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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Bibliographic Details
Published in:	Cell reports. Medicine Vol. 5; no. 11; p. 101808
Main Authors:	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
Language:	English
Published:	United States Elsevier Inc 19.11.2024 Elsevier
Subjects:	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 Access:	Get full text
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