High‐Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data
The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single‐cell level. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize, and interpret these high‐dimensional data sets. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high‐dimensional data sets using expert gating, t‐distributed stochastic neighbor embedding (t‐SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high‐dimensional clustering or dimensionality reduction algorithms such as t‐SNE, UMAP, or FlowSOM. However, large scale studies combined with an in‐depth technical analysis will be needed to assess differences between these technologies in more detail.
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