FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in Julia Article Swipe
Valerie Coppard
,
Gregory Szép
,
Zoya Georgieva
,
Sarah Howlett
,
Lorna B. Jarvis
,
Daniel B. Rainbow
,
Ondřej Suchánek
,
Edward Needham
,
Hani S. Mousa
,
David Menon
,
Felix Feyertag
,
Krishnaa T. Mahbubani
,
Kourosh Saeb‐Parsy
,
Joanne Jones
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2024.1425488
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2024.1425488
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 .
Related Topics
Concepts
Computer science
Dimensionality reduction
Leverage (statistics)
Bridging (networking)
User Friendly
Population
Curse of dimensionality
Machine learning
Cytometry
Mass cytometry
Artificial intelligence
Data science
Cell
Biology
Sociology
Phenotype
Genetics
Biochemistry
Gene
Operating system
Demography
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2024.1425488
- https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1425488/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400746289
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400746289Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fimmu.2024.1425488Digital Object Identifier
- Title
-
FlowAtlas: an interactive tool for high-dimensional immunophenotyping analysis bridging FlowJo with computational tools in JuliaWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-17Full publication date if available
- Authors
-
Valerie Coppard, Gregory Szép, Zoya Georgieva, Sarah Howlett, Lorna B. Jarvis, Daniel B. Rainbow, Ondřej Suchánek, Edward Needham, Hani S. Mousa, David Menon, Felix Feyertag, Krishnaa T. Mahbubani, Kourosh Saeb‐Parsy, Joanne JonesList of authors in order
- Landing page
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https://doi.org/10.3389/fimmu.2024.1425488Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1425488/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1425488/pdfDirect OA link when available
- Concepts
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Computer science, Dimensionality reduction, Leverage (statistics), Bridging (networking), User Friendly, Population, Curse of dimensionality, Machine learning, Cytometry, Mass cytometry, Artificial intelligence, Data science, Cell, Biology, Sociology, Phenotype, Genetics, Biochemistry, Gene, Operating system, Demography, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 14, 74 |
| abstract_inverted_index.key | 108 |
| abstract_inverted_index.the | 1, 12, 54, 66, 72, 95 |
| abstract_inverted_index.web | 31 |
| abstract_inverted_index.Here | 25 |
| abstract_inverted_index.cell | 105 |
| abstract_inverted_index.data | 8, 39 |
| abstract_inverted_index.does | 11 |
| abstract_inverted_index.need | 73 |
| abstract_inverted_index.rare | 85 |
| abstract_inverted_index.that | 19, 33, 43 |
| abstract_inverted_index.with | 46, 49 |
| abstract_inverted_index.Julia | 63 |
| abstract_inverted_index.human | 101 |
| abstract_inverted_index.tools | 18, 61 |
| abstract_inverted_index.using | 99 |
| abstract_inverted_index.FlowJo | 58 |
| abstract_inverted_index.coding | 75 |
| abstract_inverted_index.demand | 13 |
| abstract_inverted_index.immune | 104 |
| abstract_inverted_index.rapid. | 92 |
| abstract_inverted_index.bridges | 53 |
| abstract_inverted_index.enables | 34 |
| abstract_inverted_index.machine | 22, 68 |
| abstract_inverted_index.panels. | 51 |
| abstract_inverted_index.stained | 48 |
| abstract_inverted_index.without | 40 |
| abstract_inverted_index.analysis | 17 |
| abstract_inverted_index.dataset, | 106 |
| abstract_inverted_index.datasets | 47 |
| abstract_inverted_index.learning | 23, 69 |
| abstract_inverted_index.leverage | 20 |
| abstract_inverted_index.FlowAtlas | 52, 88, 98, 111 |
| abstract_inverted_index.available | 113 |
| abstract_inverted_index.cytometry | 7, 38 |
| abstract_inverted_index.detection | 83 |
| abstract_inverted_index.developed | 64 |
| abstract_inverted_index.discovery | 81 |
| abstract_inverted_index.findings. | 110 |
| abstract_inverted_index.introduce | 27 |
| abstract_inverted_index.intuitive | 90 |
| abstract_inverted_index.reduction | 36 |
| abstract_inverted_index.FlowAtlas: | 28 |
| abstract_inverted_index.community, | 70 |
| abstract_inverted_index.compatible | 45 |
| abstract_inverted_index.complexity | 5 |
| abstract_inverted_index.expertise. | 78 |
| abstract_inverted_index.increases, | 9 |
| abstract_inverted_index.population | 80 |
| abstract_inverted_index.scientific | 67 |
| abstract_inverted_index.throughput | 3 |
| abstract_inverted_index.application | 32 |
| abstract_inverted_index.demonstrate | 94 |
| abstract_inverted_index.eliminating | 71 |
| abstract_inverted_index.environment | 56 |
| abstract_inverted_index.frameworks. | 24 |
| abstract_inverted_index.interactive | 16, 30 |
| abstract_inverted_index.multi-donor | 103 |
| abstract_inverted_index.populations | 86 |
| abstract_inverted_index.capabilities | 96 |
| abstract_inverted_index.highlighting | 107 |
| abstract_inverted_index.computational | 60 |
| abstract_inverted_index.down-sampling | 41 |
| abstract_inverted_index.immunological | 109 |
| abstract_inverted_index.multi-tissue, | 102 |
| abstract_inverted_index.non-identical | 50 |
| abstract_inverted_index.user-friendly | 55 |
| abstract_inverted_index.bioinformatics | 77 |
| abstract_inverted_index.dimensionality | 35 |
| abstract_inverted_index.user-friendly, | 15 |
| abstract_inverted_index.dimensionality, | 2 |
| abstract_inverted_index.high-performance | 21 |
| abstract_inverted_index.https://github.com/gszep/FlowAtlas.jl.git | 115 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5079444934, https://openalex.org/A5067880345 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 14 |
| corresponding_institution_ids | https://openalex.org/I241749 |
| citation_normalized_percentile.value | 0.59287177 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |