Skip to content

astir | Automated cell identity from single-cell multiplexed imaging and proteomics 🖥🔬✨

License

Notifications You must be signed in to change notification settings

camlab-bioml/astir

Repository files navigation

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

Build Status PyPI Code Style

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

Launch the interactive tutorial: in collab on github

See the full documentation and check out the tutorials.

Authors

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

About

astir | Automated cell identity from single-cell multiplexed imaging and proteomics 🖥🔬✨

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published