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Improving the Rigor and Reproducibility of Flow Cytometry‐Based Clinical Research and Trials Through Automated Data Analysis

The steps that characterize data analysis for flow cytometrybased clinical trials can be grouped into quality assessment, compensation, normalization, transformation, cell population identification, cross-sample comparison (population mapping or matching), feature extraction, visualization, and interpretation. Of these steps, cell population identification (i.e., gating), which generates reportables such as cell population counts/percentages and MFI, is the focus of significant efforts to improve the rigor and reproducibility of data analysis. Here, rigor is the application of the scientific method to ensure unbiased and well-controlled analysis, interpretation, and reporting of results. Reproducibility is important as analyses are only validated when they can be duplicated by multiple scientists. It is especially important for clinical studies due to what has been deemed a reproducibility crisis in medicine, as only 11% of a series of preclinical cancer studies could be replicated (1). While manual gating is the gold standard and current practice for cell population identification, assessments of its reproducibility have recognized it as a significant contributor of variation in flow cytometry studies, with interlaboratory C.V.s up to 30% (2). While having a single operator analyze all the data can significantly reduce variability, the chosen individual is still influenced by the same personal biases that result in inconsistencies within and across individuals, centers, and time. Resorting to a single operator to improve reproducibility does not scale for large studies as manual gating can take from 45 to 90 min for one clinical sample (3). With clinical trials now involving thousands of patients assayed with 18 parameters, the challenges associated with the rigor and reproducibility of manual analysis of flow cytometry-based clinical data have only become more apparent and pressing. The first algorithm for automated flow cytometry cell population identification was published in 1985, where it was noted that: “Unfortunately, the use of three or more independent fluorescent parameters complicates the analysis of the resulting data significantly” (4). While data complexity has increased significantly since then, automated analysis approaches for flow cytometry data have also matured. The state of the art is at a stage where results from automated data analysis algorithms have reached a level of maturity that enables them to match, and in many cases exceed, the results produced by human experts. The maturity and acceptance has gone as far as being included as part of the documentation for the FDA approval of a first in class CAR-T therapy (5–7). The overwhelming majority of automated gating methods are unsupervised. Unsupervised algorithms are those approaches that work on data that do not come with predefined labels. With respect to FCM data, these algorithms