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

Front Immunol. 2024 Jul 17:15:1425488. doi: 10.3389/fimmu.2024.1425488. eCollection 2024.

Abstract

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.

Keywords: Julia programming language; dimensionality reduction; flow cytometry analysis; high-dimensional cytometry; immunophenotyping; spectral flow cytometry.

MeSH terms

  • Computational Biology* / methods
  • Flow Cytometry* / methods
  • Humans
  • Immunophenotyping* / methods
  • Machine Learning
  • Software*

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded in part by the Wellcome Trust (Grant number 105924/Z/14/Z; RG79413 to JJ). This work was also supported by the NIHR Cambridge Biomedical Research Centre (BRC121520014), and by the Cambridge NIHR BRC Cell Phenotyping Hub. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GS was supported by Microsoft Research and by the EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES, EP/L015854/1). ZG was supported by the Wellcome Trust (Grant number 220554/Z/20/Z). HM was supported by The Rosetrees Trust (RG82826, JS16/M589). For the purpose of open access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.