Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Gating mass cytometry data by deep learning.

Huamin Li1, Uri Shaham2, Kelly P Stanton3

  • 1Applied Mathematics Program.

Bioinformatics (Oxford, England)
|October 17, 2017
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Differential cortical responses of robot-assisted active and mirror therapy task conditions in stroke patients and healthy controls: a comparative fNIRS study.

Frontiers in neurology·2026
Same author

Half a century of pneumococcal vaccination in sickle cell disease: Are we winning the battle or losing immunity?

Vaccine·2026
Same author

Adaptive Immune Remodeling in Sickle Cell Disease: Linking Hemolysis-Driven Inflammation to Immune Dysfunction.

Experimental hematology·2026
Same author

Distribution and antimicrobial resistance of pathogens in oral and maxillofacial infections during and Post-COVID-19 pandemic.

Frontiers in public health·2026
Same author

Revealing the microbial diversity and functional annotation during postharvest storage of sweet cherry using metagenomics.

Food research international (Ottawa, Ont.)·2026
Same author

Modulation of cerebral activation strategies by training mode in stratified stroke cohorts: an fNIRS study.

Frontiers in human neuroscience·2026
Same journal

Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.

Bioinformatics (Oxford, England)·2026
Same journal

Quantifying domain-specific relevance of computational biology Wikipedia articles using TF-IDF and cosine similarity.

Bioinformatics (Oxford, England)·2026
Same journal

GATSBI: improving context-aware protein embeddings through biologically motivated data splits.

Bioinformatics (Oxford, England)·2026
Same journal

BiMba: using Vision Mamba to predict protein sites that bind other proteins.

Bioinformatics (Oxford, England)·2026
Same journal

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

Bioinformatics (Oxford, England)·2026
Same journal

Is a Win-Win possible? Achieving pareto-optimal privacy-utility balance in fine-tuned genome language model embeddings against embedding reconstruction attacks.

Bioinformatics (Oxford, England)·2026
See all related articles

DeepCyTOF uses deep learning for automated cell gating in mass cytometry (CyTOF) data. This method achieves high accuracy, matching manual gating and enabling efficient analysis of large immune cell datasets.

Area of Science:

  • Immunology
  • Computational Biology
  • Biotechnology

Background:

  • Mass cytometry (CyTOF) enables high-dimensional single-cell analysis, surpassing traditional flow cytometry.
  • Analyzing large-scale CyTOF datasets presents challenges in automated cell identification (gating).
  • Manual gating is time-consuming and not scalable for extensive studies.

Purpose of the Study:

  • To introduce DeepCyTOF, a deep learning-based approach for automating cell gating in CyTOF data.
  • To develop a standardization method for high-dimensional single-cell analysis.
  • To improve the efficiency and scalability of CyTOF data interpretation.

Main Methods:

  • DeepCyTOF employs deep learning and domain adaptation principles for unsupervised calibration between data distributions.

Related Experiment Videos

  • The method requires labeled cells from a single sample for training.
  • It generalizes previous computational approaches for data standardization.

    • Main Results:

    • DeepCyTOF demonstrated 98% concordance with manual gating across 16 biological replicates of immune cells, even with inter-instrument variability.
    • Achieved high accuracy in the FlowCAP-I semi-automated gating challenge.
    • Validated on CyTOF datasets from West Nile virus-infected subjects and healthy individuals of various ages.

    Conclusions:

    • Deep learning, specifically DeepCyTOF, provides a powerful computational tool for semi-automated gating of CyTOF and flow cytometry data.
    • The approach enhances the analysis of complex single-cell datasets.
    • DeepCyTOF offers a scalable solution for high-dimensional immune cell profiling.