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        "chi2,numeric,matrix-method",
        "chi2,numeric,numeric-method",
        "chi2-methods"
      ]
    },
    {
      "page": "classWeights",
      "title": "Calculate class weights",
      "topics": [
        "classWeights"
      ]
    },
    {
      "page": "clustDist",
      "title": "Pairwise Distance Computation for Protein Information Sets",
      "topics": [
        "clustDist"
      ]
    },
    {
      "page": "ClustDist-class",
      "title": "Class '\"ClustDist\"'",
      "topics": [
        "class:ClustDist",
        "ClustDist",
        "ClustDist-class",
        "plot,ClustDist,MSnSet-method",
        "show,ClustDist-method"
      ]
    },
    {
      "page": "ClustDistList-class",
      "title": "Storing multiple ClustDist instances",
      "topics": [
        "class:ClustDistList",
        "ClustDistList",
        "ClustDistList-class",
        "lapply,ClustDistList-method",
        "length,ClustDistList-method",
        "names,ClustDistList-method",
        "names<-,ClustDistList,ANY-method",
        "plot,ClustDistList,missing-method",
        "sapply,ClustDistList-method",
        "show,ClustDistList-method",
        "[,ClustDistList,ANY,ANY,ANY-method",
        "[,ClustDistList,ANY,missing,missing-method",
        "[[,ClustDistList,ANY,ANY-method",
        "[[,ClustDistList,ANY,missing-method"
      ]
    },
    {
      "page": "empPvalues",
      "title": "Estimate empirical p-values for Chi^2 protein correlations.",
      "topics": [
        "empPvalues"
      ]
    },
    {
      "page": "fDataToUnknown",
      "title": "Update a feature variable",
      "topics": [
        "fDataToUnknown"
      ]
    },
    {
      "page": "filterBinMSnSet",
      "title": "Filter a binary MSnSet",
      "topics": [
        "filterBinMSnSet"
      ]
    },
    {
      "page": "filterMaxMarkers",
      "title": "Removes class/annotation information from a matrix of candidate markers that appear in the 'fData'.",
      "topics": [
        "filterMaxMarkers"
      ]
    },
    {
      "page": "filterMinMarkers",
      "title": "Removes class/annotation information from a matrix of candidate markers that appear in the 'fData'.",
      "topics": [
        "filterMinMarkers"
      ]
    },
    {
      "page": "filterZeroCols",
      "title": "Remove 0 columns/rows",
      "topics": [
        "filterZeroCols",
        "filterZeroRows"
      ]
    },
    {
      "page": "GenRegRes-class",
      "title": "Class '\"GenRegRes\"' and '\"ThetaRegRes\"'",
      "topics": [
        "class:GenRegRes",
        "class:ThetaRegRes",
        "combineThetaRegRes",
        "f1Count",
        "f1Count,GenRegRes-method",
        "f1Count,ThetaRegRes-method",
        "favourPrimary",
        "GenRegRes",
        "GenRegRes-class",
        "getF1Scores",
        "getF1Scores,GenRegRes-method",
        "getF1Scores,ThetaRegRes-method",
        "getParams",
        "getParams,GenRegRes-method",
        "getParams,ThetaRegRes-method",
        "getRegularisedParams",
        "getRegularisedParams,GenRegRes-method",
        "getRegularizedParams",
        "getRegularizedParams,GenRegRes-method",
        "getSeed",
        "getSeed,GenRegRes-method",
        "getWarnings",
        "getWarnings,GenRegRes-method",
        "levelPlot",
        "levelPlot,GenRegRes-method",
        "plot,GenRegRes,missing-method",
        "plot,ThetaRegRes,missing-method",
        "show,GenRegRes-method",
        "show,ThetaRegRes-method",
        "ThetaRegRes",
        "ThetaRegRes-class"
      ]
    },
    {
      "page": "getMarkerClasses",
      "title": "Returns the organelle classes in an 'MSnSet'",
      "topics": [
        "getMarkerClasses"
      ]
    },
    {
      "page": "getMarkers",
      "title": "Get the organelle markers in an 'MSnSet'",
      "topics": [
        "getMarkers"
      ]
    },
    {
      "page": "getNormDist",
      "title": "Extract Distances from a '\"ClustDistList\"' object",
      "topics": [
        "getNormDist"
      ]
    },
    {
      "page": "getPredictions",
      "title": "Returns the predictions in an 'MSnSet'",
      "topics": [
        "getPredictions"
      ]
    },
    {
      "page": "highlightOnPlot",
      "title": "Highlight features of interest on a spatial proteomics plot",
      "topics": [
        "highlightOnPlot",
        "highlightOnPlot3D"
      ]
    },
    {
      "page": "knnClassification",
      "title": "knn classification",
      "topics": [
        "knnClassification",
        "knnPrediction"
      ]
    },
    {
      "page": "knnOptimisation",
      "title": "knn parameter optimisation",
      "topics": [
        "knnOptimisation",
        "knnOptimization",
        "knnRegularisation"
      ]
    },
    {
      "page": "knntlClassification",
      "title": "knn transfer learning classification",
      "topics": [
        "knntlClassification"
      ]
    },
    {
      "page": "knntlOptimisation",
      "title": "theta parameter optimisation",
      "topics": [
        "knntlOptimisation"
      ]
    },
    {
      "page": "ksvmClassification",
      "title": "ksvm classification",
      "topics": [
        "ksvmClassification",
        "ksvmPrediction"
      ]
    },
    {
      "page": "ksvmOptimisation",
      "title": "ksvm parameter optimisation",
      "topics": [
        "ksvmOptimisation",
        "ksvmOptimization",
        "ksvmRegularisation"
      ]
    },
    {
      "page": "tagm-map",
      "title": "The `logPosteriors` function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.",
      "topics": [
        "class:MAPParams",
        "logPosteriors",
        "MAPParams",
        "MAPParams-class",
        "show,MAPParams-method",
        "tagmMapPredict",
        "tagmMapTrain"
      ]
    },
    {
      "page": "markerMSnSet",
      "title": "Extract marker/unknown subsets",
      "topics": [
        "markerMSnSet",
        "unknownMSnSet"
      ]
    },
    {
      "page": "MartInstance-class",
      "title": "Class '\"MartInstance\"'",
      "topics": [
        "as.data.frame.MartInstance",
        "as.data.frame.MartInstanceList",
        "filterAttrs",
        "getFilterList",
        "getMartInstanceList",
        "getMartTab",
        "lapply,MartInstanceList,ANY-method",
        "lapply,MartInstanceList-method",
        "MartInstance",
        "MartInstance-class",
        "MartInstanceList",
        "MartInstanceList-class",
        "nDatasets",
        "sapply,MartInstanceList,ANY-method",
        "sapply,MartInstanceList-method",
        "show,MartInstance-method",
        "[,MartInstanceList,ANY,ANY,ANY-method",
        "[,MartInstanceList,ANY,ANY-method",
        "[,MartInstanceList-method",
        "[[,MartInstanceList,ANY,ANY-method",
        "[[,MartInstanceList-method"
      ]
    },
    {
      "page": "mcmc-helpers",
      "title": "Number of outlier at each iteration of MCMC",
      "topics": [
        "geweke_test",
        "mcmc_burn_chains",
        "mcmc_get_meanComponent",
        "mcmc_get_meanoutliersProb",
        "mcmc_get_outliers",
        "mcmc_pool_chains",
        "mcmc_thin_chains",
        "plot,MCMCParams,character-method"
      ]
    },
    {
      "page": "MCMCParams",
      "title": "Instrastructure to store and process MCMC results",
      "topics": [
        ".MCMCChain",
        ".MCMCChains",
        ".MCMCParams",
        ".MCMCSummary",
        "chains",
        "class:MCMCChain",
        "class:MCMCChains",
        "class:MCMCParams",
        "class:MCMCSummary",
        "length,MCMCChains-method",
        "length,MCMCParams-method",
        "MCMCChain",
        "MCMCChain-class",
        "MCMCChains",
        "MCMCChains-class",
        "MCMCParams-class",
        "MCMCSummary",
        "MCMCSummary-class",
        "MCMCSummary-class.",
        "show,ComponentParam-method",
        "show,MCMCChain-method",
        "show,MCMCChains-method",
        "show,MCMCParams-method",
        "[,MCMCChains,ANY,ANY,ANY-method",
        "[,MCMCParams,ANY,ANY,ANY-method",
        "[[,MCMCChains,ANY,ANY-method",
        "[[,MCMCParams,ANY,ANY-method"
      ]
    },
    {
      "page": "minMarkers",
      "title": "Creates a reduced marker variable",
      "topics": [
        "minMarkers"
      ]
    },
    {
      "page": "mixing_posterior_check",
      "title": "Model calibration plots",
      "topics": [
        "mixing_posterior_check"
      ]
    },
    {
      "page": "MLearn-methods",
      "title": "The 'MLearn' interface for machine learning",
      "topics": [
        "MLearn,formula,MSnSet,clusteringSchema,missing-method",
        "MLearn,formula,MSnSet,learnerSchema,numeric-method",
        "MLearn,formula,MSnSet,learnerSchema,xvalSpec-method",
        "MLearnMSnSet",
        "MSnSetMLean"
      ]
    },
    {
      "page": "move2Ds",
      "title": "Displays a spatial proteomics animation",
      "topics": [
        "move2Ds"
      ]
    },
    {
      "page": "mrkConsProfiles",
      "title": "Marker consensus profiles",
      "topics": [
        "mrkConsProfiles"
      ]
    },
    {
      "page": "mrkHClust",
      "title": "Draw a dendrogram of subcellular clusters",
      "topics": [
        "mrkHClust"
      ]
    },
    {
      "page": "markers",
      "title": "Create a marker vector or matrix.",
      "topics": [
        "isMrkMat",
        "isMrkVec",
        "markers",
        "mrkEncoding",
        "mrkMatAndVec",
        "mrkMatToVec",
        "mrkVecToMat",
        "showMrkMat"
      ]
    },
    {
      "page": "nbClassification",
      "title": "nb classification",
      "topics": [
        "nbClassification",
        "nbPrediction"
      ]
    },
    {
      "page": "nbOptimisation",
      "title": "nb paramter optimisation",
      "topics": [
        "nbOptimisation",
        "nbOptimization",
        "nbRegularisation"
      ]
    },
    {
      "page": "nicheMeans2D",
      "title": "Uncertainty plot organelle means",
      "topics": [
        "nicheMeans2D"
      ]
    },
    {
      "page": "nndist-methods",
      "title": "Nearest neighbour distances",
      "topics": [
        "nndist",
        "nndist,matrix,matrix-method",
        "nndist,matrix,missing-method",
        "nndist,MSnSet,missing-method",
        "nndist-methods"
      ]
    },
    {
      "page": "nnetClassification",
      "title": "nnet classification",
      "topics": [
        "nnetClassification",
        "nnetPrediction"
      ]
    },
    {
      "page": "nnetOptimisation",
      "title": "nnet parameter optimisation",
      "topics": [
        "nnetOptimisation",
        "nnetOptimization",
        "nnetRegularisation"
      ]
    },
    {
      "page": "orderGoAnnotations",
      "title": "Orders annotation information",
      "topics": [
        "orderGoAnnotations"
      ]
    },
    {
      "page": "orgQuants",
      "title": "Returns organelle-specific quantile scores",
      "topics": [
        "orgQuants"
      ]
    },
    {
      "page": "perTurboClassification",
      "title": "perTurbo classification",
      "topics": [
        "perTurboClassification"
      ]
    },
    {
      "page": "perTurboOptimisation",
      "title": "PerTurbo parameter optimisation",
      "topics": [
        "perTurboOptimisation",
        "perTurboOptimization"
      ]
    },
    {
      "page": "phenoDisco",
      "title": "Runs the 'phenoDisco' algorithm.",
      "topics": [
        "phenoDisco"
      ]
    },
    {
      "page": "plot2D",
      "title": "Plot organelle assignment data and results.",
      "topics": [
        "plot2D",
        "plot2Dmethods",
        "plot3D,MSnSet-method"
      ]
    },
    {
      "page": "plot2Ds",
      "title": "Draw 2 data sets on one PCA plot",
      "topics": [
        "col1",
        "col2",
        "data1",
        "data2",
        "plot2Ds"
      ]
    },
    {
      "page": "plotConsProfiles",
      "title": "Plot marker consensus profiles.",
      "topics": [
        "plotConsProfiles"
      ]
    },
    {
      "page": "plotDist",
      "title": "Plots the distribution of features across fractions",
      "topics": [
        "plotDist"
      ]
    },
    {
      "page": "plotEllipse",
      "title": "A function to plot probabiltiy ellipses on marker PCA plots to visualise and assess TAGM models.",
      "topics": [
        "plotEllipse"
      ]
    },
    {
      "page": "plsdaClassification",
      "title": "plsda classification",
      "topics": [
        "plsdaClassification",
        "plsdaPrediction"
      ]
    },
    {
      "page": "plsdaOptimisation",
      "title": "plsda parameter optimisation",
      "topics": [
        "plsdaOptimisation",
        "plsdaOptimization",
        "plsdaRegularisation"
      ]
    },
    {
      "page": "pRolocmarkers",
      "title": "Organelle markers",
      "topics": [
        "pRolocmarkers"
      ]
    },
    {
      "page": "QSep-class",
      "title": "Quantify resolution of a spatial proteomics experiment",
      "topics": [
        "class::QSep",
        "levelPlot,QSep-method",
        "names,QSep-method",
        "names<-,QSep,character-method",
        "plot,QSep,missing-method",
        "plot,QSep-method",
        "QSep",
        "qsep",
        "QSep-class",
        "show,QSep-method",
        "summary,QSep-method"
      ]
    },
    {
      "page": "rfClassification",
      "title": "rf classification",
      "topics": [
        "rfClassification",
        "rfPrediction"
      ]
    },
    {
      "page": "rfOptimisation",
      "title": "svm parameter optimisation",
      "topics": [
        "rfOptimisation",
        "rfOptimization",
        "rfRegularisation"
      ]
    },
    {
      "page": "sampleMSnSet",
      "title": "Extract a stratified sample of an 'MSnSet'",
      "topics": [
        "sampleMSnSet"
      ]
    },
    {
      "page": "getStockcol",
      "title": "Manage default colours and point characters",
      "topics": [
        "getLisacol",
        "getOldcol",
        "getStockbg",
        "getStockcol",
        "getStockpch",
        "getUnknownbg",
        "getUnknowncol",
        "getUnknownpch",
        "setLisacol",
        "setOldcol",
        "setStockbg",
        "setStockcol",
        "setStockpch",
        "setUnknownbg",
        "setUnknowncol",
        "setUnknownpch"
      ]
    },
    {
      "page": "spatial2D",
      "title": "Uncertainty plot in localisation probabilities",
      "topics": [
        "spatial2D"
      ]
    },
    {
      "page": "SpatProtVis-class",
      "title": "Class 'SpatProtVis'",
      "topics": [
        "class:SpatProtVis",
        "plot,SpatProtVis,missing-method",
        "show,SpatProtVis-method",
        "SpatProtVis",
        "SpatProtVis-class"
      ]
    },
    {
      "page": "subsetMarkers",
      "title": "Subsets markers",
      "topics": [
        "subsetMarkers"
      ]
    },
    {
      "page": "svmClassification",
      "title": "svm classification",
      "topics": [
        "svmClassification",
        "svmPrediction"
      ]
    },
    {
      "page": "svmOptimisation",
      "title": "svm parameter optimisation",
      "topics": [
        "svmOptimisation",
        "svmOptimization",
        "svmRegularisation"
      ]
    },
    {
      "page": "tagm-mcmc",
      "title": "Localisation of proteins using the TAGM MCMC method",
      "topics": [
        "tagmMcmcPredict",
        "tagmMcmcProcess",
        "tagmMcmcTrain",
        "tagmPredict"
      ]
    },
    {
      "page": "testMarkers",
      "title": "Tests marker class sizes",
      "topics": [
        "testMarkers"
      ]
    },
    {
      "page": "testMSnSet",
      "title": "Create a stratified 'test' 'MSnSet'",
      "topics": [
        "testMSnSet"
      ]
    },
    {
      "page": "thetas",
      "title": "Draw matrix of thetas to test",
      "topics": [
        "thetas"
      ]
    },
    {
      "page": "undocumented",
      "title": "Undocumented/unexported entries",
      "topics": [
        "getParams,ClustRegRes-method",
        "levelPlot,ClustRegRes-method",
        "plot,ClustRegRes,missing-method",
        "show,ClustRegRes-method",
        "undocumented"
      ]
    },
    {
      "page": "zerosInBinMSnSet",
      "title": "Compute the number of non-zero values in each marker classes",
      "topics": [
        "zerosInBinMSnSet"
      ]
    }
  ],
  "_readme": "https://github.com/bioc/pRoloc/raw/HEAD/README.md",
  "_rundeps": [
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    "affy",
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    "AnnotationFilter",
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    "BH",
    "Biobase",
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    "BiocParallel",
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    "flexmix",
    "FNN",
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    "futile.options",
    "future",
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    "gbm",
    "gdata",
    "genefilter",
    "generics",
    "GenomicRanges",
    "ggplot2",
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    "globals",
    "glue",
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    "gtools",
    "hardhat",
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    "hms",
    "htmltools",
    "htmlwidgets",
    "httpuv",
    "httr",
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    "impute",
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    "lubridate",
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    "MASS",
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    "RSQLite",
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    "S4Vectors",
    "S7",
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    "sass",
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    "segmented",
    "Seqinfo",
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    "sparsevctrs",
    "Spectra",
    "SQUAREM",
    "statmod",
    "stringi",
    "stringr",
    "SummarizedExperiment",
    "survival",
    "sys",
    "threejs",
    "tibble",
    "tidyr",
    "tidyselect",
    "timechange",
    "timeDate",
    "tinytex",
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    "utf8",
    "vctrs",
    "viridis",
    "viridisLite",
    "vsn",
    "withr",
    "xfun",
    "XML",
    "xml2",
    "xtable",
    "XVector",
    "yaml"
  ],
  "_sysdeps": [
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  ],
  "_vignettes": [
    {
      "source": "v05-pRoloc-transfer-learning.Rmd",
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