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FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows.
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.
Type
Journal articleSubject
HumansFlow Cytometry
Computational Biology
Algorithms
Software
Workflow
Single-Cell Analysis
Machine Learning
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https://hdl.handle.net/10161/25009Published Version (Please cite this version)
10.3389/fimmu.2021.768541Publication Info
White, Scott; Quinn, John; Enzor, Jennifer; Staats, Janet; Mosier, Sarah M; Almarode,
James; ... Chan, Cliburn (2021). FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows.
Frontiers in immunology, 12. pp. 768541. 10.3389/fimmu.2021.768541. Retrieved from https://hdl.handle.net/10161/25009.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Chi Wei Cliburn Chan
Professor of Biostatistics & Bioinformatics
Computational immunology (stochastic and spatial models and simulations, T cell signaling,
immune regulation) Statistical methodology for immunological laboratory techniques
(flow cytometry, CFSE analysis, receptor-ligand binding and signaling kinetics) Informatics
of the immune system (reference and application ontologies, meta-programming, text
mining and machine learning)
Thomas Norton Denny
Professor in Medicine
Thomas N. Denny, MSc, M.Phil, is the Chief Operating Officer of the Duke Human Vaccine
Institute (DHVI), Associate Dean for Duke Research and Discovery @RTP, and a Professor
of Medicine in the Department of Medicine at Duke University Medical Center. He is
also an Affiliate Member of the Duke Global Health Institute. Previously, he served
on the Health Sector Advisory Council of the Duke University Fuquay School of Business.
Prior to joining Duke, he was an Associate Professor of Pathology, Labo
Guido Ferrari
Professor in Surgery
The activities of the Ferrari Laboratory are based on both independent basic research
and immune monitoring studies. The research revolves around three main areas of interest:
class I-mediated cytotoxic CD8+ T cell responses, antibody-dependent cellular cytotoxicity
(ADCC), gene expression in NK and T cellular subsets upon infection with HIV-1. With
continuous funding over the last 11 years from the NIH and Bill & Melinda Gates Foundation
along with many other productive collaborations wi
Kent James Weinhold
Joseph W. and Dorothy W. Beard Distinguished Professor of Experimental Surgery
The Weinhold Laboratory is currently focused on utilizing a comprehensive repertoire
of highly standardized and formerly validated assay platforms to profile the human
immune system in order to identify immunologic signatures that predict disease outcomes.
These ongoing studies span a broad range of highly relevant clinical arenas, including:
1) cancer (non-small cell lung cancer, head and neck cancer, glioblastoma neoforme,
ovarian cancer, and prostate cancer), 2) autoimmune
Alphabetical list of authors with Scholars@Duke profiles.

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