Metadata-Version: 2.4
Name: perturb-hd
Version: 0.1.0
Summary: Evaluation metrics for perturb-seq model predictions
Author-email: Michael Bereket <mbereket@stanford.edu>
License: MIT
Project-URL: Homepage, https://github.com/snap-stanford/perturb-hd
Project-URL: Repository, https://github.com/snap-stanford/perturb-hd
Project-URL: Bug Tracker, https://github.com/snap-stanford/perturb-hd/issues
Keywords: perturbation,single-cell,evaluation,hit-discovery,perturb-seq
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.23.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: scipy>=1.10.0
Requires-Dist: tqdm>=4.66.2
Dynamic: license-file

## PerturbHD: Evaluation Metrics for AI-Enabled Hit Discovery

Evaluation functions to compute PerturbHD hit discovery metrics. See `tutorials/tutorial.ipynb` for usage.

### Installation

`pip install perturb-hd`


### Citation
```
@article{Bereket2026PerturbHD,
  author  = {Bereket, Michael D and Leskovec, Jure},
  title   = {Are Current AI Virtual Cell Models Useful for Scientific Discovery?},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.04.23.719015}
}
```

[Paper Link](https://www.biorxiv.org/content/10.64898/2026.04.23.719015v1)

[Code to reproduce experiments](https://github.com/snap-stanford/perturb-hd/tree/main)
