Scirpy: A Scanpy extension for analyzing single-cell immune-cell receptor sequencing data

Build Status Documentation Status PyPI Bioconda AIRR-compliant The uncompromising python formatter

Scirpy is a scalable python-toolkit to analyse T cell receptor (TCR) or B cell receptor (BCR) repertoires from single-cell RNA sequencing (scRNA-seq) data. It seamlessly integrates with the popular scanpy library and provides various modules for data import, analysis and visualization.

The scirpy workflow

Getting started

Please refer to the documentation. In particular, the

In the documentation, you can also learn more about our immune-cell receptor model.


The case study from our paper is available here.


You need to have Python 3.8 or newer installed on your system. If you don’t have Python installed, we recommend installing Miniconda.

There are several alternative options to install scirpy:

  1. Install the latest release of scirpy from PyPI:

pip install scirpy
  1. Get it from Bioconda:

conda install -c conda-forge -c bioconda scirpy
  1. Install the latest development version:

pip install git+
  1. Run it in a container using Docker or Podman:

docker pull<tag>

where tag is one of these tags.


We are happy to assist with problems when using scirpy.

  • If you need help with scirpy or have questions regarding single-cell immune-cell receptor analysis in general, please join us in the scverse discourse.

  • For bug report or feature requests, please use the issue tracker.

We try to respond within two working days, however fixing bugs or implementing new features can take substantially longer, depending on the availability of our developers.

Release notes

See the release section.


Please use the issue tracker.


If you use scirpy in your work, please cite the scirpy publication as follows:

Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

Gregor Sturm, Tamas Szabo, Georgios Fotakis, Marlene Haider, Dietmar Rieder, Zlatko Trajanoski, Francesca Finotello

Bioinformatics 2020 Sep 15. doi: 10.1093/bioinformatics/btaa611.

You can cite the scverse publication as follows:

The scverse project provides a computational ecosystem for single-cell omics data analysis

Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Scverse Community, Bonnie Berger, Dana Pe’er, Aviv Regev, Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle & Fabian J. Theis

Nat Biotechnol. 2022 Apr 10. doi: 10.1038/s41587-023-01733-8.