ArticleBioinformatics (Oxford, England)2019
CyTOFmerge: integrating mass cytometry data across multiple panels.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Restoration of NK Cell Cytotoxic Function With Elotuzumab and Daratumumab Promotes Elimination of Circulating Plasma Cells in Patients With SLE.Frontiers in immunology · 2021Trial
- CytoVI: deep generative modeling of antibody-based single cell data.Nature methods · 2026Article
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- A backbone-based flow cytometry approach to decipher regulatory T cell trajectories in the human thymus.Frontiers in immunology · 2025Article
- RUCova: Removal of Unwanted Covariance in mass cytometry data.Bioinformatics (Oxford, England) · 2024Article
- Review of flow cytometry as a tool for cell and gene therapy.Cytotherapy · 2024Review
- FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia.Frontiers in immunology · 2024Article
- Cytomulate: accurate and efficient simulation of CyTOF data.Genome biology · 2023Article
- Article
- Inferring direction of associations between histone modifications using a neural processes-based framework.iScience · 2023Article
- UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia.Cancers · 2022Article
- CyTOFFrontiers in immunology · 2022Review
- Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms.Patterns (New York, N.Y.) · 2021Article
- High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning.Science advances · 2021Article
- Article
- Unraveling the Complexity of the Cancer Microenvironment With Multidimensional Genomic and Cytometric Technologies.Frontiers in oncology · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationHigh-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel.
resultsTo extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection. AVAILABILITY AND IMPLEMENTATION: Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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