Duke Center for AIDS Research, Duke University, Durham, NC, United States.
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, United States.
Front Immunol. 2021 Nov 5;12:768541. doi: 10.3389/fimmu.2021.768541. eCollection 2021.
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.
流式细胞术数据分析的一个重要挑战是如何促进领域专家和定量专家之间富有成效的合作。面对日益复杂的数据,流式细胞术实验室和核心设施中的领域专家越来越认识到自动化工作流程的必要性,但总体而言,他们仍然使用传统应用程序(主要是 FlowJo)进行所有分析。在很大程度上,这使得领域专家无法使用不断增长的单细胞数据科学算法库,限制了这些专家对结果验证和解释的潜在贡献。为了解决这个挑战,我们开发了 FlowKit,这是一个符合 Gating-ML 2.0 标准的 Python 包,可以读取和写入 FCS 文件和 FlowJo 工作区。我们展示了使用 FlowKit 构建报告和分析工作流程的示例,包括将结果往返传输到 FlowJo 以进行领域和定量专家的联合分析。