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Background fluorescence and spreading error are major contributors of variability in high‐dimensional flow cytometry data visualization by t‐distributed stochastic neighboring embedding
Author(s) -
Mazza Emilia Maria Cristina,
Brummelman Jolanda,
Alvisi Giorgia,
Roberto Alessandra,
De Paoli Federica,
Za Veronica,
Colombo Federico,
Roederer Mario,
Lugli Enrico
Publication year - 2018
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.23566
Subject(s) - visualization , computer science , embedding , flow cytometry , data visualization , transformation (genetics) , data mining , representation (politics) , cytometry , limiting , algorithm , biological system , artificial intelligence , biology , mechanical engineering , engineering , biochemistry , politics , gene , political science , law , genetics
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.