A unifying bioinformatics framework for spatial proteomics

Documentation for package ‘pRoloc’ version 1.44.1

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

-- A --

addGoAnnotations	Add GO annotations
addLegend	Adds a legend
addMarkers	Adds markers to the data
andy2011params	Class '"AnnotationParams"'
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
dunkley2006params	Class '"AnnotationParams"'

-- 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
flipGoTermId	Convert GO ids to/from terms

-- 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"'
getGOEvidenceCodes	GO Evidence Codes
getGOFromFeatures	Retrieve GO terms for feature names
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"'
getStockcol	Manage default colours and point characters
getStockpch	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
goIdToTerm	Convert GO ids to/from terms
goTermToId	Convert GO ids to/from terms

-- 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 --

makeGoSet	Creates a GO feature 'MSnSet'
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 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
plsdaOptimization	plsda parameter optimisation
plsdaPrediction	plsda classification
plsdaRegularisation	plsda parameter optimisation
prettyGoTermId	Convert GO ids to/from terms
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
setStockcol	Manage default colours and point characters
setStockpch	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
showGOEvidenceCodes	GO Evidence Codes
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"'