class:AnnotationParams AnnotationParams AnnotationParams-class show,AnnotationParams-method AnnotationParams setAnnotationParams getAnnotationParams dunkley2006params andy2011params
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Class "AnnotationParams" |
ClustDist class:ClustDist ClustDist-class plot,ClustDist,MSnSet-method show,ClustDist-method
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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
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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
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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>)
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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
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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
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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
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Quantify resolution of a spatial proteomics experiment |
SpatProtVis()
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Class SpatProtVis |
addGoAnnotations()
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Add GO annotations |
addLegend()
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Adds a legend |
addMarkers()
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Adds markers to the data |
checkFeatureNamesOverlap()
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Check feature names overlap |
checkFvarOverlap()
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Compare a feature variable overlap |
chi2 chi2-methods chi2,matrix,matrix-method chi2,matrix,numeric-method chi2,numeric,matrix-method chi2,numeric,numeric-method
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The PCP 'chi square' method |
classWeights()
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Calculate class weights |
clustDist()
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Pairwise Distance Computation for Protein Information Sets |
empPvalues()
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Estimate empirical p-values for \(Chi^2\) protein correlations. |
fDataToUnknown()
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Update a feature variable |
filterBinMSnSet()
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Filter a binary MSnSet |
filterMaxMarkers()
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Removes class/annotation information from a matrix of candidate markers that appear in the fData . |
filterMinMarkers()
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Removes class/annotation information from a matrix of candidate markers that appear in the fData . |
filterZeroCols() filterZeroRows()
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Remove 0 columns/rows |
getGOFromFeatures()
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Retrieve GO terms for feature names |
getMarkerClasses()
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Returns the organelle classes in an 'MSnSet' |
getMarkers()
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Get the organelle markers in an MSnSet |
getNormDist()
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Extract Distances from a "ClustDistList" object |
getPredictions()
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Returns the predictions in an 'MSnSet' |
setLisacol() getLisacol() getOldcol() setOldcol() getStockcol() setStockcol() getStockpch() setStockpch() getUnknowncol() setUnknowncol() getUnknownpch() setUnknownpch()
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Manage default colours and point characters |
goIdToTerm() goTermToId() flipGoTermId() prettyGoTermId()
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Convert GO ids to/from terms |
highlightOnPlot() highlightOnPlot3D()
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Highlight features of interest on a spatial proteomics plot |
knnClassification()
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knn classification |
knnOptimisation()
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knn parameter optimisation |
knntlClassification()
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knn transfer learning classification |
knntlOptimisation()
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theta parameter optimisation |
ksvmClassification()
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ksvm classification |
ksvmOptimisation()
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ksvm parameter optimisation |
makeGoSet()
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Creates a GO feature MSnSet |
markerMSnSet() unknownMSnSet()
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Extract marker/unknown subsets |
mrkVecToMat() mrkMatToVec() mrkMatAndVec() showMrkMat() isMrkMat() isMrkVec() mrkEncoding()
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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>)
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Number of outlier at each iteration of MCMC |
minMarkers()
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Creates a reduced marker variable |
mixing_posterior_check()
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Model calibration plots |
move2Ds()
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Displays a spatial proteomics animation |
mrkConsProfiles()
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Marker consensus profiles |
mrkHClust()
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Draw a dendrogram of subcellular clusters |
nbClassification()
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nb classification |
nbOptimisation()
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nb paramter optimisation |
nicheMeans2D()
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Uncertainty plot organelle means |
nndist-methods nndist,matrix,matrix-method nndist,matrix,missing-method nndist,MSnSet,missing-method nndist
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Nearest neighbour distances |
nnetClassification()
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nnet classification |
nnetOptimisation()
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nnet parameter optimisation |
orderGoAnnotations()
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Orders annotation information |
orgQuants()
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Returns organelle-specific quantile scores |
pRolocmarkers()
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Organelle markers |
perTurboClassification()
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perTurbo classification |
perTurboOptimisation()
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PerTurbo parameter optimisation |
phenoDisco()
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Runs the phenoDisco algorithm. |
plot2D() plot3D(<MSnSet>)
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Plot organelle assignment data and results. |
plot2Ds()
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Draw 2 data sets on one PCA plot |
plotConsProfiles()
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Plot marker consenses profiles. |
plotDist()
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Plots the distribution of features across fractions |
plotEllipse()
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A function to plot probabiltiy ellipses on marker PCA plots to visualise and assess TAGM models. |
plsdaClassification()
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plsda classification |
plsdaOptimisation()
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plsda parameter optimisation |
rfClassification()
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rf classification |
rfOptimisation()
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svm parameter optimisation |
sampleMSnSet()
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Extract a stratified sample of an MSnSet |
showGOEvidenceCodes() getGOEvidenceCodes()
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GO Evidence Codes |
spatial2D()
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Uncertainty plot in localisation probabilities |
subsetMarkers()
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Subsets markers |
svmClassification()
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svm classification |
svmOptimisation()
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svm parameter optimisation |
show(<MAPParams>) logPosteriors() tagmMapTrain() tagmMapPredict()
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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()
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Localisation of proteins using the TAGM MCMC method |
testMSnSet()
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Create a stratified 'test' MSnSet |
testMarkers()
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Tests marker class sizes |
thetas()
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Draw matrix of thetas to test |
undocumented getParams,ClustRegRes-method levelPlot,ClustRegRes-method plot,ClustRegRes,missing-method show,ClustRegRes-method
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Undocumented/unexported entries |
zerosInBinMSnSet()
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Compute the number of non-zero values in each marker classes |