=================================================================================== astir - Automated cell identity from single-cell multiplexed imaging and proteomics

|Build Status| |PyPI| |Code Style|

.. |Build Status| image:: https://travis-ci.com/camlab-bioml/astir.svg?branch=master :target: https://travis-ci.org/camlab-bioml/astir .. |Code Style| image:: https://img.shields.io/badge/code%20style-black-black :target: https://github.com/python/black .. |PyPI| image:: https://img.shields.io/badge/pypi-v2.1-orange :target: https://pypi.org/project/pypi/

astir is a modelling framework for the assignment of cell type across a range of single-cell technologies such as Imaging Mass Cytometry (IMC). astir is built using pytorch <https://pytorch.org/>_ and uses recognition networks for fast minibatch stochastic variational inference.

Key applications:

• Automated assignment of cell type and state from highly multiplexed imaging and proteomic data
• Diagnostic measures to check quality of resulting type and state inferences
• Ability to map new data to cell types and states trained on existing data using recognition neural networks
• A range of plotting and data loading utilities

.. image:: https://www.camlab.ca/img/astir.png :align: center :alt: automated single-cell pathology

Getting started

Launch the interactive tutorial: |in collab| |on github|

.. |in collab| image:: https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667 :target: https://colab.research.google.com/github/camlab-bioml/Astir-Vignette/blob/main/astir_tutorial.ipynb .. |on github| image:: https://img.shields.io/badge/on-github-black :target: https://github.com/camlab-bioml/Astir-Vignette

See the full documentation <https://astir.readthedocs.io/en/latest>_ and check out the tutorials <https://astir.readthedocs.io/en/latest/tutorials/index.html>_.

Authors

| Jinyu Hou, Sunyun Lee, Michael Geuenich, Kieran Campbell | Lunenfeld-Tanenbaum Research Institute & University of Toronto