FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows
An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increa...
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| Vydané v: | Frontiers in immunology Ročník 12; s. 768541 |
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| Hlavní autori: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Switzerland
Frontiers Media S.A
05.11.2021
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| Abstract | An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increasing data complexity, but by and large, still conduct all analysis using traditional applications, predominantly FlowJo. To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts. |
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| AbstractList | An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increasing data complexity, but by and large, still conduct all analysis using traditional applications, predominantly FlowJo. To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts. An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increasing data complexity, but by and large, still conduct all analysis using traditional applications, predominantly FlowJo. To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts.An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increasing data complexity, but by and large, still conduct all analysis using traditional applications, predominantly FlowJo. To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts. |
| Author | Enzor, Jennifer Mosier, Sarah M. Ferrari, Guido Weinhold, Kent J. Denny, Thomas N. Almarode, James Quinn, John Staats, Janet Chan, Cliburn White, Scott |
| AuthorAffiliation | 6 Department of Surgery, Duke University Medical Center , Durham, NC , United States 5 Duke Immune Profiling Core, Duke University School of Medicine , Durham, NC , United States 1 Duke Center for AIDS Research, Duke University , Durham, NC , United States 2 Department of Biostatistics and Bioinformatics, Duke University Medical Center , Durham, NC , United States 3 Center for Human Systems Immunology, Duke University Medical Center , Durham, NC , United States 4 BD Life Sciences - FlowJo , Ashland, OR , United States 7 Duke Human Vaccine Institute , Durham, NC , United States |
| AuthorAffiliation_xml | – name: 3 Center for Human Systems Immunology, Duke University Medical Center , Durham, NC , United States – name: 4 BD Life Sciences - FlowJo , Ashland, OR , United States – name: 6 Department of Surgery, Duke University Medical Center , Durham, NC , United States – name: 5 Duke Immune Profiling Core, Duke University School of Medicine , Durham, NC , United States – name: 1 Duke Center for AIDS Research, Duke University , Durham, NC , United States – name: 2 Department of Biostatistics and Bioinformatics, Duke University Medical Center , Durham, NC , United States – name: 7 Duke Human Vaccine Institute , Durham, NC , United States |
| Author_xml | – sequence: 1 givenname: Scott surname: White fullname: White, Scott – sequence: 2 givenname: John surname: Quinn fullname: Quinn, John – sequence: 3 givenname: Jennifer surname: Enzor fullname: Enzor, Jennifer – sequence: 4 givenname: Janet surname: Staats fullname: Staats, Janet – sequence: 5 givenname: Sarah M. surname: Mosier fullname: Mosier, Sarah M. – sequence: 6 givenname: James surname: Almarode fullname: Almarode, James – sequence: 7 givenname: Thomas N. surname: Denny fullname: Denny, Thomas N. – sequence: 8 givenname: Kent J. surname: Weinhold fullname: Weinhold, Kent J. – sequence: 9 givenname: Guido surname: Ferrari fullname: Ferrari, Guido – sequence: 10 givenname: Cliburn surname: Chan fullname: Chan, Cliburn |
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| Cites_doi | 10.1088/1742-5468/2008/10/P10008 10.1371/journal.pcbi.1009071 10.1038/sdata.2018.15 10.1038/s41592-018-0308-4 10.1002/cyto.a.22690 10.1038/s41598-019-41695-z 10.1186/s13059-017-1382-0 10.1016/j.jim.2014.05.021 10.1021/acssynbio.5b00284 10.1186/1471-2105-10-106 10.1007/s12026-014-8516-1 10.1038/nbt.4314 |
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| Copyright | Copyright © 2021 White, Quinn, Enzor, Staats, Mosier, Almarode, Denny, Weinhold, Ferrari and Chan. Copyright © 2021 White, Quinn, Enzor, Staats, Mosier, Almarode, Denny, Weinhold, Ferrari and Chan 2021 White, Quinn, Enzor, Staats, Mosier, Almarode, Denny, Weinhold, Ferrari and Chan |
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| Keywords | software python (programming language) single cell data science FlowJo GatingML flow cytometry systems immunology |
| Language | English |
| License | Copyright © 2021 White, Quinn, Enzor, Staats, Mosier, Almarode, Denny, Weinhold, Ferrari and Chan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| References | Linderman (B5) 2019; 16 Blondel (B9) 2008; 2008 B12 Castillo-Hair (B14) 2016; 5 B15 Wang (B7) 2020; 22 Traag (B8) 2019; 9 Finak (B4) 2021 Spidlen (B1) 2015; 87 Becht (B6) 2018; 37 Bhattacharya (B11) 2014; 58 Staats (B2) 2014; 409 Wolf (B16) 2018; 19 Bhattacharya (B10) 2018; 5 Hahne (B3) 2009; 10 Burton (B13) 2021; 17 |
| References_xml | – volume: 2008 start-page: P10008 year: 2008 ident: B9 article-title: Fast Unfolding of Communities in Large Networks publication-title: J Stat Mech: Theory Experiment doi: 10.1088/1742-5468/2008/10/P10008 – ident: B12 – volume: 17 start-page: e1009071 year: 2021 ident: B13 article-title: CytoPy: An Autonomous Cytometry Analysis Framework publication-title: PloS Comput Biol doi: 10.1371/journal.pcbi.1009071 – volume: 5 start-page: 180015 year: 2018 ident: B10 article-title: ImmPort, Toward Repurposing of Open Access Immunological Assay Data for Translational and Clinical Research publication-title: Sci Data doi: 10.1038/sdata.2018.15 – volume: 16 year: 2019 ident: B5 article-title: Fast Interpolation-Based T-SNE for Improved Visualization of Single-Cell RNA-Seq Data publication-title: Nat Methods doi: 10.1038/s41592-018-0308-4 – volume: 87 year: 2015 ident: B1 article-title: Isac’s Gating-ML 2.0 Data Exchange Standard for Gating Description publication-title: Cytometry A doi: 10.1002/cyto.a.22690 – volume: 9 start-page: 1 year: 2019 ident: B8 article-title: From Louvain to Leiden: Guaranteeing Well-Connected Communities publication-title: Sci Rep doi: 10.1038/s41598-019-41695-z – volume: 19 start-page: 15 year: 2018 ident: B16 article-title: SCANPY: Large-Scale Single-Cell Gene Expression Data Analysis publication-title: Genome Biol doi: 10.1186/s13059-017-1382-0 – volume-title: R Package Version 4.4.0 year: 2021 ident: B4 article-title: Flowworkspace: Infrastructure for Representing and Interacting With Gated and Ungated Cytometry Data Sets – volume: 409 start-page: 44 year: 2014 ident: B2 article-title: Toward Development of a Comprehensive External Quality Assurance Program for Polyfunctional Intracellular Cytokine Staining Assays publication-title: J Immunol Methods doi: 10.1016/j.jim.2014.05.021 – volume: 5 year: 2016 ident: B14 article-title: FlowCal: A User-Friendly, Open Source Software Tool for Automatically Converting Flow Cytometry Data From Arbitrary to Calibrated Units publication-title: ACS Synth Biol doi: 10.1021/acssynbio.5b00284 – volume: 10 start-page: 106 year: 2009 ident: B3 article-title: Flowcore: A Bioconductor Package for High Throughput Flow Cytometry publication-title: BMC Bioinf doi: 10.1186/1471-2105-10-106 – volume: 22 start-page: 1 year: 2020 ident: B7 article-title: Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering T-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization publication-title: ArXiv Preprint ArXiv:201204456 – volume: 58 year: 2014 ident: B11 article-title: ImmPort: Disseminating Data to the Public for the Future of Immunology publication-title: Immunol Res doi: 10.1007/s12026-014-8516-1 – volume: 37 start-page: 38 year: 2018 ident: B6 article-title: Dimensionality Reduction for Visualizing Single-Cell Data Using UMAP publication-title: Nat Biotechnol doi: 10.1038/nbt.4314 – ident: B15 |
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| SubjectTerms | Algorithms Computational Biology flow cytometry Flow Cytometry - methods FlowJo Humans Immunology Machine Learning python (programming language) single cell data science Single-Cell Analysis Software systems immunology Workflow |
| Title | FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows |
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