To ensure that foundational cytometry operations are supported, we checked for full compliance with Gating-ML 2.0 ( 1), hence ensuring that compensation, transformation, and gating operations were all implemented correctly. Specifically, we wanted to develop a robust basis for foundational cytometry operations, provide a straightforward interface to SCDS algorithms, and facilitate the integration of manual and automated analysis. We developed FlowKit to bridge the gap between manual and automated workflows.
However, there are also severe limitations to a purely manual workflow for data analysis, especially the poor scalability to high-volume workflows and limitations of visual discovery for high-dimensional data sets. For example, domain experts are typically better at removing debris, dead cells, and cell aggregates by gating than automated approaches. There are good reasons for this - traditional software such as FlowJo excels at the visual manipulation and analysis of data, and human analysis is inherently more adaptable than any fully automated workflow. 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.ĭespite the phenomenal advances in Single Cell Data Science (SCDS) methodology and an ever-growing collection of algorithms and open-source packages, it is an open secret that the day-to-day analysis of cytometric data in flow laboratories and core facilities is still predominantly performed using traditional software, especially FlowJo.
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. 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. 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. An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts.
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