# A unifying bioinformatics framework for spatial proteomics

## Documentation for package ‘pRoloc’ version 1.49.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C D E F G H I K L M N O P Q R S T U Z misc

## -- A --

addLegend | Adds a legend
addMarkers | Adds markers to the data
AnnotationParams | Class '"AnnotationParams"'
AnnotationParams-class | Class '"AnnotationParams"'
as.data.frame.MartInstance | Class '"MartInstance"'
as.data.frame.MartInstanceList | Class '"MartInstance"'

## -- C --

chains | Instrastructure to store and process MCMC results
checkFeatureNamesOverlap | Check feature names overlap
checkFvarOverlap | Compare a feature variable overlap
chi2 | The PCP 'chi square' method
chi2-method | The PCP 'chi square' method
chi2-methods | The PCP 'chi square' method
class::QSep | Quantify resolution of a spatial proteomics experiment
class:AnnotationParams | Class '"AnnotationParams"'
class:ClustDist | Class '"ClustDist"'
class:ClustDistList | Storing multiple ClustDist instances
class:GenRegRes | Class '"GenRegRes"' and '"ThetaRegRes"'
class:MAPParams | The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.
class:MCMCChain | Instrastructure to store and process MCMC results
class:MCMCChains | Instrastructure to store and process MCMC results
class:MCMCParams | Instrastructure to store and process MCMC results
class:MCMCSummary | Instrastructure to store and process MCMC results
class:SpatProtVis | Class 'SpatProtVis'
class:ThetaRegRes | Class '"GenRegRes"' and '"ThetaRegRes"'
classWeights | Calculate class weights
ClustDist | Class '"ClustDist"'
clustDist | Pairwise Distance Computation for Protein Information Sets
ClustDist-class | Class '"ClustDist"'
ClustDistList | Storing multiple ClustDist instances
ClustDistList-class | Storing multiple ClustDist instances
col1 | Draw 2 data sets on one PCA plot
col2 | Draw 2 data sets on one PCA plot
combineThetaRegRes | Class '"GenRegRes"' and '"ThetaRegRes"'

## -- D --

data1 | Draw 2 data sets on one PCA plot
data2 | Draw 2 data sets on one PCA plot

## -- E --

empPvalues | Estimate empirical p-values for Chi^2 protein correlations.

## -- F --

f1Count | Class '"GenRegRes"' and '"ThetaRegRes"'
f1Count-method | Class '"GenRegRes"' and '"ThetaRegRes"'
favourPrimary | Class '"GenRegRes"' and '"ThetaRegRes"'
fDataToUnknown | Update a feature variable
filterAttrs | Class '"MartInstance"'
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 | Remove 0 columns/rows
filterZeroRows | Remove 0 columns/rows

## -- G --

GenRegRes | Class '"GenRegRes"' and '"ThetaRegRes"'
GenRegRes-class | Class '"GenRegRes"' and '"ThetaRegRes"'
getAnnotationParams | Class '"AnnotationParams"'
getF1Scores | Class '"GenRegRes"' and '"ThetaRegRes"'
getF1Scores-method | Class '"GenRegRes"' and '"ThetaRegRes"'
getFilterList | Class '"MartInstance"'
getLisacol | Manage default colours and point characters
getMarkerClasses | Returns the organelle classes in an 'MSnSet'
getMarkers | Get the organelle markers in an 'MSnSet'
getMartInstanceList | Class '"MartInstance"'
getMartTab | Class '"MartInstance"'
getNormDist | Extract Distances from a '"ClustDistList"' object
getOldcol | Manage default colours and point characters
getParams | Class '"GenRegRes"' and '"ThetaRegRes"'
getParams-method | Class '"GenRegRes"' and '"ThetaRegRes"'
getParams-method | Undocumented/unexported entries
getPredictions | Returns the predictions in an 'MSnSet'
getRegularisedParams | Class '"GenRegRes"' and '"ThetaRegRes"'
getRegularisedParams-method | Class '"GenRegRes"' and '"ThetaRegRes"'
getRegularizedParams | Class '"GenRegRes"' and '"ThetaRegRes"'
getRegularizedParams-method | Class '"GenRegRes"' and '"ThetaRegRes"'
getSeed | Class '"GenRegRes"' and '"ThetaRegRes"'
getSeed-method | Class '"GenRegRes"' and '"ThetaRegRes"'
getStockbg | Manage default colours and point characters
getStockcol | Manage default colours and point characters
getStockpch | Manage default colours and point characters
getUnknownbg | Manage default colours and point characters
getUnknowncol | Manage default colours and point characters
getUnknownpch | Manage default colours and point characters
getWarnings | Class '"GenRegRes"' and '"ThetaRegRes"'
getWarnings-method | Class '"GenRegRes"' and '"ThetaRegRes"'
geweke_test | Number of outlier at each iteration of MCMC

## -- H --

highlightOnPlot | Highlight features of interest on a spatial proteomics plot
highlightOnPlot3D | Highlight features of interest on a spatial proteomics plot

## -- I --

isMrkMat | Create a marker vector or matrix.
isMrkVec | Create a marker vector or matrix.

## -- K --

knnClassification | knn classification
knnOptimisation | knn parameter optimisation
knnOptimization | knn parameter optimisation
knnPrediction | knn classification
knnRegularisation | knn parameter optimisation
knntlClassification | knn transfer learning classification
knntlOptimisation | theta parameter optimisation
ksvmClassification | ksvm classification
ksvmOptimisation | ksvm parameter optimisation
ksvmOptimization | ksvm parameter optimisation
ksvmPrediction | ksvm classification
ksvmRegularisation | ksvm parameter optimisation

## -- L --

lapply-method | Storing multiple ClustDist instances
lapply-method | Class '"MartInstance"'
length-method | Storing multiple ClustDist instances
length-method | Instrastructure to store and process MCMC results
levelPlot | Class '"GenRegRes"' and '"ThetaRegRes"'
levelPlot-method | Class '"GenRegRes"' and '"ThetaRegRes"'
levelPlot-method | Quantify resolution of a spatial proteomics experiment
levelPlot-method | Undocumented/unexported entries
logPosteriors | The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.

## -- M --

MAPParams | The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.
MAPParams-class | The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.
markerMSnSet | Extract marker/unknown subsets
markers | Create a marker vector or matrix.
MartInstance | Class '"MartInstance"'
MartInstance-class | Class '"MartInstance"'
MartInstanceList | Class '"MartInstance"'
MartInstanceList-class | Class '"MartInstance"'
MCMCChain | Instrastructure to store and process MCMC results
MCMCChain-class | Instrastructure to store and process MCMC results
MCMCChains | Instrastructure to store and process MCMC results
MCMCChains-class | Instrastructure to store and process MCMC results
MCMCParams-class | Instrastructure to store and process MCMC results
MCMCSummary | Instrastructure to store and process MCMC results
MCMCSummary-class | Instrastructure to store and process MCMC results
MCMCSummary-class. | Instrastructure to store and process MCMC results
mcmc_burn_chains | Number of outlier at each iteration of MCMC
mcmc_get_meanComponent | Number of outlier at each iteration of MCMC
mcmc_get_meanoutliersProb | Number of outlier at each iteration of MCMC
mcmc_get_outliers | Number of outlier at each iteration of MCMC
mcmc_pool_chains | Number of outlier at each iteration of MCMC
mcmc_thin_chains | Number of outlier at each iteration of MCMC
minMarkers | Creates a reduced marker variable
mixing_posterior_check | Model calibration plots
MLearn-method | The 'MLearn' interface for machine learning
MLearnMSnSet | The 'MLearn' interface for machine learning
move2Ds | Displays a spatial proteomics animation
mrkConsProfiles | Marker consensus profiles
mrkEncoding | Create a marker vector or matrix.
mrkHClust | Draw a dendrogram of subcellular clusters
mrkMatAndVec | Create a marker vector or matrix.
mrkMatToVec | Create a marker vector or matrix.
mrkVecToMat | Create a marker vector or matrix.
MSnSetMLean | The 'MLearn' interface for machine learning

## -- N --

names-method | Storing multiple ClustDist instances
names-method | Quantify resolution of a spatial proteomics experiment
names<--method | Storing multiple ClustDist instances
names<--method | Quantify resolution of a spatial proteomics experiment
nbClassification | nb classification
nbOptimisation | nb paramter optimisation
nbOptimization | nb paramter optimisation
nbPrediction | nb classification
nbRegularisation | nb paramter optimisation
nDatasets | Class '"MartInstance"'
nicheMeans2D | Uncertainty plot organelle means
nndist | Nearest neighbour distances
nndist-method | Nearest neighbour distances
nndist-methods | Nearest neighbour distances
nnetClassification | nnet classification
nnetOptimisation | nnet parameter optimisation
nnetOptimization | nnet parameter optimisation
nnetPrediction | nnet classification
nnetRegularisation | nnet parameter optimisation

## -- O --

orderGoAnnotations | Orders annotation information
orgQuants | Returns organelle-specific quantile scores

## -- P --

perTurboClassification | perTurbo classification
perTurboOptimisation | PerTurbo parameter optimisation
perTurboOptimization | PerTurbo parameter optimisation
phenoDisco | Runs the 'phenoDisco' algorithm.
plot-method | Class '"ClustDist"'
plot-method | Storing multiple ClustDist instances
plot-method | Class '"GenRegRes"' and '"ThetaRegRes"'
plot-method | Quantify resolution of a spatial proteomics experiment
plot-method | Class 'SpatProtVis'
plot-method | Number of outlier at each iteration of MCMC
plot-method | Undocumented/unexported entries
plot2D | Plot organelle assignment data and results.
plot2Dmethods | Plot organelle assignment data and results.
plot2Ds | Draw 2 data sets on one PCA plot
plot3D-method | Plot organelle assignment data and results.
plotConsProfiles | Plot marker consensus 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
plsdaOptimization | plsda parameter optimisation
plsdaPrediction | plsda classification
plsdaRegularisation | plsda parameter optimisation
pRolocmarkers | Organelle markers

## -- Q --

QSep | Quantify resolution of a spatial proteomics experiment
qsep | Quantify resolution of a spatial proteomics experiment
QSep-class | Quantify resolution of a spatial proteomics experiment

## -- R --

rfClassification | rf classification
rfOptimisation | svm parameter optimisation
rfOptimization | svm parameter optimisation
rfPrediction | rf classification
rfRegularisation | svm parameter optimisation

## -- S --

sampleMSnSet | Extract a stratified sample of an 'MSnSet'
sapply-method | Storing multiple ClustDist instances
sapply-method | Class '"MartInstance"'
setAnnotationParams | Class '"AnnotationParams"'
setLisacol | Manage default colours and point characters
setOldcol | Manage default colours and point characters
setStockbg | Manage default colours and point characters
setStockcol | Manage default colours and point characters
setStockpch | Manage default colours and point characters
setUnknownbg | Manage default colours and point characters
setUnknowncol | Manage default colours and point characters
setUnknownpch | Manage default colours and point characters
show-method | Class '"AnnotationParams"'
show-method | Class '"ClustDist"'
show-method | Storing multiple ClustDist instances
show-method | Class '"GenRegRes"' and '"ThetaRegRes"'
show-method | Instrastructure to store and process MCMC results
show-method | Class '"MartInstance"'
show-method | Quantify resolution of a spatial proteomics experiment
show-method | Class 'SpatProtVis'
show-method | The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.
show-method | Undocumented/unexported entries
showMrkMat | Create a marker vector or matrix.
spatial2D | Uncertainty plot in localisation probabilities
SpatProtVis | Class 'SpatProtVis'
SpatProtVis-class | Class 'SpatProtVis'
subsetMarkers | Subsets markers
summary-method | Quantify resolution of a spatial proteomics experiment
svmClassification | svm classification
svmOptimisation | svm parameter optimisation
svmOptimization | svm parameter optimisation
svmPrediction | svm classification
svmRegularisation | svm parameter optimisation

## -- T --

tagmMapPredict | The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.
tagmMapTrain | The 'logPosteriors' function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.
tagmMcmcPredict | Localisation of proteins using the TAGM MCMC method
tagmMcmcProcess | Localisation of proteins using the TAGM MCMC method
tagmMcmcTrain | Localisation of proteins using the TAGM MCMC method
tagmPredict | Localisation of proteins using the TAGM MCMC method
testMarkers | Tests marker class sizes
testMSnSet | Create a stratified 'test' 'MSnSet'
ThetaRegRes | Class '"GenRegRes"' and '"ThetaRegRes"'
ThetaRegRes-class | Class '"GenRegRes"' and '"ThetaRegRes"'
thetas | Draw matrix of thetas to test

## -- U --

undocumented | Undocumented/unexported entries
unknownMSnSet | Extract marker/unknown subsets

## -- Z --

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

## -- misc --

.MCMCChain | Instrastructure to store and process MCMC results
.MCMCChains | Instrastructure to store and process MCMC results
.MCMCParams | Instrastructure to store and process MCMC results
.MCMCSummary | Instrastructure to store and process MCMC results
[-method | Storing multiple ClustDist instances
[-method | Instrastructure to store and process MCMC results
[-method | Class '"MartInstance"'
[[-method | Storing multiple ClustDist instances
[[-method | Instrastructure to store and process MCMC results
[[-method | Class '"MartInstance"'