FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows.
dc.contributor.author | White, Scott | |
dc.contributor.author | Quinn, John | |
dc.contributor.author | Enzor, Jennifer | |
dc.contributor.author | Staats, Janet | |
dc.contributor.author | Mosier, Sarah M | |
dc.contributor.author | Almarode, James | |
dc.contributor.author | Denny, Thomas N | |
dc.contributor.author | Weinhold, Kent J | |
dc.contributor.author | Ferrari, Guido | |
dc.contributor.author | Chan, Cliburn | |
dc.date.accessioned | 2022-05-03T19:29:15Z | |
dc.date.available | 2022-05-03T19:29:15Z | |
dc.date.issued | 2021-01 | |
dc.date.updated | 2022-05-03T19:29:14Z | |
dc.description.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. | |
dc.identifier.issn | 1664-3224 | |
dc.identifier.issn | 1664-3224 | |
dc.identifier.uri | ||
dc.language | eng | |
dc.publisher | Frontiers Media SA | |
dc.relation.ispartof | Frontiers in immunology | |
dc.relation.isversionof | 10.3389/fimmu.2021.768541 | |
dc.subject | Humans | |
dc.subject | Flow Cytometry | |
dc.subject | Computational Biology | |
dc.subject | Algorithms | |
dc.subject | Software | |
dc.subject | Workflow | |
dc.subject | Single-Cell Analysis | |
dc.subject | Machine Learning | |
dc.title | FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows. | |
dc.type | Journal article | |
duke.contributor.orcid | Ferrari, Guido|0000-0001-7747-3349 | |
duke.contributor.orcid | Chan, Cliburn|0000-0001-5901-6806 | |
pubs.begin-page | 768541 | |
pubs.organisational-group | Duke | |
pubs.organisational-group | School of Medicine | |
pubs.organisational-group | Trinity College of Arts & Sciences | |
pubs.organisational-group | Staff | |
pubs.organisational-group | Basic Science Departments | |
pubs.organisational-group | Clinical Science Departments | |
pubs.organisational-group | Institutes and Centers | |
pubs.organisational-group | Biostatistics & Bioinformatics | |
pubs.organisational-group | Immunology | |
pubs.organisational-group | Molecular Genetics and Microbiology | |
pubs.organisational-group | Medicine | |
pubs.organisational-group | Pathology | |
pubs.organisational-group | Surgery | |
pubs.organisational-group | Medicine, Duke Human Vaccine Institute | |
pubs.organisational-group | Surgery, Surgical Sciences | |
pubs.organisational-group | Duke Cancer Institute | |
pubs.organisational-group | Statistical Science | |
pubs.organisational-group | Duke Human Vaccine Institute | |
pubs.organisational-group | Institutes and Provost's Academic Units | |
pubs.organisational-group | University Institutes and Centers | |
pubs.organisational-group | Duke Global Health Institute | |
pubs.publication-status | Published | |
pubs.volume | 12 |
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