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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Published inFrontiers in immunology Vol. 12; p. 768541
Main Authors White, Scott, Quinn, John, Enzor, Jennifer, Staats, Janet, Mosier, Sarah M., Almarode, James, Denny, Thomas N., Weinhold, Kent J., Ferrari, Guido, Chan, Cliburn
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 05.11.2021
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Online AccessGet full text
ISSN1664-3224
1664-3224
DOI10.3389/fimmu.2021.768541

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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.
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
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– 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
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Copyright Copyright © 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.
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Reviewed by: Thomas Myles Ashhurst, The University of Sydney, Australia; Gur Yaari, Bar-Ilan University, Israel
This article was submitted to Systems Immunology, a section of the journal Frontiers in Immunology
Edited by: Juan J. Garcia-Vallejo, Amsterdam University Medical Center, Netherlands
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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
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Title FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows
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