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Project description
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.
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