pertpy - Perturbation Analysis in Python
Pertpy is a scverse ecosystem framework for analyzing large-scale single-cell perturbation experiments. It provides tools for harmonizing perturbation datasets, automating metadata annotation, calculating perturbation distances, and efficiently analyzing how cells respond to various stimuli like genetic modifications, drug treatments, and environmental changes.
Please read our associated manuscript "Pertpy: an end-to-end framework for perturbation analysis".
Documentation
Please read the documentation for installation instructions, tutorials, use cases, and more.
Installation
We recommend installing and running pertpy on a recent version of Linux (e.g. Ubuntu 24.04 LTS). No particular hardware beyond a standard laptop is required.
You can install pertpy in less than a minute via pip from PyPI:
pip install pertpy
or conda-forge:
conda install -c conda-forge pertpy
JAX backed tools
pip install 'pertpy[jax]'
Required by scCODA, CINEMA-OT, scGen, MLPClassifierSpace, the "wasserstein" distance metric and the mixture model of GuideAssignment.
tascCODA pulls it in via pertpy[tcoda].
Everything else works without it.
Differential gene expression
If you want to use the differential gene expression interface, please install pertpy by running:
pip install 'pertpy[de]'
tascCODA
if you want to use tascCODA, please install pertpy as follows:
pip install 'pertpy[tcoda]'
milo
milo requires either the "de" extra for the "pydeseq2" solver:
pip install 'pertpy[de]'
or, edger, statmod, and rpy2 for the "edger" solver:
BiocManager::install("edgeR")
BiocManager::install("statmod")
pip install rpy2
Citation
@article{Heumos2025,
author = {Heumos, Lukas and Ji, Yuge and May, Lilly and Green, Tessa D. and Peidli, Stefan and Zhang, Xinyue and Wu, Xichen and Ostner, Johannes and Schumacher, Antonia and Hrovatin, Karin and Müller, Michaela and Chong, Faye and Sturm, Gregor and Tejada, Alejandro and Dann, Emma and Dong, Mingze and Pinto, Gonçalo and Bahrami, Mojtaba and Gold, Ilan and Rybakov, Sergei and Namsaraeva, Altana and Moinfar, Amir Ali and Zheng, Zihe and Roellin, Eljas and Mekki, Isra and Sander, Chris and Lotfollahi, Mohammad and Schiller, Herbert B. and Theis, Fabian J.},
title = {Pertpy: an end-to-end framework for perturbation analysis},
journal = {Nature Methods},
year = {2025},
publisher = {Springer Nature},
doi = {10.1038/s41592-025-02909-7},
url = {https://doi.org/10.1038/s41592-025-02909-7},
issn = {1548-7105}
}
pertpy is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. If you like scverse® and want to support our mission, please consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.
Release files for pertpy 1.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pertpy-1.3.0.tar.gz | 4.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pertpy-1.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.8 MB
Release files / pertpy-1.3.0.tar.gz
| Download URL | pertpy-1.3.0.tar.gz |
|---|---|
| Size | 4.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
e27ce2fd0b63e40554d09b57a6cf3751eaf41f0db49f9062767f94cecffc735c
|
|
BLAKE2b-256 checksum How to use checksums |
af301a5effa59e2860cd4e59d20ff7b6aa25ee73bc7ae999680229d8b729c734
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2026.
Transparency logRelease files / pertpy-1.3.0-py3-none-any.whl
| Download URL | pertpy-1.3.0-py3-none-any.whl |
|---|---|
| Size | 282.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ea9d391dac4049feae0e78b0c5b968d60d21dd1d4d8c1329994d5f8f77126498
|
|
BLAKE2b-256 checksum How to use checksums |
87f35ec54dc0fcbd6ff9231988fc33b9f02c038a3177e770dbaeb8a31a6a4ef6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2026.
Transparency log
