{
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  "Package": "CytoMDS",
  "Title": "Low Dimensions projection of cytometry samples",
  "Version": "1.9.0",
  "Authors@R": "c(person(given = \"Philippe\",\nfamily = \"Hauchamps\",\nrole = c(\"aut\", \"cre\"),\nemail = \"philippe.hauchamps@uclouvain.be\",\ncomment = c(ORCID = \"0000-0003-2865-1852\")),\nperson(given = \"Laurent\", family = \"Gatto\",\nemail = \"laurent.gatto@uclouvain.be\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-1520-2268\")),\nperson(given = \"Dan\",\nfamily = \"Lin\",\nrole = \"ctb\",\nemail = \"dan.8.lin@gsk.com\"))",
  "Description": "This package implements a low dimensional visualization of\na set of cytometry samples, in order to visually assess the\n'distances' between them. This, in turn, can greatly help the\nuser to identify quality issues like batch effects or outlier\nsamples, and/or check the presence of potential sample clusters\nthat might align with the exeprimental design. The CytoMDS\nalgorithm combines, on the one hand, the concept of Earth\nMover's Distance (EMD), a.k.a. Wasserstein metric and, on the\nother hand, the Multi Dimensional Scaling (MDS) algorithm for\nthe low dimensional projection. Also, the package provides some\ndiagnostic tools for both checking the quality of the MDS\nprojection, as well as tools to help with the interpretation of\nthe axes of the projection.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "BugReports": "https://github.com/UCLouvain-CBIO/CytoMDS/issues",
  "URL": "https://uclouvain-cbio.github.io/CytoMDS",
  "biocViews": "FlowCytometry, QualityControl, DimensionReduction,\nMultidimensionalScaling, Software, Visualization",
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  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-28 13:02:40 UTC",
  "RemoteUrl": "https://github.com/bioc/CytoMDS",
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    "User": "root"
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  "Author": "Philippe Hauchamps [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-2865-1852>),\nLaurent Gatto [aut] (ORCID: <https://orcid.org/0000-0002-1520-2268>),\nDan Lin [ctb]",
  "Maintainer": "Philippe Hauchamps <philippe.hauchamps@uclouvain.be>",
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  "_published": "2026-05-28T06:50:21.604Z",
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    "featureNames",
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    "stress"
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      "page": "channelSummaryStats",
      "title": "Summary statistics per channel computation",
      "topics": [
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      ]
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    {
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      "title": "metric MDS projection of sample",
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        "computeMetricMDS"
      ]
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    {
      "page": "DistSum-class",
      "title": "DistSum class",
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        "as.matrix,DistSum-method",
        "colnames,DistSum-method",
        "colnames<-,DistSum-method",
        "dim,DistSum-method",
        "dimnames,DistSum-method",
        "dimnames<-,DistSum,ANY-method",
        "dimnames<-,DistSum,list-method",
        "distByFeature",
        "DistSum",
        "DistSum,list-method",
        "DistSum,matrix-method",
        "DistSum-class",
        "featureNames,DistSum-method",
        "featureNames<-,DistSum-method",
        "ncol,DistSum-method",
        "nFeatures",
        "nrow,DistSum-method",
        "rownames,DistSum-method",
        "rownames<-,DistSum-method",
        "show,DistSum-method",
        "[,DistSum,ANY,ANY,ANY-method",
        "[,DistSum,ANY,ANY,missing-method",
        "[,DistSum,ANY,missing,ANY-method",
        "[,DistSum,ANY,missing,missing-method"
      ]
    },
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      "page": "EMDDist",
      "title": "Calculate Earth Mover's distance between two samples",
      "topics": [
        "EMDDist"
      ]
    },
    {
      "page": "ggplotDistFeatureImportance",
      "title": "Plot of feature relative importance in distance",
      "topics": [
        "ggplotDistFeatureImportance"
      ]
    },
    {
      "page": "ggplotMarginalDensities",
      "title": "Plot of channel intensity marginal densities",
      "topics": [
        "ggplotMarginalDensities"
      ]
    },
    {
      "page": "ggplotSampleMDS",
      "title": "Plot of Metric MDS object",
      "topics": [
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    },
    {
      "page": "ggplotSampleMDSShepard",
      "title": "Plot of Metric MDS object - Shepard diagram",
      "topics": [
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        "nPoints",
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        "stress"
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      "title": "Low Dimensional Projection of Cytometry Samples",
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      "engine": "knitr::rmarkdown",
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        "Installation and loading dependencies",
        "Introduction",
        "Illustrative dataset",
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        "Individual marker contribution in the distance matrix",
        "Metric Multidimensional scaling",
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