Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper

Single-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional methods for annotating cytometry data face challenges in scalability, robustness, and accuracy. We propose a cytometry masked autoencoder (cyMAE), which...

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Veröffentlicht in:	Cell reports. Medicine Jg. 5; H. 11; S. 101808
Hauptverfasser:	Kim, Jaesik, Ionita, Matei, Lee, Matthew, McKeague, Michelle L., Pattekar, Ajinkya, Painter, Mark M., Wagenaar, Joost, Truong, Van, Norton, Dylan T., Mathew, Divij, Nam, Yonghyun, Apostolidis, Sokratis A., Clendenin, Cynthia, Orzechowski, Patryk, Jung, Sang-Hyuk, Woerner, Jakob, Ittner, Caroline A.G., Turner, Alexandra P., Esperanza, Mika, Dunn, Thomas G., Mangalmurti, Nilam S., Reilly, John P., Meyer, Nuala J., Calfee, Carolyn S., Liu, Kathleen D., Matthy, Michael A., Swigart, Lamorna Brown, Burnham, Ellen L., McKeehan, Jeffrey, Gandotra, Sheetal, Russel, Derek W., Gibbs, Kevin W., Thomas, Karl W., Barot, Harsh, Greenplate, Allison R., Wherry, E. John, Kim, Dokyoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 19.11.2024 Elsevier
Schlagworte:	Advanced Basic Science Algorithms automated gating deep learning Flow Cytometry - methods high-dimensional cytometry Humans immunophenotyping Immunophenotyping - methods machine learning mass cytometry representation learning Single-Cell Analysis - methods deep learning automated gating representation learning immunophenotyping high-dimensional cytometry mass cytometry machine learning
ISSN:	2666-3791, 2666-3791
Online-Zugang:	Volltext
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