astir 0.1.4

No project description provided

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

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