![]() ![]() 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. ![]() If you find such alterations necessary, then perhaps you should consider the quality of the compensation controls.An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Warning! Manual alteration of the spillover coefficient matrices is not trivial, especially when more than a few parameters are involved. The aquisition matrix is created when you set your compensation on a new generation instrument such as the DiVa, FC500, or CyAn. Note that you can edit a hardware compensation matrix by choosing to create a new matrix using the acquisition matrix as the source. If you check the Immediately recalculate Workspace statistics in current group with changescheckbox, your statistics will update live to reflect changes made to a matrix. (FlowJo will always show you which values you modified.) If you have graphs open, they will automatically update to show you the changes you are making. You can edit the compensation matrices directly from FlowJo, using the Platform-> Compensate Sample-> Edit/Save Matrix command (from the Platform -> Compensate Sample menu, you can also Remove Compensation).įrom this interface, you can save matrices to disk (so that you can import them into other workspaces), you can create an entirely new matrix from scratch, or you can modify an existing matrix. When you create a new compensation matrix, FlowJo no longer automatically saves it to a file. Starting with FlowJo version 4.5, you can easily change the compensation matrix values. If you are not familiar with the format of the Compensation Matrix, read that page first.
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