ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2018
Background fluorescence and spreading error are major contributors of variability in high-dimensional flow cytometry data visualization by t-distributed stochastic neighboring embedding.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data.BMC bioinformatics · 2024Article
- Variability of fluorescence intensity distribution measured by flow cytometry is influenced by cell size and cell cycle progression.Scientific reports · 2023Article
- Mapping Cell Phenomics with Multiparametric Flow Cytometry Assays.Phenomics (Cham, Switzerland) · 2022Review
- Immune cell profiling in atherosclerosis: role in research and precision medicine.Nature reviews. Cardiology · 2022Review
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition).European journal of immunology · 2021Review
- Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data.International journal of molecular sciences · 2021Article
- Activity-Based Probes to Utilize the Proteolytic Activity of Cathepsin G in Biological Samples.Frontiers in chemistry · 2021Review
- High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Computational Analysis of Multiparametric Flow Cytometric Data to Dissect B Cell Subsets in Vaccine Studies.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors.Frontiers in immunology · 2020Review
- Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data.Frontiers in cell and developmental biology · 2020Review
- Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.Nature communications · 2019Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry.Nature protocols · 2019Article
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9 authors.
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Abstract
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.
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