FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows

• Published in: Frontiers 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
• Language: English
• Published: Switzerland Frontiers Media S.A 05.11.2021
• Subjects: 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; software; python (programming language); single cell data science; FlowJo; GatingML; flow cytometry; systems immunology
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2021.768541

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.