FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia.

Autor:	Coppard V; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom., Szep G; Randall Centre for Cell & Molecular Biophysics, King's College London, London, United Kingdom., Georgieva Z; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom., Howlett SK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom., Jarvis LB; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom., Rainbow DB; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom., Suchanek O; Department of Medicine, University of Cambridge, Cambridge, United Kingdom., Needham EJ; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom., Mousa HS; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom., Menon DK; Department of Anaesthesia, University of Cambridge, Cambridge, United Kingdom., Feyertag F; Independent Researcher, Oxford, United Kingdom., Mahbubani KT; Department of Surgery, University of Cambridge, Cambridge, United Kingdom.; Collaborative Biorepository for Translational Medicine (CBTM), Cambridge NIHR Biomedical Research Centre, Cambridge, United Kingdom., Saeb-Parsy K; Department of Surgery, University of Cambridge, Cambridge, United Kingdom.; Collaborative Biorepository for Translational Medicine (CBTM), Cambridge NIHR Biomedical Research Centre, Cambridge, United Kingdom., Jones JL; Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in immunology [Front Immunol] 2024 Jul 17; Vol. 15, pp. 1425488. Date of Electronic Publication: 2024 Jul 17 (Print Publication: 2024).
DOI:	10.3389/fimmu.2024.1425488
Abstrakt:	As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git. Competing Interests: JJ reports receiving consulting fees and grant support from Sanofi Genzyme and Enhanc3DGenomics, and consulting fees from Roche. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funders did not play a part in study design, data collection, decision to publish, or preparation of the manuscript. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. (Copyright © 2024 Coppard, Szep, Georgieva, Howlett, Jarvis, Rainbow, Suchanek, Needham, Mousa, Menon, Feyertag, Mahbubani, Saeb-Parsy and Jones.)
Databáze:	MEDLINE
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