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Improving the Rigor and Reproducibility of Flow Cytometry-Based Clinical Research and Trials Through Automated Data Analysis
Brinkman RR. Improving the Rigor and Reproducibility of Flow Cytometry-Based Clinical Research and Trials Through Automated Data Analysis. Cytometry A, 2019. [Epub ahead of print]
The steps that characterize data analysis for flow cytometry‐based 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.