![]() Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment.
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