All functions

AnnotationParams-class

Class "AnnotationParams"

ClustDist-class

Class "ClustDist"

ClustDistList-class

Storing multiple ClustDist instances

GenRegRes-class

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-methods

The MLearn interface for machine learning

MartInstance-class

Class "MartInstance"

QSep-class

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-methods

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

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

Undocumented/unexported entries

zerosInBinMSnSet()

Compute the number of non-zero values in each marker classes