ArticleBioinformatics (Oxford, England)2021
Agile workflow for interactive analysis of mass cytometry data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Ex Vivo Immuno-Oncology Platform Reveals Spatial T-cell Infiltration Patterns Linked to ATR Inhibition Responses in High-Grade Serous Ovarian Cancer.Cancer immunology research · 2026Article
- Chemotherapy induces myeloid-driven spatially confined T cell exhaustion in ovarian cancer.Cancer cell · 2024Article
- Article
- Computational Methods for Single-Cell Proteomics.Annual review of biomedical data science · 2023Review
- ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Single-cell tumor-immune microenvironment of BRCA1/2 mutated high-grade serous ovarian cancer.Nature communications · 2022Article
- Quantitative proteomics characterization of cancer biomarkers and treatment.Molecular therapy oncolytics · 2021Review
- Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationSingle-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge.
resultsWe present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AVAILABILITYAND IMPLEMENTATION: The method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.