  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.
