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A robust and interpretable, end-to-end deep learning model for cytometry data

View ORCID ProfileZicheng Hu, Alice Tang, Jaiveer Singh, Sanchita Bhattacharya, Atul J. Butte
doi: https://doi.org/10.1101/2020.02.05.934521
Zicheng Hu
1Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA
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  • ORCID record for Zicheng Hu
  • For correspondence: Zicheng.Hu@ucsf.edu Atul.Butte@ucsf.edu
Alice Tang
1Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA
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Jaiveer Singh
1Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA
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Sanchita Bhattacharya
1Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA
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Atul J. Butte
1Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA
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  • For correspondence: Zicheng.Hu@ucsf.edu Atul.Butte@ucsf.edu
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Abstract

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Traditional approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large CyTOF studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model and identified a CD27-CD94+ CD8+ T cell population significantly associated with latent CMV infection. Finally, we provide a tutorial for creating, training and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (github.com/hzc363/DeepLearningCyTOF).

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  • https://github.com/hzc363/DeepLearningCyTOF

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 05, 2020.
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A robust and interpretable, end-to-end deep learning model for cytometry data
Zicheng Hu, Alice Tang, Jaiveer Singh, Sanchita Bhattacharya, Atul J. Butte
bioRxiv 2020.02.05.934521; doi: https://doi.org/10.1101/2020.02.05.934521
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A robust and interpretable, end-to-end deep learning model for cytometry data
Zicheng Hu, Alice Tang, Jaiveer Singh, Sanchita Bhattacharya, Atul J. Butte
bioRxiv 2020.02.05.934521; doi: https://doi.org/10.1101/2020.02.05.934521

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