Journal Article

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

Jones, Joanne L., Suchanek, Ondrej, Mousa, Hani S., Coppard, Valerie, Mahbubani, Krishnaa T., Saeb-Parsy, Kourosh, Georgieva, Zoya, Szep, Grisha, Jarvis, Lorna B., Howlett, Sarah K., Feyertag, Felix, Rainbow, Daniel B., Needham, Edward J., Menon, David K.

2024

Overview

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 .

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Algorithms / Bioinformatics / Clustering / Computational Biology - methods / Computer applications / Cytometry / Data analysis / Datasets / dimensionality reduction / Flow cytometry / Flow Cytometry - methods / flow cytometry analysis / high-dimensional cytometry / Humans / Immunology / immunophenotyping / Immunophenotyping - methods / Julia programming language / Learning algorithms / Literacy / Machine Learning / Names / Programming languages / Software / spectral flow cytometry