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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 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
automated single-cell pathology

Getting started

See the full documentation and check out the tutorials.

An interactive vignette can be found as a Google colab notebook as part of the following repository.

https://github.com/camlab-bioml/Astir-Vignette

Authors

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

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