🧬 Single-cell Cytometry Annotation Network

Based on biological knowledge prior, Scyan provides a fast cell population annotation without requiring any training label. It is an interpretable model that also corrects batch-effect and can be used for debarcoding, cell sampling, and population discovery.

The complete documentation can be found here. It contains installation guidelines, tutorials, a description of the API, etc.

Scyan is a Bayesian probabilistic model composed of a deep invertible neural network called a normalizing flow (the function $f_{\phi}$). It maps a latent distribution of cell expressions into the empirical distribution of cell expressions. This cell distribution is a mixture of gaussian-like distributions representing the sum of a cell-specific and a population-specific term. Also, interpretability and batch effect correction are based on the model latent space — more details in the article's Methods section.

Scyan can be installed on every OS with pip for python>=3.11:

pip install scyan

import scyan

adata, table = scyan.data.load("aml") # Automatic loading

model = scyan.Scyan(adata, table)
model.fit()
model.predict()

This code should run in approximately 40 seconds (once the dataset is loaded). For more usage demo, read the tutorials or the complete documentation.

Our paper is published in Briefings in Bioinformatics and is available here.

@article{10.1093/bib/bbad260,
    author = {Blampey, Quentin and Bercovici, Nadège and Dutertre, Charles-Antoine and Pic, Isabelle and Ribeiro, Joana Mourato and André, Fabrice and Cournède, Paul-Henry},
    title = "{A biology-driven deep generative model for cell-type annotation in cytometry}",
    journal = {Briefings in Bioinformatics},
    pages = {bbad260},
    year = {2023},
    month = {07},
    issn = {1477-4054},
    doi = {10.1093/bib/bbad260},
    url = {https://doi.org/10.1093/bib/bbad260},
    eprint = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbad260/50973199/bbad260.pdf},
}