Package index • pRoloc

All functions

class:AnnotationParams AnnotationParams AnnotationParams-class show,AnnotationParams-method AnnotationParams setAnnotationParams getAnnotationParams dunkley2006params andy2011params: Class "AnnotationParams"

ClustDist class:ClustDist ClustDist-class plot,ClustDist,MSnSet-method show,ClustDist-method: Class "ClustDist"

ClustDistList class:ClustDistList ClustDistList-class plot,ClustDistList,missing-method show,ClustDistList-method [,ClustDistList,ANY,ANY,ANY-method [,ClustDistList,ANY,missing,missing-method [[,ClustDistList,ANY,ANY-method [[,ClustDistList,ANY,missing-method length,ClustDistList-method names,ClustDistList-method names lapply,ClustDistList-method sapply,ClustDistList-method: Storing multiple ClustDist instances

GenRegRes ThetaRegRes class:GenRegRes class:ThetaRegRes GenRegRes-class ThetaRegRes-class getF1Scores getF1Scores,GenRegRes-method getF1Scores,ThetaRegRes-method f1Count f1Count,GenRegRes-method f1Count,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 combineThetaRegRes favourPrimary: Class "GenRegRes" and "ThetaRegRes"

chains() show(<MCMCParams>) show(<ComponentParam>) show(<MCMCChain>) length(<MCMCChains>) length(<MCMCParams>) `[[`(<MCMCChains>,<ANY>,<ANY>) `[[`(<MCMCParams>,<ANY>,<ANY>) `[`(<MCMCChains>,<ANY>,<ANY>,<ANY>) `[`(<MCMCParams>,<ANY>,<ANY>,<ANY>) show(<MCMCChains>): Instrastructure to store and process MCMC results

MLearn,formula,MSnSet,learnerSchema,numeric-method MLearn,formula,MSnSet,learnerSchema,xvalSpec-method MLearn,formula,MSnSet,clusteringSchema,missing-method MSnSetMLean MLearnMSnSet: The MLearn interface for machine learning

MartInstance-class MartInstance show,MartInstance-method MartInstanceList-class MartInstanceList as.data.frame.MartInstanceList as.data.frame.MartInstance [,MartInstanceList-method [,MartInstanceList,ANY,ANY-method [,MartInstanceList,ANY,ANY,ANY-method [[,MartInstanceList-method [[,MartInstanceList,ANY,ANY-method sapply,MartInstanceList-method sapply,MartInstanceList,ANY-method lapply,MartInstanceList-method lapply,MartInstanceList,ANY-method nDatasets filterAttrs getMartInstanceList getMartTab getFilterList: Class "MartInstance"

QSep-class class::QSep QSep show,QSep-method summary,QSep-method names,QSep-method names plot,QSep-method plot,QSep,missing-method levelPlot,QSep-method qsep: Quantify resolution of a spatial proteomics experiment

SpatProtVis(): Class SpatProtVis

addGoAnnotations(): Add GO annotations

addLegend(): Adds a legend

addMarkers(): Adds markers to the data

checkFeatureNamesOverlap(): Check feature names overlap

checkFvarOverlap(): Compare a feature variable overlap

chi2 chi2-methods chi2,matrix,matrix-method chi2,matrix,numeric-method chi2,numeric,matrix-method chi2,numeric,numeric-method: The PCP 'chi square' method

classWeights(): Calculate class weights

clustDist(): Pairwise Distance Computation for Protein Information Sets

empPvalues(): Estimate empirical p-values for \(Chi^2\) protein correlations.

fDataToUnknown(): Update a feature variable

filterBinMSnSet(): Filter a binary MSnSet

filterMaxMarkers(): Removes class/annotation information from a matrix of candidate markers that appear in the fData.

filterMinMarkers(): Removes class/annotation information from a matrix of candidate markers that appear in the fData.

filterZeroCols() filterZeroRows(): Remove 0 columns/rows

getGOFromFeatures(): Retrieve GO terms for feature names

getMarkerClasses(): Returns the organelle classes in an 'MSnSet'

getMarkers(): Get the organelle markers in an MSnSet

getNormDist(): Extract Distances from a "ClustDistList" object

getPredictions(): Returns the predictions in an 'MSnSet'

setLisacol() getLisacol() getOldcol() setOldcol() getStockcol() setStockcol() getStockpch() setStockpch() getUnknowncol() setUnknowncol() getUnknownpch() setUnknownpch(): Manage default colours and point characters

goIdToTerm() goTermToId() flipGoTermId() prettyGoTermId(): Convert GO ids to/from terms

highlightOnPlot() highlightOnPlot3D(): Highlight features of interest on a spatial proteomics plot

knnClassification(): knn classification

knnOptimisation(): knn parameter optimisation

knntlClassification(): knn transfer learning classification

knntlOptimisation(): theta parameter optimisation

ksvmClassification(): ksvm classification

ksvmOptimisation(): ksvm parameter optimisation

makeGoSet(): Creates a GO feature MSnSet

markerMSnSet() unknownMSnSet(): Extract marker/unknown subsets

mrkVecToMat() mrkMatToVec() mrkMatAndVec() showMrkMat() isMrkMat() isMrkVec() mrkEncoding(): Create a marker vector or matrix.

mcmc_get_outliers() mcmc_get_meanComponent() mcmc_get_meanoutliersProb() geweke_test() mcmc_pool_chains() mcmc_burn_chains() mcmc_thin_chains() plot(<MCMCParams>,<character>): Number of outlier at each iteration of MCMC

minMarkers(): Creates a reduced marker variable

mixing_posterior_check(): Model calibration plots

move2Ds(): Displays a spatial proteomics animation

mrkConsProfiles(): Marker consensus profiles

mrkHClust(): Draw a dendrogram of subcellular clusters

nbClassification(): nb classification

nbOptimisation(): nb paramter optimisation

nicheMeans2D(): Uncertainty plot organelle means

nndist-methods nndist,matrix,matrix-method nndist,matrix,missing-method nndist,MSnSet,missing-method nndist: Nearest neighbour distances

nnetClassification(): nnet classification

nnetOptimisation(): nnet parameter optimisation

orderGoAnnotations(): Orders annotation information

orgQuants(): Returns organelle-specific quantile scores

pRolocmarkers(): Organelle markers

perTurboClassification(): perTurbo classification

perTurboOptimisation(): PerTurbo parameter optimisation

phenoDisco(): Runs the phenoDisco algorithm.

plot2D() plot3D(<MSnSet>): Plot organelle assignment data and results.

plot2Ds(): Draw 2 data sets on one PCA plot

plotConsProfiles(): Plot marker consenses profiles.

plotDist(): Plots the distribution of features across fractions

plotEllipse(): A function to plot probabiltiy ellipses on marker PCA plots to visualise and assess TAGM models.

plsdaClassification(): plsda classification

plsdaOptimisation(): plsda parameter optimisation

rfClassification(): rf classification

rfOptimisation(): svm parameter optimisation

sampleMSnSet(): Extract a stratified sample of an MSnSet

showGOEvidenceCodes() getGOEvidenceCodes(): GO Evidence Codes

spatial2D(): Uncertainty plot in localisation probabilities

subsetMarkers(): Subsets markers

svmClassification(): svm classification

svmOptimisation(): svm parameter optimisation

show(<MAPParams>) logPosteriors() tagmMapTrain() tagmMapPredict(): The `logPosteriors` function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.

tagmMcmcTrain() tagmMcmcPredict() tagmPredict() tagmMcmcProcess(): Localisation of proteins using the TAGM MCMC method

testMSnSet(): Create a stratified 'test' MSnSet

testMarkers(): Tests marker class sizes

thetas(): Draw matrix of thetas to test

undocumented getParams,ClustRegRes-method levelPlot,ClustRegRes-method plot,ClustRegRes,missing-method show,ClustRegRes-method: Undocumented/unexported entries

zerosInBinMSnSet(): Compute the number of non-zero values in each marker classes