ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2020
High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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25 citing papers in PubMed, 43 citations in OpenAlex.
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- Development of Spectral Imaging Cytometry.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Performance of spectral flow cytometry and mass cytometry for the study of innate myeloid cell populations.Frontiers in immunology · 2023Article
- Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
- Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data.iScience · 2022Article
- A roadmap for translational cancer glycoimmunology at single cell resolution.Journal of experimental & clinical cancer research : CR · 2022Review
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- FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology.Blood advances · 2022Article
- Spectral flow cytometry cluster analysis of therapeutic donor lymphocyte infusions identifies T cell subsets associated with outcome in patients with AML relapse.Frontiers in immunology · 2022Observational
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
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. © 2020 International Society for Advancement of Cytometry.
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