Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper

• 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

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...