ArticleProceedings of the National Academy of Sciences of the United States of America2020
A robust and interpretable end-to-end deep learning model for cytometry data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 74 citations in OpenAlex.
- dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data.Nature communications · 2026Article
- Deep learning representations of human Immune Health for precision immunology.bioRxiv : the preprint server for biology · 2026Article
- VoxelCoder: Classification of human cellular phenotypes via autoencoder batch alignment and hyperdimensional representation of cytometry data.Patterns (New York, N.Y.) · 2026Article
- Towards a Cytometry Foundation Model: Interpretable Sample-level Predictive Modelling via Pretrained Transformers.bioRxiv : the preprint server for biology · 2026Article
- Article
- Application and characterization of the multiple instance learning framework in flow cytometry.Scientific reports · 2026Article
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- CMV-specific clonal expansion of Th1, GZMKbioRxiv : the preprint server for biology · 2025Article
- cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.Patterns (New York, N.Y.) · 2025Article
- Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer.Nature communications · 2025Article
- Transformer-based artificial intelligence on single-cell clinical data for homeostatic mechanism inference and rational biomarker discovery.medRxiv : the preprint server for health sciences · 2025Article
- Automated cytometric gating with human-level performance using bivariate segmentation.Nature communications · 2025Article
- Machine Learning Methods in Clinical Flow Cytometry.Cancers · 2025Review
- Detection of acute myeloid leukemia and remission states using heterogeneous flow cytometry data.Frontiers in oncology · 2025Article
- Pan-cancer convergence of tumour-immune microenvironment motifs revealed by CyTOF and imaging mass cytometry.Frontiers in immunology · 2025Review
- Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper.Cell reports. Medicine · 2024Article
- AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.Scientific reports · 2024Article
- Restricting datasets to classifiable samples augments discovery of immune disease biomarkers.Nature communications · 2024Article
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Interpretable deep learning in single-cell omics.Bioinformatics (Oxford, England) · 2024Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing 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 cytometry by time-of-flight mass spectrometry or mass cytometry (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. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.