Man pages for pRoloc
A unifying bioinformatics framework for spatial proteomics

addGoAnnotationsAdd GO annotations
addLegendAdds a legend
addMarkersAdds markers to the data
AnnotationParams-classClass '"AnnotationParams"'
checkFeatureNamesOverlapCheck feature names overlap
checkFvarOverlapCompare a feature variable overlap
chi2-methodsThe PCP 'chi square' method
classWeightsCalculate class weights
clustDistPairwise Distance Computation for Protein Information Sets
ClustDist-classClass '"ClustDist"'
ClustDistList-classStoring multiple ClustDist instances
defunctpRoloc Deprecated and Defunct
empPvaluesEstimate empirical p-values for Chi^2 protein correlations.
exprsToRatios-methodsCalculate all ratio pairs
fDataToUnknownUpdate a feature variable
filterBinMSnSetFilter a binary MSnSet
filterMaxMarkersRemoves class/annotation information from a matrix of...
filterMinMarkersRemoves class/annotation information from a matrix of...
filterZeroColsRemove 0 columns/rows
GenRegRes-classClass '"GenRegRes"' and '"ThetaRegRes"'
getGOFromFeaturesRetrieve GO terms for feature names
getMarkerClassesReturns the organelle classes in an 'MSnSet'
getMarkersGet the organelle markers in an 'MSnSet'
getNormDistExtract Distances from a '"ClustDistList"' object
getPredictionsReturns the predictions in an 'MSnSet'
getStockcolManage default colours and point characters
goIdToTermConvert GO ids to/from terms
highlightOnPlotHighlight features of interest on a spatial proteomics plot
knnClassificationknn classification
knnOptimisationknn parameter optimisation
knntlClassificationknn transfer learning classification
knntlOptimisationtheta parameter optimisation
ksvmClassificationksvm classification
ksvmOptimisationksvm parameter optimisation
lopimsA complete LOPIMS pipeline
makeGoSetCreates a GO feature 'MSnSet'
markerMSnSetExtract marker/unknown subsets
markersCreate a marker vector or matrix.
MartInstance-classClass '"MartInstance"'
MCMCParamsInstrastructure to store and process MCMC results
minMarkersCreates a reduced marker variable
MLearn-methodsThe 'MLearn' interface for machine learning
move2DsDisplays a spatial proteomics animation
mrkConsProfilesMarker consensus profiles
mrkHClustDraw a dendrogram of subcellular clusters
nbClassificationnb classification
nbOptimisationnb paramter optimisation
nndist-methodsNearest neighbour distances
nnetClassificationnnet classification
nnetOptimisationnnet parameter optimisation
orderGoAnnotationsOrders annotation information
orgQuantsReturns organelle-specific quantile scores
perTurboClassificationperTurbo classification
perTurboOptimisationPerTurbo parameter optimisation
phenoDiscoRuns the 'phenoDisco' algorithm.
plot2DPlot organelle assignment data and results.
plot2DsDraw 2 data sets on one PCA plot
plotDistPlots the distribution of features across fractions
plotEllipseA function to plot probabiltiy ellipses on marker PCA plots...
plsdaClassificationplsda classification
plsdaOptimisationplsda parameter optimisation
pRolocmarkersOrganelle markers
QSep-classQuantify resolution of a spatial proteomics experiment
rfClassificationrf classification
rfOptimisationsvm parameter optimisation
sampleMSnSetExtract a stratified sample of an 'MSnSet'
showGOEvidenceCodesGO Evidence Codes
SpatProtVis-classClass 'SpatProtVis'
subsetMarkersSubsets markers
svmClassificationsvm classification
svmOptimisationsvm parameter optimisation
tagm-mapLocalisation of proteins using the TAGM MAP method
tagm-mcmcLocalisation of proteins using the TAGM MCMC method
testMarkersTests marker class sizes
testMSnSetCreate a stratified 'test' 'MSnSet'
thetasDraw matrix of thetas to test
undocumentedUndocumented/unexported entries
zerosInBinMSnSetCompute the number of non-zero values in each marker classes
pRoloc documentation built on Nov. 1, 2018, 4:31 a.m.