Background fluorescence and spreading error are major contributors of variability in high‐dimensional flow cytometry data visualization by t‐distributed stochastic neighboring embedding

By E. Mazza, J. Brummelman, Giorgia Alvisi, Alessandra Roberto, F. De Paoli, V. Zanon, F. Colombo, Mario Roederer, E. Lugli. 2018

33 citations recorded by the source corpus.

Multidimensional single‐cell analysis requires approaches to visualize complex data in intuitive 2D graphs. In this regard, t‐distributed stochastic neighboring embedding (tSNE) is the most popular algorithm for single‐cell RNA sequencing and cytometry by time‐of‐flight (CyTOF), but its application to polychromatic flow cytometry, including the recently developed 30‐parameter platform, is still under investigation. We identified differential distribution of background values between samples, generated by either background calculation or spreading error (SE), as a major source of variability in polychromatic flow cytometry data representation by tSNE, ultimately resulting in the identification of erroneous heterogeneity among cell populations. Biexponential transformation of raw data and limiting SE during panel development dramatically improved data visualization. These aspects must be taken into consideration when using computational approaches as discovery tools in large sets of samples from independent experiments or immunomonitoring in clinical trials.

Subfields: Biophysics, Molecular Biology

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In Biophysics

1. J. Snyder-Cappione
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1. J. Snyder-Cappione
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6. Martin Stražar

Citations in the corpus

• Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets (2018, background): “While opt-SNE and t-SNE both capture the macro-structure illustrated by gene overlays, several Louvain clusters (3, 18, 19, 25, 28) appear partially or completely overlapped by other clusters in standard t-SNE but not in the opt-SNE embedding.”