A biology-driven deep generative model for cell-type annotation in cytometry
This work addresses the challenge of time-consuming and batch-effect-sensitive cell-type annotation for researchers using cytometry, offering an automated solution that improves efficiency and accuracy.
The paper tackles the problem of manual cell-type annotation in cytometry, which lacks reproducibility and struggles with high-dimensional data, by introducing Scyan, a deep generative model that automatically annotates cell types using prior expert knowledge. The result shows that Scyan significantly outperforms state-of-the-art models on multiple public datasets while being faster and interpretable.
Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method suffers from a lack of reproducibility and sensitivity to batch-effect. Also, the most recent cytometers - spectral flow or mass cytometers - create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan (https://github.com/MICS-Lab/scyan), a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks such as batch-effect removal, debarcoding, and population discovery. Overall, this model accelerates and eases cell population characterisation, quantification, and discovery in cytometry.