Evidence map›Paper›PMID 39086484›Full record

ArticleFrontiers in immunology2024

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

Valerie Coppard, Grisha Szep, Zoya Georgieva, Sarah K Howlett, Lorna B Jarvis, Daniel B Rainbow, Ondrej Suchanek, Edward J Needham, Hani S Mousa, David K Menon and 4 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Valerie CoppardDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Grisha SzepRandall Centre for Cell & Molecular Biophysics, King's College London, London, United Kingdom.
Zoya GeorgievaDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Sarah K HowlettDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Lorna B JarvisDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Daniel B RainbowDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Ondrej SuchanekDepartment of Medicine, University of Cambridge, Cambridge, United Kingdom.
Edward J NeedhamDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
Hani S MousaDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
David K MenonDepartment of Anaesthesia, University of Cambridge, Cambridge, United Kingdom.
Felix FeyertagIndependent Researcher, Oxford, United Kingdom.
Krishnaa T MahbubaniDepartment of Surgery, University of Cambridge, Cambridge, United Kingdom.
Kourosh Saeb-ParsyDepartment of Surgery, University of Cambridge, Cambridge, United Kingdom.
Joanne L JonesDepartment of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Computational BiologyFlow CytometryImmunophenotypingSoftwareHumansMachine Learningdimensionality reductionflow cytometry analysishigh-dimensional cytometryimmunophenotypingJulia programming languagespectral flow cytometry

Identifiers

PMID39086484
PMCPMC11288863

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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.