This package provides efficient and scalable handling of flow and mass cytometry data analysis. It provides

• the functionality to read in flow data in the fcs file format as anndata objects
• flow and mass cytometry specific preprocessing tools
• access to the entire scanpy workflow functionality
• GPU support through rapids-singlecell

Please refer to the documentation. In particular, the

• API documentation.

You need to have Python 3.12 or newer installed on your system. If you don't have Python installed, we recommend installing Mambaforge.

There are several alternative options to install pytometry:

1. Install the latest release of pytometry from PyPI.

pip install pytometry

FlowSOM clustering uses PyTorch. On Linux, pip installs PyTorch with its CUDA libraries, which take several GB. If you don't need a GPU, install the CPU-only PyTorch first:

pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install pytometry
# or, with uv
uv pip install pytometry --torch-backend cpu

In a uv project, see uv's PyTorch guide.

2. Install the latest development version:

pip install git+https://github.com/scverse/pytometry.git@main

3. Install locally for development:

Please refer to the Contributing Guide.

See GitHub releases.

For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.

Pytometry is currently a pre-print on bioRxiv.

pytometry 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.