FlowAtlas.jl: an interactive tool bridging FlowJo with computational tools in Julia
Preprint
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.jl: an interactive web application that bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community. We demonstrate the capabilities of FlowAtlas using a novel human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings.Keywords
All Related Versions
- Published version: Frontiers in Immunology, 15, 1425488.
This publication has 30 references indexed in Scilit:
- Merging and imputation of flow cytometry data: A critical assessmentCytometry Part A, 2023
- flowCut: An R package for automated removal of outlier events and flagging of files based on time versus fluorescence analysisCytometry Part A, 2022
- CytoNorm: A Normalization Algorithm for Cytometry DataCytometry Part A, 2019
- Helios+ and Helios− Treg subpopulations are phenotypically and functionally distinct and express dissimilar TCR repertoiresEuropean Journal of Immunology, 2019
- Human Tissue-Resident Memory T Cells Are Defined by Core Transcriptional and Functional Signatures in Lymphoid and Mucosal SitesCell Reports, 2017
- Practical NK cell phenotyping and variability in healthy adultsImmunologic Research, 2015
- FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry dataCytometry Part A, 2015
- Distribution and Compartmentalization of Human Circulating and Tissue-Resident Memory T Cell SubsetsImmunity, 2013
- Essentials of the self-organizing mapNeural Networks, 2013
- Per‐channel basis normalization methods for flow cytometry dataCytometry Part A, 2009