Assignment tolerance parameters are available to allow users to balance high cell recovery with accurate assignments. Adaptive regression modeling accounted for differential dye uptake between different cell types and Gaussian mixture modeling provided a robust method to probabilistically assign cells to samples. DebarcodeR addresses these unmet needs by providing a framework for computational debarcoding augmented by improvements to experimental methods. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes.
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