.explained_variance_ratio_.sum()), + "mmd_gamma": gamma, + "fit_cell_ids_sha256": hashlib.sha256( + "\n".join(obs.loc[train, "cell_id"]).encode() + ).hexdigest(), + "selection": { + "max_cells": max_cells, + "max_per_group": max_per_group, + "max_groups": max_groups, + }, + "software": {"scanpy": version("scanpy"), "anndata": version("anndata")}, + } + (out / "meta.json").write_text(json.dumps(meta, indent=2)) + return meta diff --git a/src/pivot/evaluation/__init__.py b/src/pivot/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/pivot/evaluation/baselines.py b/src/pivot/evaluation/baselines.py new file mode 100644 index 0000000000000000000000000000000000000000..671a6ae0ece40b6c7497f6c52c4f0ba8b6731114 --- /dev/null +++ b/src/pivot/evaluation/baselines.py @@ -0,0 +1,136 @@ +"""Reference predictors fitted with the same training cells and feature basis.""" + +from __future__ import annotations +import copy +import numpy as np +import torch +from sklearn.linear_model import Ridge +from pivot.training.train import make_model, TrainConfig, validation_loss +from pivot.models.encoders import build_pert_tensors +from pivot.utils.common import set_seed + + +class Baseline: + """Population predictor. `predict(c0, label)` returns (n_controls, d).""" + + def __init__( + self, data, kind="ridge", alpha=1.0, epochs=60, hidden=512, seed=0, device="cpu" + ): + if kind not in ( + "mean_control", + "average_effect", + "additive", + "ridge", + "endpoint_mlp", + "conditional_mlp", + ): + raise ValueError(kind) + self.data = data + self.kind = kind + self.device = device + tr = data.indices("train", False) + ctrl = data.indices("train", True) + self.control = data.emb[ctrl].mean(0) + self.effects = { + p: data.emb[np.intersect1d(tr, data.pert_to_idx[p])].mean(0) - self.control + for p in data.labels("train") + } + self.avg = np.mean(list(self.effects.values()), axis=0) + self.training_info = { + "kind": kind, + "alpha": alpha, + "seed": seed, + "train_cell_count": len(tr), + } + if kind == "ridge": + labels = list(self.effects) + self.reg = Ridge(alpha=alpha, fit_intercept=False).fit( + self.features(labels), np.stack(list(self.effects.values())) + ) + if kind in ("endpoint_mlp", "conditional_mlp"): + set_seed(seed) + torch.set_num_threads(4) + cfg = TrainConfig(hidden=hidden, depth=4, seed=seed, device=device) + self.model = make_model(data, cfg) + opt = torch.optim.AdamW(self.model.parameters(), lr=1e-3, weight_decay=1e-5) + rng = np.random.default_rng(seed) + best = float("inf") + beststate = None + history = [] + for epoch in range(epochs): + self.model.train() + for ids in np.array_split( + rng.permutation(tr), max(1, int(np.ceil(len(tr) / 1024))) + ): + c = data.emb[rng.choice(ctrl, len(ids))] + if kind == "endpoint_mlp": + c = np.broadcast_to(self.control, c.shape).copy() + c = torch.as_tensor(c, device=device) + y = torch.as_tensor(data.emb[ids], device=device) + labels = data.obs.iloc[ids].perturbation.tolist() + g, o, m, p = build_pert_tensors(data, labels, device) + pred = self.model.endpoint_from_pert(c, g, o, m, p) + loss = (pred - y).square().sum(-1).mean() + opt.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_(self.model.parameters(), 5.0) + opt.step() + self.model.eval() + vals = [] + vc = data.emb[data.indices("val", True)][:128] + for p in data.labels("val"): + ids = np.intersect1d( + data.indices("val", False), data.pert_to_idx[p] + ) + vals.append( + np.mean( + (self.predict(vc, p).mean(0) - data.emb[ids].mean(0)) ** 2 + ) + ) + score = float(np.mean(vals)) + history.append(score) + if score < best: + best = score + beststate = copy.deepcopy(self.model.state_dict()) + self.model.load_state_dict(beststate) + self.training_info.update( + validation_mse=history, + best_validation_mse=best, + epochs=epochs, + hidden=hidden, + ) + + def features(self, labels): + a = np.zeros((len(labels), len(self.data.genes_vocab)), np.float32) + for i, p in enumerate(labels): + for g in self.data.parse(p): + a[i, self.data.gene_to_id[g]] = 1.0 + return a + + def predict(self, c0, label): + if self.kind in ("endpoint_mlp", "conditional_mlp"): + c = np.asarray(c0, dtype=np.float32) + if self.kind == "endpoint_mlp": + c = np.broadcast_to(self.control, c.shape).copy() + g, o, m, p = build_pert_tensors(self.data, [label], self.device) + with torch.no_grad(): + return ( + self.model.endpoint_from_pert( + torch.as_tensor(c, device=self.device), g, o, m, p + ) + .cpu() + .numpy() + ) + if self.kind == "mean_control": + delta = np.zeros(self.data.d) + elif self.kind == "average_effect": + delta = self.avg + elif self.kind == "additive": + # Unsupported single genes receive the training-average effect. + delta = sum( + (self.effects.get(g, self.avg) for g in self.data.parse(label)), + start=np.zeros(self.data.d), + ) + else: + delta = self.reg.predict(self.features([label]))[0] + return np.asarray(c0) + delta diff --git a/src/pivot/evaluation/inference.py b/src/pivot/evaluation/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..54a8a198fdb0db1fdad4220cc14d5b552ea9e89c --- /dev/null +++ b/src/pivot/evaluation/inference.py @@ -0,0 +1,157 @@ +"""inference procedures, algorithms 2-6. + +Alg 2 forward_predict predict endpoint, aggregate population +Alg 3 endpoint_ranking score = reward of predicted endpoint, return topk +Alg 4 reward_guidance jacobian pull-back ascent in embedding space +Alg 5 project_and_rerank project e* to admissible candidates, rerank k-nn +Alg 6 greedy_combinatorial sequentially add the best gene (mean-pool embed) +""" + +from __future__ import annotations + +import numpy as np +import torch + +from pivot.models.encoders import build_pert_tensors +from pivot.models.pivot import PIVOT, candidate_embeddings + + +def encode_label(model: PIVOT, data, label: str, device) -> torch.Tensor: + g, o, mask, pid = build_pert_tensors(data, [label], device=device) + return model.encode(g, o, mask, pid) # (1, m) + + +def encode_gene_set(model: PIVOT, data, genes: list[str], device) -> torch.Tensor: + """encode an arbitrary gene set via the dataset operation (mean-pool).""" + label = data.sep.join(genes) if genes else data.control_label + return encode_label(model, data, label, device) + + +@torch.no_grad() +def forward_predict(model: PIVOT, c0: torch.Tensor, e_u: torch.Tensor) -> torch.Tensor: + """Alg 2. c0: (N,d) control embeddings; e_u: (1,m) or (N,m). returns endpoint (N,d).""" + if e_u.shape[0] == 1 and c0.shape[0] > 1: + e_u = e_u.expand(c0.shape[0], -1) + return model.endpoint_from_e(c0, e_u) + + +@torch.no_grad() +def endpoint_ranking( + model: PIVOT, + data, + candidate_labels, + c0: torch.Tensor, + reward, + topk: int | None = None, + device="cpu", + chunk: int = 4, +): + """Alg 3. score each candidate by reward of its predicted endpoint population. + + batched: all candidates x control cells go through the flow map in one + (chunked) forward pass, then per-row rewards are averaged per candidate. works + for any per-row reward (centroid/cosine). returns list of (label, score) desc. + """ + E = torch.as_tensor( + candidate_embeddings(model, data, candidate_labels, device), device=device + ) + C, N = E.shape[0], c0.shape[0] + scores = np.empty(C, dtype=np.float64) + # chunk over candidates to bound memory (c*n rows per chunk) + cands_per_chunk = max(1, chunk * 1024 // max(N, 1)) + for s in range(0, C, cands_per_chunk): + e = E[s : s + cands_per_chunk] + c = e.shape[0] + c0_rep = c0.unsqueeze(0).expand(c, N, -1).reshape(c * N, -1) + e_rep = e.unsqueeze(1).expand(c, N, -1).reshape(c * N, -1) + chat = model.endpoint_from_e(c0_rep, e_rep) + if reward.kind in ("mmd", "wasserstein"): + pops = chat.view(c, N, -1) + r = torch.stack([reward(pop).mean() for pop in pops]) + else: + r = reward(chat).view(c, N).mean(1) + scores[s : s + c] = r.detach().cpu().numpy() + order = np.argsort(-scores) + ranked = [(candidate_labels[i], float(scores[i])) for i in order] + return ranked if topk is None else ranked[:topk] + + +def reward_guidance( + model: PIVOT, + c0: torch.Tensor, + reward, + e_init: torch.Tensor, + steps: int = 25, + step_size: float = 0.5, + eps: float = 1e-8, + normalize: bool = True, +) -> torch.Tensor: + """Alg 4. gradient ascent on reward in embedding space via the flow-map jacobian. + + e_{l+1} = e_l + gamma * g_e/(norm(g_e)+eps), g_e from autograd.""" + e = e_init.detach().clone() + for _ in range(steps): + e.requires_grad_(True) + chat = model.endpoint_from_e(c0, e.expand(c0.shape[0], -1)) + r = reward(chat).mean() + (g_e,) = torch.autograd.grad(r, e) + with torch.no_grad(): + step = g_e / (g_e.norm() + eps) if normalize else g_e + e = e + step_size * step + e = e.detach() + return e + + +@torch.no_grad() +def project_and_rerank( + model: PIVOT, + data, + candidate_labels, + e_star: torch.Tensor, + c0: torch.Tensor, + reward, + k_nearest: int = 10, + topk: int = 5, + device="cpu", +): + """Alg 5. project e* to the k nearest admissible candidates, rerank by endpoint reward.""" + E = torch.as_tensor( + candidate_embeddings(model, data, candidate_labels, device), device=device + ) + d = torch.cdist(e_star.view(1, -1), E).squeeze(0) + knn = torch.argsort(d)[:k_nearest].cpu().numpy() + sub = [candidate_labels[i] for i in knn] + ranked = endpoint_ranking(model, data, sub, c0, reward, device=device) + return ranked[:topk] + + +@torch.no_grad() +def greedy_combinatorial( + model: PIVOT, + data, + gene_pool, + c0: torch.Tensor, + reward, + max_size: int = 2, + min_gain: float = 1e-4, + device="cpu", +): + """Alg 6. greedily add the gene that most improves the reward of the mean-pooled endpoint.""" + chosen: list[str] = [] + best_score = -np.inf + history = [] + while len(chosen) < max_size: + remaining = [g for g in gene_pool if g not in chosen] + if not remaining: + break + # score all candidate gene-sets {chosen + g} in one batched pass + labels = [data.sep.join(chosen + [g]) for g in remaining] + ranked = endpoint_ranking(model, data, labels, c0, reward, device=device) + best_label, best_local = ranked[0] + best_gene = remaining[labels.index(best_label)] + if best_local - best_score < min_gain: + break + chosen.append(best_gene) + best_score = best_local + history.append((list(chosen), best_score)) + return chosen, best_score, history diff --git a/src/pivot/evaluation/metrics.py b/src/pivot/evaluation/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..678fd8798bb1f827df1179a457406ed486b8dc56 --- /dev/null +++ b/src/pivot/evaluation/metrics.py @@ -0,0 +1,66 @@ +"""Population and retrieval metrics with explicit feature and target units.""" + +from __future__ import annotations +import numpy as np +from scipy.spatial.distance import cdist +from scipy.stats import pearsonr, wasserstein_distance + + +def correlation(pred, observed) -> float: + """Pearson correlation across features; undefined constant vectors return NaN.""" + p = np.asarray(pred).ravel() + y = np.asarray(observed).ravel() + if p.std() < 1e-12 or y.std() < 1e-12: + return float("nan") + return float(pearsonr(p, y).statistic) + + +def mmd2(x, y, gamma: float) -> float: + """Biased RBF MMD squared. gamma must be fixed across compared methods.""" + if gamma <= 0: + raise ValueError("gamma must be positive") + k = lambda a, b: np.exp(-gamma * cdist(a, b, metric="sqeuclidean")) + return float(k(x, x).mean() + k(y, y).mean() - 2 * k(x, y).mean()) + + +def sliced_wasserstein(x, y, n_proj=50, seed=0) -> float: + """Mean exact 1-D Wasserstein-1 distance over reproducible unit directions.""" + rng = np.random.default_rng(seed) + w = rng.normal(size=(n_proj, x.shape[1])) + w /= np.linalg.norm(w, axis=1, keepdims=True) + return float(np.mean([wasserstein_distance(x @ v, y @ v) for v in w])) + + +def energy_distance(x, y) -> float: + """Empirical energy statistic in PCA coordinates, including diagonal terms.""" + return float(2 * cdist(x, y).mean() - cdist(x, x).mean() - cdist(y, y).mean()) + + +def retrieval_metrics(ranked, truth, k=5) -> dict: + """Exact recovery and one-relevant-item nDCG; censored ranks stay undefined.""" + rank = list(ranked).index(truth) + 1 if truth in ranked else None + return { + "top1": float(rank == 1), + "top5": float(rank is not None and rank <= k), + "ndcg10": ( + float(1 / np.log2(rank + 1)) if rank is not None and rank <= 10 else 0.0 + ), + "rank": rank, + } + + +def bootstrap(values, seed=0, n_boot=2000) -> dict: + """Percentile interval over independent conditions, never over cells.""" + x = np.asarray(values, float) + x = x[np.isfinite(x)] + if not len(x): + return {"mean": None, "lower": None, "upper": None, "n": 0} + rng = np.random.default_rng(seed) + means = x[rng.integers(len(x), size=(n_boot, len(x)))].mean(1) + lo, hi = np.quantile(means, [0.025, 0.975]) + return { + "mean": float(x.mean()), + "lower": float(lo), + "upper": float(hi), + "n": len(x), + } diff --git a/src/pivot/evaluation/rewards.py b/src/pivot/evaluation/rewards.py new file mode 100644 index 0000000000000000000000000000000000000000..e4c759103cbb383bd315e7e8cc411c79ffe3cd89 --- /dev/null +++ b/src/pivot/evaluation/rewards.py @@ -0,0 +1,78 @@ +"""Differentiable rewards for a specified target population.""" + +from __future__ import annotations +import torch + + +def rbf_mmd2(x: torch.Tensor, y: torch.Tensor, gamma: float) -> torch.Tensor: + """Biased RBF MMD squared for (n,d) and (m,d) populations.""" + k = lambda a, b: torch.exp(-gamma * torch.cdist(a, b).square()) + return k(x, x).mean() + k(y, y).mean() - 2 * k(x, y).mean() + + +class Reward: + """Per-cell or population reward; call returns (n,) for a population (n,d). + + Centroid reward averages squared cell distances, including population + spread. Cosine reward averages per-cell effect cosines relative to a fixed + control centroid. MMD uses a training-derived fixed bandwidth. + """ + + def __init__( + self, + kind="centroid", + target_c=None, + target_sample=None, + device="cpu", + control_ref=None, + gamma=None, + ): + if kind not in ("centroid", "cosine", "mmd", "wasserstein", "nn_target"): + raise ValueError(f"Unsupported reward {kind}") + self.kind = kind + self.target_sample = ( + torch.as_tensor(target_sample, dtype=torch.float32, device=device) + if target_sample is not None + else None + ) + self.target_c = ( + torch.as_tensor(target_c, dtype=torch.float32, device=device) + if target_c is not None + else self.target_sample.mean(0) + ) + self.control_ref = ( + torch.as_tensor(control_ref, dtype=torch.float32, device=device) + if control_ref is not None + else None + ) + self.gamma = gamma + if kind == "cosine" and self.control_ref is None: + raise ValueError("Cosine requires a fixed control reference") + if kind == "mmd" and (gamma is None or gamma <= 0): + raise ValueError("MMD requires a positive fixed gamma") + if kind in ("mmd", "wasserstein", "nn_target") and self.target_sample is None: + raise ValueError("Population reward needs target samples") + + def __call__(self, chat: torch.Tensor) -> torch.Tensor: + if self.kind == "centroid": + return -(chat - self.target_c).square().sum(-1) + if self.kind == "cosine": + p = chat - self.control_ref + t = self.target_c - self.control_ref + return (p * t).sum(-1) / (p.norm(dim=-1) * t.norm() + 1e-8) + if self.kind == "mmd": + return -rbf_mmd2(chat, self.target_sample, self.gamma).expand(len(chat)) + if self.kind == "nn_target": + return -torch.cdist(chat, self.target_sample).square().min(1).values + # Fixed one-dimensional projections permit differentiable sorting. Quantile + # interpolation supports unequal source and target population sizes. + gen = torch.Generator(device=chat.device).manual_seed(0) + w = torch.randn(50, chat.shape[1], generator=gen, device=chat.device) + w = w / w.norm(dim=1, keepdim=True) + xp = chat @ w.T + yp = self.target_sample @ w.T + q = torch.linspace(0, 1, 256, device=chat.device) + cost = ( + (torch.quantile(xp, q, dim=0) - torch.quantile(yp, q, dim=0)).abs().mean() + ) + return -cost.expand(len(chat)) diff --git a/src/pivot/evaluation/runner.py b/src/pivot/evaluation/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..c62f9872ab9ba4cbc8e178977ea81bb8801ff063 --- /dev/null +++ b/src/pivot/evaluation/runner.py @@ -0,0 +1,268 @@ +"""Evaluate fixed populations and intervention catalogs with condition-level rows.""" + +from __future__ import annotations +import json, time +from pathlib import Path +import numpy as np +import torch +from pivot.evaluation.metrics import ( + correlation, + mmd2, + sliced_wasserstein, + energy_distance, + retrieval_metrics, + bootstrap, +) +from pivot.evaluation.rewards import Reward +from pivot.evaluation.inference import ( + encode_label, + forward_predict, + reward_guidance, + project_and_rerank, +) + + +def save_json(path, value): + """Write strict JSON. Undefined numerical statistics become null.""" + + def clean(x): + if isinstance(x, dict): + return {str(k): clean(v) for k, v in x.items()} + if isinstance(x, (list, tuple)): + return [clean(v) for v in x] + if isinstance(x, (float, np.floating)): + return float(x) if np.isfinite(x) else None + if isinstance(x, np.integer): + return int(x) + return x + + p = Path(path) + p.parent.mkdir(parents=True, exist_ok=True) + p.write_text(json.dumps(clean(value), indent=2, allow_nan=False)) + + +def evaluate( + data, + predict, + output, + method="PIVOT", + partition="test", + catalog="single", + reward_kind="cosine", + n_cells=128, + seed=0, + model=None, + device="cpu", + guidance_steps=25, + step_size=0.5, + k_nearest=10, + initialization="best", + max_targets=None, +): + """Forward and inverse evaluation on disjoint query and reference cells. + + Feature errors use normalized expression, with each target's observed top-20 + effect genes selected once. Population metrics use PCA coordinates. Inverse + query populations are disjoint from the reserved candidate-outcome cells. + The target condition is the aggregation and bootstrap unit. + """ + rng = np.random.default_rng(seed) + ci = data.indices(partition, True) + if not len(ci): + raise ValueError("Evaluation needs controls in the requested partition") + ci = rng.choice(ci, min(n_cells, len(ci)), replace=False) + c0 = data.emb[ci] + control_expr = np.asarray(data.Xhvg[ci].mean(0)).ravel() + control_latent = c0.mean(0) + candidates = ( + data.singles + if catalog == "single" + else data.combos if catalog == "combination" else data.perturbations + ) + if not candidates: + raise ValueError("Candidate catalog is empty") + labels = data.labels(partition) + if max_targets: + labels = sorted( + rng.choice(labels, min(max_targets, len(labels)), replace=False) + ) + # Cache endpoint predictions once for each candidate. Count all candidate-cell + # predictions, including those used to choose a warm-start embedding. + t0 = time.perf_counter() + predictions = {p: np.asarray(predict(c0, p)) for p in candidates} + catalog_seconds = time.perf_counter() - t0 + query_ids = data.indices(partition, False) + refids = data.indices("reference", False) + reference = { + p: data.emb[np.intersect1d(refids, data.pert_to_idx[p])] for p in candidates + } + if any(len(v) == 0 for v in reference.values()): + raise ValueError("Every candidate needs independent reference outcomes") + forward = [] + inverse = [] + for p in labels: + ids = np.intersect1d(query_ids, data.pert_to_idx[p]) + ids = rng.choice(ids, min(n_cells, len(ids)), replace=False) + target = data.emb[ids] + pred = predictions.get(p) + if pred is None: + pred = np.asarray(predict(c0, p)) + expression = np.asarray(data.Xhvg[ids].mean(0)).ravel() + pred_expr = data.decode_to_genes(pred).mean(0) + effect = expression - control_expr + pred_effect = pred_expr - control_expr + de = np.argsort(-np.abs(effect))[: min(20, len(effect))] + train_genes = {g for q in data.labels("train") for g in data.parse(q)} + forward.append( + { + "target": p, + "query_cells": len(ids), + "genes_observed_in_training": all( + g in train_genes for g in data.parse(p) + ), + "mse_expression": float(np.mean((pred_expr - expression) ** 2)), + "effect_pearson": correlation(pred_effect, effect), + "effect_pearson_de20": correlation(pred_effect[de], effect[de]), + "mmd2": mmd2(pred, target, data.meta["mmd_gamma"]), + "sliced_wasserstein": sliced_wasserstein(pred, target, seed=seed), + "energy": energy_distance(pred, target), + "variance_ratio": float( + np.var(pred, axis=0).sum() + / max(np.var(target, axis=0).sum(), 1e-12) + ), + } + ) + if p not in candidates: + continue + reward = Reward( + reward_kind, + target_sample=target, + control_ref=control_latent, + gamma=data.meta["mmd_gamma"], + device=device, + ) + score = lambda pop: float( + reward(torch.as_tensor(pop, dtype=torch.float32, device=device)) + .mean() + .detach() + .cpu() + ) + measured = {q: score(v) for q, v in reference.items()} + scores = {q: score(v) for q, v in predictions.items()} + ranked = sorted(candidates, key=lambda q: (-scores[q], q)) + tied = sum(abs(scores[q] - scores[ranked[0]]) < 1e-10 for q in candidates) + + def row(name, ranking, cost, seconds, continuous=None): + selected = ranking[0] + out = { + "target": p, + "search": name, + **retrieval_metrics(ranking, p), + "selected": selected, + "predicted_reward": scores.get(selected), + "measured_reward": measured[selected], + "measured_regret": max(measured.values()) - measured[selected], + "selected_gene_overlap": len( + set(data.parse(selected)) & set(data.parse(p)) + ) + / max(len(set(data.parse(p))), 1), + "catalog_size": len(candidates), + "candidate_cell_evaluations": cost, + "seconds": seconds, + "top_score_ties": tied, + } + if continuous is not None: + out["continuous_reward"] = continuous + return out + + inverse.append( + row( + "exhaustive", + ranked, + len(candidates) * len(c0), + catalog_seconds / len(labels), + ) + ) + if model is not None and guidance_steps > 0: + # A best-candidate initialization uses the complete ranked catalog. + # Random initialization avoids this search but still projects over all embeddings. + init = ( + ranked[0] if initialization == "best" else str(rng.choice(candidates)) + ) + e = encode_label(model, data, init, device) + ct = torch.as_tensor(c0, device=device) + t1 = time.perf_counter() + es = reward_guidance(model, ct, reward, e, guidance_steps, step_size) + guided = project_and_rerank( + model, data, candidates, es, ct, reward, k_nearest, k_nearest, device + ) + continuous = score(forward_predict(model, ct, es).cpu().numpy()) + names = [q for q, _ in guided] + cost = ( + (len(candidates) if initialization == "best" else 0) + + guidance_steps + + min(k_nearest, len(candidates)) + ) + gr = row( + "guidance_" + initialization, + names, + cost * len(c0), + time.perf_counter() + - t1 + + (catalog_seconds / len(labels) if initialization == "best" else 0), + continuous, + ) + gr["initial_candidate"] = init + gr["predicted_reward"] = guided[0][1] + gr["projection_reward_change"] = guided[0][1] - continuous + inverse.append(gr) + summary = { + "forward": { + k: bootstrap([r[k] for r in forward], seed) + for k in ( + "mse_expression", + "effect_pearson", + "effect_pearson_de20", + "mmd2", + "sliced_wasserstein", + "energy", + "variance_ratio", + ) + }, + "inverse": {}, + } + for search in sorted({r["search"] for r in inverse}): + rows = [r for r in inverse if r["search"] == search] + summary["inverse"][search] = { + k: bootstrap([r[k] for r in rows], seed) + for k in ( + "top1", + "top5", + "ndcg10", + "measured_reward", + "measured_regret", + "selected_gene_overlap", + ) + } + result = { + "protocol": "split-first-v1", + "method": method, + "data_meta": data.meta, + "partition": partition, + "catalog": catalog, + "reward": reward_kind, + "seed": seed, + "n_controls": len(c0), + "query_cell_ids": data.obs.iloc[query_ids].cell_id.tolist(), + "guidance": { + "steps": guidance_steps, + "step_size": step_size, + "k_nearest": k_nearest, + "initialization": initialization, + }, + "summary": summary, + "forward": forward, + "inverse": inverse, + } + save_json(output, result) + return result diff --git a/src/pivot/models/__init__.py b/src/pivot/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/pivot/models/encoders.py b/src/pivot/models/encoders.py new file mode 100644 index 0000000000000000000000000000000000000000..4a68527af124eb36ad59c9fd6f1cb5ed18c685f6 --- /dev/null +++ b/src/pivot/models/encoders.py @@ -0,0 +1,108 @@ +"""perturbation encoder ψ_η plus a builder that turns perturbation labels into +padded (gene_id, op_id, mask) tensors. + +ψ separates gene identity from operation and is permutation-invariant over the +gene set (mean pooling), so one encoder handles single-gene and combinatorial +perturbations: + + e_u = (1/M) Σ_j ( W_g[g_j] + W_o[o_j] ) + +rep modes: gene_op (default), op_only, gene_only, random_id, gene_pathway_op. +""" + +from __future__ import annotations + +import numpy as np +import torch +import torch.nn as nn + +REP_MODES = ("gene_op", "op_only", "gene_only", "random_id", "gene_pathway_op") + + +class PerturbationEncoder(nn.Module): + def __init__( + self, + n_genes: int, + n_ops: int, + n_perts: int, + emb_dim: int = 64, + mode: str = "gene_op", + gene_pathway: ( + np.ndarray | None + ) = None, # (n_genes,) cluster id, for gene_pathway_op mode + n_pathways: int = 0, + ): + super().__init__() + assert mode in REP_MODES, mode + self.mode = mode + self.emb_dim = emb_dim + self.W_g = nn.Embedding(n_genes, emb_dim) + self.W_o = nn.Embedding(n_ops, emb_dim) + nn.init.normal_(self.W_g.weight, std=0.02) + nn.init.normal_(self.W_o.weight, std=0.02) + if mode == "random_id": + self.W_id = nn.Embedding(n_perts + 1, emb_dim) # +1 for control/unknown + nn.init.normal_(self.W_id.weight, std=0.02) + if mode == "gene_pathway_op": + assert gene_pathway is not None and n_pathways > 0 + self.register_buffer( + "gene_pathway", torch.as_tensor(gene_pathway, dtype=torch.long) + ) + self.W_path = nn.Embedding(n_pathways, emb_dim) + nn.init.normal_(self.W_path.weight, std=0.02) + + def forward( + self, + gene_ids: torch.Tensor, + op_ids: torch.Tensor, + mask: torch.Tensor, + pert_ids: torch.Tensor | None = None, + ) -> torch.Tensor: + """gene_ids/op_ids/mask: (B, Lmax) padded; mask 1 for valid gene slots. + + returns e_u: (B, emb_dim). control rows (mask all zero) map to the zero vector. + """ + if self.mode == "random_id": + return self.W_id(pert_ids) + + m = mask.unsqueeze(-1).float() # (b, l, 1) + if self.mode == "op_only": + per_gene = self.W_o(op_ids) + elif self.mode == "gene_only": + per_gene = self.W_g(gene_ids) + elif self.mode == "gene_pathway_op": + path_ids = self.gene_pathway[gene_ids.clamp(min=0)] + per_gene = self.W_g(gene_ids) + self.W_o(op_ids) + self.W_path(path_ids) + else: # gene_op + per_gene = self.W_g(gene_ids) + self.W_o(op_ids) + per_gene = per_gene * m + denom = m.sum(dim=1).clamp( + min=1.0 + ) # (b,1); control -> 1 to avoid /0 (sum is 0 anyway) + return per_gene.sum(dim=1) / denom + + +def build_pert_tensors(data, labels, device="cpu"): + """convert a list of perturbation labels into padded (gene_ids, op_ids, mask, pert_ids). + + pert_ids index into ``data.perturbations`` (len = control/unknown sentinel) for random_id mode. + """ + pert_index = {p: i for i, p in enumerate(data.perturbations)} + L = max((len(data.parse(p)) for p in labels), default=1) + L = max(L, 1) + B = len(labels) + g = np.zeros((B, L), dtype=np.int64) + o = np.zeros((B, L), dtype=np.int64) + mask = np.zeros((B, L), dtype=np.float32) + pid = np.full(B, len(data.perturbations), dtype=np.int64) # sentinel + for i, p in enumerate(labels): + gids, oids = data.pert_gene_op_ids(p) + n = len(gids) + if n: + g[i, :n] = gids + o[i, :n] = oids + mask[i, :n] = 1.0 + if p in pert_index: + pid[i] = pert_index[p] + t = lambda a: torch.as_tensor(a, device=device) + return t(g), t(o), t(mask), t(pid) diff --git a/src/pivot/models/flow_map.py b/src/pivot/models/flow_map.py new file mode 100644 index 0000000000000000000000000000000000000000..44836980ee331b569338352ea15ea8f27785631e --- /dev/null +++ b/src/pivot/models/flow_map.py @@ -0,0 +1,78 @@ +"""perturbation-conditioned flow map X_θ. + + X_θ(s, t, c_s, e_u) = c_s + (t - s) · v_θ(s, t, c_s, e_u), 0 ≤ s ≤ t ≤ 1 + ĉ1(u) = X_θ(0, 1, c0, e_u) + +v_θ is an mlp over [γ(s), γ(t), c_s, e_u], γ is a fourier time embedding. +the map is differentiable in e_u, which is what enables the inverse-design +jacobian ∇_{e_u} X_{0,1} used by reward guidance (Alg 4). +""" + +from __future__ import annotations + +import math + +import torch +import torch.nn as nn + + +class FourierTime(nn.Module): + """map a scalar t∈[0,1] to [t, sin(2πk t), cos(2πk t) ...].""" + + def __init__(self, n_freq: int = 6): + super().__init__() + self.register_buffer( + "freqs", 2 * math.pi * (2.0 ** torch.arange(n_freq)).float() + ) + + def forward(self, t: torch.Tensor) -> torch.Tensor: + t = t.view(-1, 1) + ang = t * self.freqs.view(1, -1) + return torch.cat([t, torch.sin(ang), torch.cos(ang)], dim=-1) + + +class FlowMap(nn.Module): + def __init__( + self, + d_state: int, + d_pert: int, + hidden: int = 512, + depth: int = 4, + n_freq: int = 6, + dropout: float = 0.0, + ): + super().__init__() + self.d_state = d_state + self.d_pert = d_pert + self.time = FourierTime(n_freq) + t_dim = 1 + 2 * n_freq + in_dim = 2 * t_dim + d_state + d_pert + layers = [nn.Linear(in_dim, hidden), nn.SiLU()] + for _ in range(depth - 1): + layers += [nn.Linear(hidden, hidden), nn.SiLU()] + if dropout > 0: + layers += [nn.Dropout(dropout)] + layers += [nn.Linear(hidden, d_state)] + self.net = nn.Sequential(*layers) + # zero-init final layer => v≈0 at init => X≈identity (stable warm start) + nn.init.zeros_(self.net[-1].weight) + nn.init.zeros_(self.net[-1].bias) + + def velocity(self, s, t, c_s, e_u): + s = s.expand(c_s.shape[0]) if s.dim() == 0 else s + t = t.expand(c_s.shape[0]) if t.dim() == 0 else t + h = torch.cat([self.time(s), self.time(t), c_s, e_u], dim=-1) + return self.net(h) + + def forward(self, s, t, c_s, e_u): + """X_θ(s,t,c_s,e_u). s,t are scalars or (b,) tensors in [0,1].""" + v = self.velocity(s, t, c_s, e_u) + dt = t - s + dt = dt.view(-1, 1) if torch.is_tensor(dt) and dt.dim() >= 1 else dt + return c_s + dt * v + + def endpoint(self, c0, e_u): + """ĉ1 = X_θ(0,1,c0,e_u).""" + zeros = torch.zeros(c0.shape[0], device=c0.device) + ones = torch.ones(c0.shape[0], device=c0.device) + return self.forward(zeros, ones, c0, e_u) diff --git a/src/pivot/models/pivot.py b/src/pivot/models/pivot.py new file mode 100644 index 0000000000000000000000000000000000000000..e651c9ca924295cdcec611c0f143d06c728e2a38 --- /dev/null +++ b/src/pivot/models/pivot.py @@ -0,0 +1,72 @@ +"""the pivot model: perturbation encoder ψ_η + flow map X_θ, plus the +inverse-design jacobian used by reward guidance (Alg 4).""" + +from __future__ import annotations + +import numpy as np +import torch +import torch.nn as nn + +from pivot.models.encoders import PerturbationEncoder, build_pert_tensors +from pivot.models.flow_map import FlowMap + + +class PIVOT(nn.Module): + def __init__( + self, + d_state: int, + n_genes: int, + n_ops: int, + n_perts: int, + d_pert: int = 64, + hidden: int = 512, + depth: int = 4, + rep_mode: str = "gene_op", + gene_pathway=None, + n_pathways: int = 0, + dropout: float = 0.0, + ): + super().__init__() + self.encoder = PerturbationEncoder( + n_genes, + n_ops, + n_perts, + emb_dim=d_pert, + mode=rep_mode, + gene_pathway=gene_pathway, + n_pathways=n_pathways, + ) + self.flow = FlowMap( + d_state, d_pert, hidden=hidden, depth=depth, dropout=dropout + ) + self.d_state, self.d_pert = d_state, d_pert + + def encode(self, g, o, mask, pid): + return self.encoder(g, o, mask, pid) + + def endpoint_from_e(self, c0, e): + return self.flow.endpoint(c0, e) + + def endpoint_from_pert(self, c0, g, o, mask, pid): + e = self.encode(g, o, mask, pid) + if e.shape[0] == 1 and c0.shape[0] > 1: + e = e.expand(c0.shape[0], -1) + return self.flow.endpoint(c0, e) + + # --- inverse-design jacobian (Alg 4 building block) --- + def endpoint_jacobian_e(self, c0_single, e_single): + """∇_e X_{0,1}(c0, e) for a single (c0, e). returns (d_state, d_pert).""" + e = e_single.detach().clone().requires_grad_(True) + c0 = c0_single.unsqueeze(0) + + def f(ev): + return self.flow.endpoint(c0, ev.unsqueeze(0)).squeeze(0) + + return torch.autograd.functional.jacobian(f, e, create_graph=False) + + +@torch.no_grad() +def candidate_embeddings(model: PIVOT, data, labels, device) -> np.ndarray: + """ψ(u) for every candidate perturbation label -> (n_cand, d_pert).""" + g, o, mask, pid = build_pert_tensors(data, labels, device=device) + return model.encode(g, o, mask, pid).cpu().numpy() diff --git a/src/pivot/training/__init__.py b/src/pivot/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/pivot/training/losses.py b/src/pivot/training/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..b04196d3c4c2e82c93baee6ec8834ff7c816bee8 --- /dev/null +++ b/src/pivot/training/losses.py @@ -0,0 +1,72 @@ +"""pivot training losses: + + L_map = E‖X_θ(s,t,c_s,e_u) − c_t‖² + L_tan = E‖v_θ(τ,τ,c_τ,e_u) − (c1−c0)‖² + L_semi = E‖X_θ(s,t,c_s,e_u) − X_θ(r,t, X_θ(s,r,c_s,e_u), e_u)‖² + L_reg = ‖e_u‖² + L = L_map + λ_tan·L_tan + λ_semi·L_semi + λ_reg·L_reg + +c_s, c_t, c_τ are linear interpolants between matched control c0 and perturbed c1. +""" + +from __future__ import annotations + +import torch + + +def _sorted_uniform(n, k, device): + """return k columns of sorted U(0,1) samples: shape (n, k), each row ascending.""" + u = torch.rand(n, k, device=device) + return torch.sort(u, dim=1).values + + +def interp(c0, c1, frac): + """(1-frac)·c0 + frac·c1, frac shape (b,).""" + f = frac.view(-1, 1) + return (1 - f) * c0 + f * c1 + + +def compute_losses(flow, e_u, c0, c1, lambdas: dict): + """return (total, dict_of_components). shapes: c0,c1,(b,d); e_u (b,m).""" + B = c0.shape[0] + dev = c0.device + + # map loss: 0<=s<=t<=1 + st = _sorted_uniform(B, 2, dev) + s, t = st[:, 0], st[:, 1] + c_s = interp(c0, c1, s) + c_t = interp(c0, c1, t) + pred_t = flow(s, t, c_s, e_u) + L_map = ((pred_t - c_t) ** 2).sum(-1).mean() + + # tangent loss: diagonal velocity matches interpolation velocity (c1-c0) + tau = torch.rand(B, device=dev) + c_tau = interp(c0, c1, tau) + v_diag = flow.velocity(tau, tau, c_tau, e_u) + L_tan = ((v_diag - (c1 - c0)) ** 2).sum(-1).mean() + + # The source state must use the starting time sampled for this loss. + srt = _sorted_uniform(B, 3, dev) + s2, r2, t2 = srt[:, 0], srt[:, 1], srt[:, 2] + c_s2 = interp(c0, c1, s2) + direct = flow(s2, t2, c_s2, e_u) + mid = flow(s2, r2, c_s2, e_u) + composed = flow(r2, t2, mid, e_u) + L_semi = ((direct - composed) ** 2).sum(-1).mean() + + # regularizer + L_reg = (e_u**2).sum(-1).mean() + + total = ( + lambdas.get("map", 1.0) * L_map + + lambdas.get("tan", 1.0) * L_tan + + lambdas.get("semi", 1.0) * L_semi + + lambdas.get("reg", 1e-4) * L_reg + ) + return total, { + "map": L_map.item(), + "tan": L_tan.item(), + "semi": L_semi.item(), + "reg": L_reg.item(), + "total": total.item(), + } diff --git a/src/pivot/training/train.py b/src/pivot/training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..209fbdab1f4563f0724b259d57b0ff8bef29be3e --- /dev/null +++ b/src/pivot/training/train.py @@ -0,0 +1,219 @@ +"""Train the approved residual map and retain validation-selected checkpoints.""" + +from __future__ import annotations +import copy, json, time, platform +from pathlib import Path +from dataclasses import dataclass, asdict +import numpy as np +import torch +from pivot.models.pivot import PIVOT +from pivot.models.encoders import build_pert_tensors +from pivot.training.losses import compute_losses +from pivot.evaluation.rewards import rbf_mmd2 +from pivot.utils.common import set_seed + + +@dataclass +class TrainConfig: + d_pert: int = 64 + hidden: int = 512 + depth: int = 4 + epochs: int = 60 + batch_size: int = 1024 + lr: float = 1e-3 + weight_decay: float = 1e-5 + lam_tan: float = 1.0 + lam_semi: float = 0.5 + lam_reg: float = 1e-4 + lam_dist: float = 0.0 + n_dist_perts: int = 4 + dist_n: int = 64 + grad_clip: float = 5.0 + match: str = "batch" + rep_mode: str = "gene_op" + seed: int = 0 + device: str = "cpu" + threads: int = 4 + endpoint_weight: float = 0.0 + + +def make_model(data, cfg): + if cfg.rep_mode not in ("gene_op", "gene_only", "op_only", "random_id"): + raise ValueError("Supported encoders use gene/operation metadata only") + return PIVOT( + data.d, + len(data.genes_vocab), + len(data.op_vocab), + len(data.perturbations), + d_pert=cfg.d_pert, + hidden=cfg.hidden, + depth=cfg.depth, + rep_mode=cfg.rep_mode, + ).to(cfg.device) + + +@torch.no_grad() +def validation_loss(model, data, cfg) -> float: + """Mean condition-level endpoint MSE against validation centroids in PCA space.""" + rng = np.random.default_rng(cfg.seed + 910) + ctr = data.indices("val", True) + vid = data.indices("val", False) + c0 = torch.as_tensor( + data.emb[rng.choice(ctr, min(128, len(ctr)), replace=False)], device=cfg.device + ) + model.eval() + loss = [] + for label in data.labels("val"): + ids = np.intersect1d(data.pert_to_idx[label], vid) + g, o, m, pid = build_pert_tensors(data, [label], cfg.device) + pred = model.endpoint_from_pert(c0, g, o, m, pid).mean(0) + truth = torch.as_tensor(data.emb[ids].mean(0), device=cfg.device) + loss.append((pred - truth).square().mean().item()) + return float(np.mean(loss)) + + +def train(data, cfg: TrainConfig, output: str, resume: str | None = None) -> dict: + """Fit using training cells/controls; write best.pt, last.pt, config and history. + + Checkpoints retain the vocabulary and cache fingerprint. Resume restores + optimizer, scheduler, NumPy RNG, and Torch RNG state before the next epoch. + """ + set_seed(cfg.seed) + torch.set_num_threads(cfg.threads) + if cfg.device.startswith("cuda") and not torch.cuda.is_available(): + raise RuntimeError("Requested CUDA is unavailable") + out = Path(output) + out.mkdir(parents=True, exist_ok=True) + model = make_model(data, cfg) + opt = torch.optim.AdamW( + model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay + ) + sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=cfg.epochs) + rng = np.random.default_rng(cfg.seed) + start = 0 + history = [] + best = float("inf") + if resume: + ck = torch.load(resume, map_location=cfg.device, weights_only=False) + if ck["data_meta"] != data.meta or ck["config"] != asdict(cfg): + raise ValueError("Resume configuration or data mismatch") + model.load_state_dict(ck["model"]) + opt.load_state_dict(ck["optimizer"]) + sched.load_state_dict(ck["scheduler"]) + rng.bit_generator.state = ck["numpy_rng"] + torch.set_rng_state(ck["torch_rng"].cpu()) + if ck.get("cuda_rng") is not None and torch.cuda.is_available(): + torch.cuda.set_rng_state_all(ck["cuda_rng"]) + start = ck["epoch"] + 1 + history = ck["history"] + best = ck["best_val"] + train_ids = data.indices("train", False) + ctrl = data.indices("train", True) + labels = data.obs.perturbation.to_numpy() + z = torch.as_tensor(data.emb, device=cfg.device) + groups = {p: train_ids[labels[train_ids] == p] for p in data.labels("train")} + lam = {"map": 1.0, "tan": cfg.lam_tan, "semi": cfg.lam_semi, "reg": cfg.lam_reg} + t0 = time.perf_counter() + for epoch in range(start, cfg.epochs): + model.train() + terms = [] + for ids in np.array_split( + rng.permutation(train_ids), int(np.ceil(len(train_ids) / cfg.batch_size)) + ): + ci = data.sample_controls(ids, cfg.match, rng, ctrl) + g, o, m, pid = build_pert_tensors(data, labels[ids], cfg.device) + e = model.encode(g, o, m, pid) + total, parts = compute_losses(model.flow, e, z[ci], z[ids], lam) + if cfg.endpoint_weight: + le = (model.flow.endpoint(z[ci], e) - z[ids]).square().sum(-1).mean() + total = total + cfg.endpoint_weight * le + parts["endpoint"] = le.item() + if cfg.lam_dist: + ds = [] + for p in rng.choice( + list(groups), min(cfg.n_dist_perts, len(groups)), replace=False + ): + yi = rng.choice( + groups[p], min(cfg.dist_n, len(groups[p])), replace=False + ) + xi = data.sample_controls(yi, cfg.match, rng, ctrl) + gd, od, md, pd = build_pert_tensors(data, [p], cfg.device) + yp = model.endpoint_from_pert(z[xi], gd, od, md, pd) + ds.append(rbf_mmd2(yp, z[yi], data.meta["mmd_gamma"])) + ld = torch.stack(ds).mean() + total = total + cfg.lam_dist * ld + parts["dist"] = ld.item() + opt.zero_grad() + total.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.grad_clip) + opt.step() + parts["total"] = total.item() + terms.append(parts) + sched.step() + val = validation_loss(model, data, cfg) + record = { + "epoch": epoch, + "validation_mse": val, + **{k: float(np.mean([t[k] for t in terms])) for k in terms[0]}, + } + history.append(record) + improved = val < best + best = min(best, val) + ck = { + "protocol": "split-first-v1", + "model": model.state_dict(), + "optimizer": opt.state_dict(), + "scheduler": sched.state_dict(), + "epoch": epoch, + "best_val": best, + "config": asdict(cfg), + "data_meta": data.meta, + "gene_vocab": data.genes_vocab, + "perturbations": data.perturbations, + "numpy_rng": rng.bit_generator.state, + "torch_rng": torch.get_rng_state(), + "cuda_rng": ( + torch.cuda.get_rng_state_all() if torch.cuda.is_available() else None + ), + "history": history, + } + torch.save(ck, out / "last.pt") + if improved: + torch.save(ck, out / "best.pt") + print( + f"epoch {epoch+1}/{cfg.epochs} train={record['total']:.4f} val={val:.4f}", + flush=True, + ) + info = { + "protocol": "split-first-v1", + "config": asdict(cfg), + "history": history, + "duration_s": time.perf_counter() - t0, + "n_train_cells": len(train_ids), + "n_parameters": sum(p.numel() for p in model.parameters()), + "software": { + "python": platform.python_version(), + "torch": torch.__version__, + "numpy": np.__version__, + }, + } + (out / "training.json").write_text(json.dumps(info, indent=2)) + return info + + +def load_checkpoint(path, data, device="cpu"): + """Load trusted local checkpoint with an exact cache/vocabulary match.""" + ck = torch.load(path, map_location=device, weights_only=False) + if ck.get("protocol") != "split-first-v1": + raise ValueError( + "Historical weights need their original preprocessing and archived loader" + ) + if ck["data_meta"] != data.meta or ck["gene_vocab"] != data.genes_vocab: + raise ValueError("Checkpoint and cache do not match") + cfg = TrainConfig(**ck["config"]) + cfg.device = device + torch.set_num_threads(cfg.threads) + model = make_model(data, cfg) + model.load_state_dict(ck["model"]) + model.eval() + return model, cfg diff --git a/src/pivot/utils/__init__.py b/src/pivot/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/pivot/utils/common.py b/src/pivot/utils/common.py new file mode 100644 index 0000000000000000000000000000000000000000..3a7b322878c1a7296eb42c5fb86344254e4453e6 --- /dev/null +++ b/src/pivot/utils/common.py @@ -0,0 +1,84 @@ +"""shared utils: seeding, device selection, timing, json i/o.""" + +from __future__ import annotations + +import json +import os +import random +import time +from contextlib import contextmanager + +import numpy as np + + +def set_seed(seed: int = 0) -> None: + """seed python, numpy and torch (if available) for reproducibility.""" + random.seed(seed) + np.random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + try: + import torch + + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # determinism for reproducibility; benchmark off + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + except Exception: + pass + + +def pick_device(prefer_index: int | None = None): + """return a torch device, picking the gpu with the most free memory. + + on this shared node several gpus may be partly occupied, so we grab the + emptiest one unless ``prefer_index`` is given. + """ + import torch + + if not torch.cuda.is_available(): + return torch.device("cpu") + if prefer_index is not None: + return torch.device(f"cuda:{prefer_index}") + best, best_free = 0, -1 + for i in range(torch.cuda.device_count()): + free, _ = torch.cuda.mem_get_info(i) + if free > best_free: + best, best_free = i, free + return torch.device(f"cuda:{best}") + + +@contextmanager +def timer(name: str = "block"): + """context manager yielding wall-clock seconds via ``.t``.""" + + class _T: + t = 0.0 + + obj = _T() + start = time.perf_counter() + try: + yield obj + finally: + obj.t = time.perf_counter() - start + + +def save_json(obj, path: str) -> None: + os.makedirs(os.path.dirname(path), exist_ok=True) + with open(path, "w") as f: + json.dump(obj, f, indent=2, default=_json_default) + + +def load_json(path: str): + with open(path) as f: + return json.load(f) + + +def _json_default(o): + if isinstance(o, (np.integer,)): + return int(o) + if isinstance(o, (np.floating,)): + return float(o) + if isinstance(o, (np.ndarray,)): + return o.tolist() + return str(o) diff --git a/tests/test_integrity.py b/tests/test_integrity.py new file mode 100644 index 0000000000000000000000000000000000000000..61c1666a3725d9f920d68da127888e72af52d35f --- /dev/null +++ b/tests/test_integrity.py @@ -0,0 +1,198 @@ +"""Regression tests for data isolation, flow-map times, gradients and population metrics.""" + +from pathlib import Path +import json +import numpy as np +import pandas as pd +import pytest +import torch +from pivot.data.preprocess import assign_splits, prepare +from pivot.data.perturb_data import PerturbData +from pivot.models.flow_map import FlowMap +from pivot.models.encoders import PerturbationEncoder +from pivot.training.losses import compute_losses +from pivot.evaluation.rewards import Reward, rbf_mmd2 +from pivot.evaluation.metrics import mmd2, retrieval_metrics + + +def test_diagonal_identity(): + m = FlowMap(3, 2, hidden=8, depth=1) + torch.nn.init.normal_(m.net[-1].weight) + t = torch.rand(5) + c = torch.randn(5, 3) + e = torch.randn(5, 2) + torch.testing.assert_close(m(t, t, c, e), c, rtol=0, atol=0) + + +def test_pooling_permutation(): + m = PerturbationEncoder(5, 2, 10, emb_dim=3) + g = torch.tensor([[1, 3]]) + o = torch.ones_like(g) + mask = torch.ones_like(g) + torch.testing.assert_close(m(g, o, mask), m(g.flip(1), o, mask)) + + +def test_semigroup_uses_corresponding_source_time(): + class RecordedFlow: + def __init__(self): + self.calls = [] + + def __call__(self, s, t, c, e): + self.calls.append((s.clone(), t.clone(), c.clone())) + return c + (t - s)[:, None] * 2 + + def velocity(self, s, t, c, e): + return torch.ones_like(c) * 2 + + f = RecordedFlow() + c0 = torch.zeros(12, 3) + c1 = c0 + 2 + e = torch.zeros(12, 2) + loss, parts = compute_losses(f, e, c0, c1, {}) + # Call 0 is map supervision; calls 1 and 2 start the composition comparison. + for s, t, c in [f.calls[1], f.calls[2]]: + torch.testing.assert_close(c, 2 * s[:, None].expand_as(c)) + assert parts["semi"] < 1e-10 + + +def test_reward_gradient_matches_finite_difference(): + torch.manual_seed(1) + a = torch.randn(3, 2, dtype=torch.double) + e = torch.randn(2, dtype=torch.double, requires_grad=True) + target = torch.randn(3, dtype=torch.double) + r = lambda z: -((a @ z - target) ** 2).sum() + (g,) = torch.autograd.grad(r(e), e) + h = 1e-6 + numeric = torch.stack( + [ + ( + r(e.detach() + h * torch.eye(2, dtype=torch.double)[i]) + - r(e.detach() - h * torch.eye(2, dtype=torch.double)[i]) + ) + / (2 * h) + for i in range(2) + ] + ) + torch.testing.assert_close(g, numeric, rtol=1e-6, atol=1e-6) + + +def test_numpy_torch_population_statistic(): + rng = np.random.default_rng(2) + x = rng.normal(size=(7, 3)) + y = rng.normal(size=(11, 3)) + assert np.isclose( + mmd2(x, y, 0.2), rbf_mmd2(torch.tensor(x), torch.tensor(y), 0.2).item() + ) + assert abs(mmd2(x, x, 0.2)) < 1e-12 + + +def test_retrieval_censoring(): + assert retrieval_metrics(["A", "B"], "C") == dict( + top1=0.0, top5=0.0, ndcg10=0.0, rank=None + ) + + +def test_held_out_genes_do_not_occur_in_training(): + labels = [ + "control", + "A", + "B", + "C", + "D", + "E", + "F", + "G", + "H", + "A_B", + "C_D", + "E_F", + "G_H", + ] + o = pd.DataFrame({"perturbation": np.repeat(labels, 20)}) + o["is_control"] = o.perturbation.eq("control") + split = assign_splits(o, "gene", 5) + genes = lambda part: { + g + for p in o.loc[(split == part) & ~o.is_control, "perturbation"] + for g in p.split("_") + } + held_single = set( + o.loc[ + (split == "test") & ~o.is_control & ~o.perturbation.str.contains("_"), + "perturbation", + ] + ) + assert held_single and not genes("train") & held_single + assert not genes("val") & held_single + + +@pytest.fixture(scope="module") +def prepared(tmp_path_factory): + raw = Path(__file__).parents[1] / "fixtures/norman_small.h5ad" + d = tmp_path_factory.mktemp("cache") + prepare(str(raw), str(d), "norman", n_hvg=100, n_pca=5, seed=0) + return raw, PerturbData(str(d)) + + +def test_partition_ids_are_disjoint(prepared): + _, d = prepared + parts = [set(d.indices(p)) for p in ["train", "val", "test", "reference"]] + assert len(set.union(*parts)) == len(d.obs) + for i, a in enumerate(parts): + for b in parts[i + 1 :]: + assert not a & b + assert all(len(d.indices(p, True)) for p in ["train", "val", "test"]) + assert all( + len(np.intersect1d(d.indices("reference", False), ids)) + for ids in d.pert_to_idx.values() + ) + + +def test_pca_fit_uses_only_training_cells(prepared): + _, d = prepared + train = d.indices("train") + mean = np.asarray(d.Xhvg[train].mean(0)).ravel() + np.testing.assert_allclose(d.pca_mean, mean, rtol=1e-5, atol=1e-5) + + +def test_test_expression_cannot_change_fitted_features(prepared, tmp_path): + import anndata as ad + + raw, d = prepared + a = ad.read_h5ad(raw) + held = d.obs.loc[d.obs.split.ne("train"), "cell_id"] + ids = a.obs_names.get_indexer(held) + x = a.X.tocsr() + x[ids] = x[ids] * 7 + a.X = x + changed = tmp_path / "changed.h5ad" + a.write_h5ad(changed) + out = tmp_path / "second" + prepare(str(changed), str(out), "norman", n_hvg=100, n_pca=5, seed=0) + e = PerturbData(str(out)) + assert e.genes == d.genes + np.testing.assert_allclose(e.pca_mean, d.pca_mean, atol=1e-6) + np.testing.assert_allclose(e.pca_components, d.pca_components, atol=1e-5) + + +def test_reference_outcomes_never_change_model_ranking(prepared): + from pivot.evaluation.runner import evaluate + + _, d = prepared + # The predictor accesses source populations and action labels only. + predict = lambda c, p: c + len(d.parse(p)) * 0.1 + root = Path(d.dir) + a = evaluate(d, predict, root / "a.json", catalog="all", n_cells=8) + ref = d.indices("reference", False) + saved = d.emb[ref].copy() + d.emb[ref] += 5 + try: + b = evaluate(d, predict, root / "b.json", catalog="all", n_cells=8) + finally: + d.emb[ref] = saved + assert [r["selected"] for r in a["inverse"]] == [ + r["selected"] for r in b["inverse"] + ] + assert [r["measured_reward"] for r in a["inverse"]] != [ + r["measured_reward"] for r in b["inverse"] + ]