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Title: Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline
Authors: Chen H.
Lau M.C.
Wong M.T.
Newell E.W. 
Poidinger M. 
Chen J. 
Keywords: algorithm
CD4+ T lymphocyte
computer interface
controlled study
data analysis
flow cytometry
human cell
mass cytometry
measurement accuracy
measurement precision
peripheral blood mononuclear cell
Issue Date: 2016
Citation: Chen H., Lau M.C., Wong M.T., Newell E.W., Poidinger M., Chen J. (2016). Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline. PLoS Computational Biology 12 (9) : e1005112. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14 ? CD19 ? PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4 + T cell dataset, cytofkit uncovered multiple subtypes of T FH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, Cytofkit is also applicable for flow cytometry data analysis. ? 2016 Chen et al.
Source Title: PLoS Computational Biology
ISSN: 1553734X
DOI: 10.1371/journal.pcbi.1005112
Rights: Attribution 4.0 International
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