تقرير
A biology-driven deep generative model for cell-type annotation in cytometry
| العنوان: | A biology-driven deep generative model for cell-type annotation in cytometry |
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| المؤلفون: | Blampey, Quentin, Bercovici, Nadège, Dutertre, Charles-Antoine, Pic, Isabelle, André, Fabrice, Ribeiro, Joana Mourato, Cournède, Paul-Henry |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1093/bib/bbad260 |
| URL الوصول: | http://arxiv.org/abs/2208.05745 |
| رقم الأكسشن: | edsarx.2208.05745 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1093/bib/bbad260 |
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