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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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
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Abstract	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 automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies. [Display omitted] •Masked cytometry modeling learns relationships among proteins without cell identity•We develop cytometry masked autoencoder (cyMAE) to automate immunophenotyping•cyMAE improves both cell-level and subject-level immune profiling Kim et al. introduces cyMAE, a cytometry masked autoencoder that automates immune cell profiling from single-cell cytometry data. By leveraging unlabeled data for pre-training and fine-tuning on specific tasks, the model improves immune profiling accuracy and enhances prediction of subject-level clinical data, advancing large-scale immune studies.
AbstractList	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 automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies. •Masked cytometry modeling learns relationships among proteins without cell identity•We develop cytometry masked autoencoder (cyMAE) to automate immunophenotyping•cyMAE improves both cell-level and subject-level immune profiling Kim et al. introduces cyMAE, a cytometry masked autoencoder that automates immune cell profiling from single-cell cytometry data. By leveraging unlabeled data for pre-training and fine-tuning on specific tasks, the model improves immune profiling accuracy and enhances prediction of subject-level clinical data, advancing large-scale immune studies. 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 automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies. 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 automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies.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 automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies. SummarySingle-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 automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies. 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 automates immunophenotyping tasks including cell type annotation. The model upholds user-defined cell type definitions, facilitating interpretability and cross-study comparisons. The training of cyMAE has a self-supervised phase, which leverages large amounts of unlabeled data, followed by fine-tuning on specialized tasks using smaller amounts of annotated data. The cost of training a new model is amortized over repeated inferences on new datasets using the same panel. Through validation across multiple studies using the same panel, we demonstrate that cyMAE delivers accurate and interpretable cellular immunophenotyping and improves the prediction of subject-level metadata. This proof of concept marks a significant step forward for large-scale immunology studies. [Display omitted] •Masked cytometry modeling learns relationships among proteins without cell identity•We develop cytometry masked autoencoder (cyMAE) to automate immunophenotyping•cyMAE improves both cell-level and subject-level immune profiling Kim et al. introduces cyMAE, a cytometry masked autoencoder that automates immune cell profiling from single-cell cytometry data. By leveraging unlabeled data for pre-training and fine-tuning on specific tasks, the model improves immune profiling accuracy and enhances prediction of subject-level clinical data, advancing large-scale immune studies.
ArticleNumber	101808
Author	Kim, Jaesik Swigart, Lamorna Brown Wagenaar, Joost Apostolidis, Sokratis A. Painter, Mark M. Thomas, Karl W. Jung, Sang-Hyuk Norton, Dylan T. Matthy, Michael A. Wherry, E. John McKeehan, Jeffrey Ittner, Caroline A.G. Russel, Derek W. Turner, Alexandra P. Woerner, Jakob Liu, Kathleen D. Esperanza, Mika Burnham, Ellen L. Orzechowski, Patryk Mangalmurti, Nilam S. Kim, Dokyoon Pattekar, Ajinkya Reilly, John P. Truong, Van Clendenin, Cynthia Dunn, Thomas G. Barot, Harsh Nam, Yonghyun Gibbs, Kevin W. Meyer, Nuala J. Ionita, Matei Calfee, Carolyn S. Gandotra, Sheetal Greenplate, Allison R. McKeague, Michelle L. Mathew, Divij Lee, Matthew
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Keywords	deep learning automated gating representation learning immunophenotyping high-dimensional cytometry mass cytometry machine learning
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Snippet	Single-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional methods for... SummarySingle-cell cytometry data are crucial for understanding the role of the immune system in diseases and responses to treatment. However, traditional...
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SubjectTerms	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
Title	Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper
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