Scyan documentation

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

Overview

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.

Technical description

Scyan is a Python library based on:

• AnnData, a data library that works nicely with single-cell data
• Pytorch, a deep learning framework
• Pytorch Lightning, for model training

Optionally, it also supports:

• Hydra, for project configuration
• Weight & Biases, for model monitoring