Cytometry is a single cell, high-dimensional, high-throughput technique that is being applied across a range of disciplines. However, many elements alongside the data acquisition process might give rise to technical variation in the dataset, called batch effects. CytoNorm is a normalization algorithm for batch effect removal in cytometry data that was originally published in 2020 and has been applied on a variety of datasets since then. Here, we present CytoNorm 2.0, discussing new, illustrative use cases to increase the applicability of the algorithm and showcasing new visualizations that enable thorough quality control and understanding of the normalization process. We explain how CytoNorm can be used without the need for technical replicates or controls, show how the goal distribution can be tailored toward the experimental design and we elaborate on the choice of markers for CytoNorm's internal FlowSOM clustering step.
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