Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1005112
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dc.titleCytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline
dc.contributor.authorChen H.
dc.contributor.authorLau M.C.
dc.contributor.authorWong M.T.
dc.contributor.authorNewell E.W.
dc.contributor.authorPoidinger M.
dc.contributor.authorChen J.
dc.date.accessioned2019-11-08T06:47:21Z
dc.date.available2019-11-08T06:47:21Z
dc.date.issued2016
dc.identifier.citationChen 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. https://doi.org/10.1371/journal.pcbi.1005112
dc.identifier.issn1553734X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161907
dc.description.abstractSingle-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, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis. ? 2016 Chen et al.
dc.sourceUnpaywall
dc.subjectalgorithm
dc.subjectArticle
dc.subjectbioinformatics
dc.subjectCD4+ T lymphocyte
dc.subjectcomputer interface
dc.subjectcontrolled study
dc.subjectcytometry
dc.subjectdata analysis
dc.subjectflow cytometry
dc.subjecthuman
dc.subjecthuman cell
dc.subjectmass cytometry
dc.subjectmeasurement accuracy
dc.subjectmeasurement precision
dc.subjectperipheral blood mononuclear cell
dc.typeArticle
dc.contributor.departmentDEPT OF MICROBIOLOGY & IMMUNOLOGY
dc.contributor.departmentBIOLOGY (NU)
dc.description.doi10.1371/journal.pcbi.1005112
dc.description.sourcetitlePLoS Computational Biology
dc.description.volume12
dc.description.issue9
dc.description.pagee1005112
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