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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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| Filename, size astir-0.1.4-py3-none-any.whl (48.7 kB) | File type Wheel | Python version py3 | Upload date | Hashes View |
| Filename, size astir-0.1.4.tar.gz (33.0 kB) | File type Source | Python version None | Upload date | Hashes View |