A biology-driven deep generative model for cell-type annotation in cytometry

• URL: http://arxiv.org/abs/2208.05745v2
• Date: Fri, 21 Apr 2023 15:52:06 GMT
• Title: A biology-driven deep generative model for cell-type annotation in cytometry
• Authors: Quentin Blampey, Nad\`ege Bercovici, Charles-Antoine Dutertre, Isabelle Pic, Fabrice Andr\'e, Joana Mourato Ribeiro, and Paul-Henry Courn\`ede
• Abstract summary: We introduce Scyan, a Single-cell Cytometry Network that automatically annotates cell types using only prior expert knowledge. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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