Package: pRoloc 1.53.0

Lisa Breckels

pRoloc: A unifying bioinformatics framework for spatial proteomics

The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation.

Authors:Laurent Gatto [aut], Lisa Breckels [aut, cre], Thomas Burger [ctb], Samuel Wieczorek [ctb], Charlotte Hutchings [ctb], Oliver Crook [aut]

pRoloc_1.53.0.tar.gz
pRoloc_1.53.0.zip(r-4.7)pRoloc_1.53.0.zip(r-4.6)pRoloc_1.53.0.zip(r-4.5)
pRoloc_1.53.0.tgz(r-4.6-x86_64)pRoloc_1.53.0.tgz(r-4.6-arm64)pRoloc_1.53.0.tgz(r-4.5-x86_64)pRoloc_1.53.0.tgz(r-4.5-arm64)
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pRoloc_1.53.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
pRoloc/json (API)
NEWS

# Install 'pRoloc' in R:

install.packages('pRoloc', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lgatto/proloc/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On BioConductor:pRoloc-1.53.0(bioc 3.24)pRoloc-1.52.0(bioc 3.23)

immunooncology proteomics massspectrometry classification clustering qualitycontrol bioconductor proteomics-data spatial-proteomics visualisation openblas cpp

10.42 score 16 stars 2 packages 106 scripts 777 downloads 12 mentions 155 exports 219 dependencies

Last updated from:7dc0783101. Checks:12 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	335
linux-devel-arm64	WARNING	663
linux-devel-x86_64	WARNING	777
source / vignettes	OK	540
linux-release-arm64	WARNING	642
linux-release-x86_64	WARNING	633
macos-release-arm64	WARNING	541
macos-release-x86_64	WARNING	764
macos-oldrel-arm64	WARNING	517
macos-oldrel-x86_64	WARNING	963
windows-devel	WARNING	831
windows-release	WARNING	923
windows-oldrel	WARNING	734
wasm-release	OK	282

Exports:addGoAnnotations addLegend addMarkers chains checkFeatureNamesOverlap checkFvarOverlap chi2 classWeights clustDist ClustDistList col1 col2 combineThetaRegRes data1 data2 empPvalues f1Count favourPrimary fDataToUnknown filterBinMSnSet filterMaxMarkers filterMinMarkers filterZeroCols filterZeroRows flipGoTermId getAnnotationParams getF1Scores getGOEvidenceCodes getGOFromFeatures getLisacol getMarkerClasses getMarkers getNormDist getOldcol getParams getPredictions getRegularisedParams getRegularizedParams getSeed getStockbg getStockcol getStockpch getUnknownbg getUnknowncol getUnknownpch getWarnings geweke_test goIdToTerm goTermToId highlightOnPlot highlightOnPlot3D isMrkMat isMrkVec knnClassification knnOptimisation knnOptimization knnPrediction knnRegularisation knntlClassification knntlOptimisation ksvmClassification ksvmOptimisation ksvmOptimization ksvmPrediction ksvmRegularisation levelPlot logPosteriors makeGoSet markerMSnSet mcmc_burn_chains mcmc_get_meanComponent mcmc_get_meanoutliersProb mcmc_get_outliers mcmc_pool_chains mcmc_thin_chains minClassScore minMarkers MLearn move2Ds mrkConsProfiles mrkHClust mrkMatAndVec mrkMatToVec mrkVecToMat nbClassification nbOptimisation nbOptimization nbPrediction nbRegularisation nndist nnetClassification nnetOptimisation nnetOptimization nnetPrediction nnetRegularisation orderGoAnnotations orgQuants perTurboClassification perTurboOptimisation perTurboOptimization phenoDisco plot plot2D plot2Dmethods plot2Ds plot3D plotConsProfiles plotDist plotEllipse plsdaClassification plsdaOptimisation plsdaOptimization plsdaPrediction plsdaRegularisation prettyGoTermId pRolocmarkers qsep QSep rfClassification rfOptimisation rfOptimization rfPrediction rfRegularisation sampleMSnSet setAnnotationParams setLisacol setOldcol setStockbg setStockcol setStockpch setUnknownbg setUnknowncol setUnknownpch show showGOEvidenceCodes showMrkMat spatial2D SpatProtVis subsetMarkers svmClassification svmOptimisation svmOptimization svmPrediction svmRegularisation tagmMapPredict tagmMapTrain tagmMcmcPredict tagmMcmcProcess tagmMcmcTrain tagmPredict testMarkers testMSnSet thetas unknownMSnSet zerosInBinMSnSet

Dependencies:abind affy affyio annotate AnnotationDbi AnnotationFilter askpass assertthat base64enc BH Biobase BiocBaseUtils BiocFileCache BiocGenerics biocmake BiocManager BiocParallel biomaRt Biostrings bit bit64 blob bslib cachem caret class cli clock clue cluster coda codetools colorspace commonmark cpp11 crayon crosstalk curl data.table DBI dbplyr DelayedArray dendextend DEoptimR diagram digest diptest dir.expiry doParallel dplyr e1071 evaluate farver fastmap filelock flexmix FNN fontawesome foreach formatR fpc fs futile.logger futile.options future future.apply gbm gdata genefilter generics GenomicRanges ggplot2 ggvis globals glue gower gridExtra gtable gtools hardhat hexbin highr hms htmltools htmlwidgets httpuv httr httr2 hwriter igraph impute ipred IRanges isoband iterators jquerylib jsonlite KEGGREST kernlab KernSmooth knitr labeling lambda.r LaplacesDemon later lattice lava lazyeval lifecycle limma listenv lpSolve lubridate magrittr MALDIquant MASS Matrix MatrixGenerics matrixStats mclust memoise MetaboCoreUtils mime mixtools mlbench MLInterfaces ModelMetrics modeltools MsCoreUtils MSnbase MultiAssayExperiment mvtnorm mzID mzR ncdf4 nlme nnet numDeriv openssl otel parallelly pcaMethods pillar pkgconfig plotly pls plyr png prabclus preprocessCore prettyunits pROC prodlim progress progressr promises ProtGenerics proxy PSMatch PTMods purrr QFeatures R6 randomForest rappdirs RColorBrewer Rcpp RcppArmadillo recipes reshape2 Rhdf5lib rlang rmarkdown robustbase rpart RSQLite S4Arrays S4Vectors S7 sampling sass scales segmented Seqinfo sfsmisc shape shiny snow sourcetools SparseArray sparsevctrs Spectra SQUAREM statmod stringi stringr SummarizedExperiment survival sys threejs tibble tidyr tidyselect timechange timeDate tinytex tzdb utf8 vctrs viridis viridisLite vsn withr xfun XML xml2 xtable XVector yaml

A transfer learning algorithm for spatial proteomics

Lisa M. Breckels, Laurent Gatto

Rendered fromv05-pRoloc-transfer-learning.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-10-14
Started: 2018-07-07

Bayesian Analysis of Spatial Proteomics data using pRoloc

Oliver Crook, Laurent Gatto

Rendered fromv03-pRoloc-bayesian.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2019-04-11
Started: 2018-07-07

Machine learning techniques available in pRoloc

Laurent Gatto

Rendered fromv02-pRoloc-ml.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2019-03-14
Started: 2018-07-07

Using pRoloc for spatial proteomics data analysis

Lisa M. Breckels, Laurent Gatto

Rendered fromv01-pRoloc-tutorial.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-03-27
Started: 2018-07-07

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Adds a legend	addLegend
Adds markers to the data	addMarkers
Class '"AnnotationParams"'	AnnotationParams AnnotationParams-class class:AnnotationParams getAnnotationParams setAnnotationParams show,AnnotationParams-method
Check feature names overlap	checkFeatureNamesOverlap
Compare a feature variable overlap	checkFvarOverlap
The PCP 'chi square' method	chi2 chi2,matrix,matrix-method chi2,matrix,numeric-method chi2,numeric,matrix-method chi2,numeric,numeric-method chi2-methods
Calculate class weights	classWeights
Pairwise Distance Computation for Protein Information Sets	clustDist
Class '"ClustDist"'	class:ClustDist ClustDist ClustDist-class plot,ClustDist,MSnSet-method show,ClustDist-method
Storing multiple ClustDist instances	class:ClustDistList ClustDistList ClustDistList-class lapply,ClustDistList-method length,ClustDistList-method names,ClustDistList-method names<-,ClustDistList,ANY-method plot,ClustDistList,missing-method sapply,ClustDistList-method show,ClustDistList-method [,ClustDistList,ANY,ANY,ANY-method [,ClustDistList,ANY,missing,missing-method [[,ClustDistList,ANY,ANY-method [[,ClustDistList,ANY,missing-method
Estimate empirical p-values for Chi^2 protein correlations.	empPvalues
Update a feature variable	fDataToUnknown
Filter a binary MSnSet	filterBinMSnSet
Removes class/annotation information from a matrix of candidate markers that appear in the 'fData'.	filterMaxMarkers
Removes class/annotation information from a matrix of candidate markers that appear in the 'fData'.	filterMinMarkers
Remove 0 columns/rows	filterZeroCols filterZeroRows
Class '"GenRegRes"' and '"ThetaRegRes"'	class:GenRegRes class:ThetaRegRes combineThetaRegRes f1Count f1Count,GenRegRes-method f1Count,ThetaRegRes-method favourPrimary GenRegRes GenRegRes-class getF1Scores getF1Scores,GenRegRes-method getF1Scores,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 ThetaRegRes ThetaRegRes-class
Returns the organelle classes in an 'MSnSet'	getMarkerClasses
Get the organelle markers in an 'MSnSet'	getMarkers
Extract Distances from a '"ClustDistList"' object	getNormDist
Returns the predictions in an 'MSnSet'	getPredictions
Highlight features of interest on a spatial proteomics plot	highlightOnPlot highlightOnPlot3D
knn classification	knnClassification knnPrediction
knn parameter optimisation	knnOptimisation knnOptimization knnRegularisation
knn transfer learning classification	knntlClassification
theta parameter optimisation	knntlOptimisation
ksvm classification	ksvmClassification ksvmPrediction
ksvm parameter optimisation	ksvmOptimisation ksvmOptimization ksvmRegularisation
The `logPosteriors` function can be used to extract the log-posteriors at each iteration of the EM algorithm to check for convergence.	class:MAPParams logPosteriors MAPParams MAPParams-class show,MAPParams-method tagmMapPredict tagmMapTrain
Extract marker/unknown subsets	markerMSnSet unknownMSnSet
Class '"MartInstance"'	as.data.frame.MartInstance as.data.frame.MartInstanceList filterAttrs getFilterList getMartInstanceList getMartTab lapply,MartInstanceList,ANY-method lapply,MartInstanceList-method MartInstance MartInstance-class MartInstanceList MartInstanceList-class nDatasets sapply,MartInstanceList,ANY-method sapply,MartInstanceList-method show,MartInstance-method [,MartInstanceList,ANY,ANY,ANY-method [,MartInstanceList,ANY,ANY-method [,MartInstanceList-method [[,MartInstanceList,ANY,ANY-method [[,MartInstanceList-method
Number of outlier at each iteration of MCMC	geweke_test mcmc_burn_chains mcmc_get_meanComponent mcmc_get_meanoutliersProb mcmc_get_outliers mcmc_pool_chains mcmc_thin_chains plot,MCMCParams,character-method
Instrastructure to store and process MCMC results	.MCMCChain .MCMCChains .MCMCParams .MCMCSummary chains class:MCMCChain class:MCMCChains class:MCMCParams class:MCMCSummary length,MCMCChains-method length,MCMCParams-method MCMCChain MCMCChain-class MCMCChains MCMCChains-class MCMCParams-class MCMCSummary MCMCSummary-class MCMCSummary-class. show,ComponentParam-method show,MCMCChain-method show,MCMCChains-method show,MCMCParams-method [,MCMCChains,ANY,ANY,ANY-method [,MCMCParams,ANY,ANY,ANY-method [[,MCMCChains,ANY,ANY-method [[,MCMCParams,ANY,ANY-method
Creates a reduced marker variable	minMarkers
Model calibration plots	mixing_posterior_check
The 'MLearn' interface for machine learning	MLearn,formula,MSnSet,clusteringSchema,missing-method MLearn,formula,MSnSet,learnerSchema,numeric-method MLearn,formula,MSnSet,learnerSchema,xvalSpec-method MLearnMSnSet MSnSetMLean
Displays a spatial proteomics animation	move2Ds
Marker consensus profiles	mrkConsProfiles
Draw a dendrogram of subcellular clusters	mrkHClust
Create a marker vector or matrix.	isMrkMat isMrkVec markers mrkEncoding mrkMatAndVec mrkMatToVec mrkVecToMat showMrkMat
nb classification	nbClassification nbPrediction
nb paramter optimisation	nbOptimisation nbOptimization nbRegularisation
Uncertainty plot organelle means	nicheMeans2D
Nearest neighbour distances	nndist nndist,matrix,matrix-method nndist,matrix,missing-method nndist,MSnSet,missing-method nndist-methods
nnet classification	nnetClassification nnetPrediction
nnet parameter optimisation	nnetOptimisation nnetOptimization nnetRegularisation
Orders annotation information	orderGoAnnotations
Returns organelle-specific quantile scores	orgQuants
perTurbo classification	perTurboClassification
PerTurbo parameter optimisation	perTurboOptimisation perTurboOptimization
Runs the 'phenoDisco' algorithm.	phenoDisco
Plot organelle assignment data and results.	plot2D plot2Dmethods plot3D,MSnSet-method
Draw 2 data sets on one PCA plot	col1 col2 data1 data2 plot2Ds
Plot marker consensus profiles.	plotConsProfiles
Plots the distribution of features across fractions	plotDist
A function to plot probabiltiy ellipses on marker PCA plots to visualise and assess TAGM models.	plotEllipse
plsda classification	plsdaClassification plsdaPrediction
plsda parameter optimisation	plsdaOptimisation plsdaOptimization plsdaRegularisation
Organelle markers	pRolocmarkers
Quantify resolution of a spatial proteomics experiment	class::QSep levelPlot,QSep-method names,QSep-method names<-,QSep,character-method plot,QSep,missing-method plot,QSep-method QSep qsep QSep-class show,QSep-method summary,QSep-method
rf classification	rfClassification rfPrediction
svm parameter optimisation	rfOptimisation rfOptimization rfRegularisation
Extract a stratified sample of an 'MSnSet'	sampleMSnSet
Manage default colours and point characters	getLisacol getOldcol getStockbg getStockcol getStockpch getUnknownbg getUnknowncol getUnknownpch setLisacol setOldcol setStockbg setStockcol setStockpch setUnknownbg setUnknowncol setUnknownpch
Uncertainty plot in localisation probabilities	spatial2D
Class 'SpatProtVis'	class:SpatProtVis plot,SpatProtVis,missing-method show,SpatProtVis-method SpatProtVis SpatProtVis-class
Subsets markers	subsetMarkers
svm classification	svmClassification svmPrediction
svm parameter optimisation	svmOptimisation svmOptimization svmRegularisation
Localisation of proteins using the TAGM MCMC method	tagmMcmcPredict tagmMcmcProcess tagmMcmcTrain tagmPredict
Tests marker class sizes	testMarkers
Create a stratified 'test' 'MSnSet'	testMSnSet
Draw matrix of thetas to test	thetas
Undocumented/unexported entries	getParams,ClustRegRes-method levelPlot,ClustRegRes-method plot,ClustRegRes,missing-method show,ClustRegRes-method undocumented
Compute the number of non-zero values in each marker classes	zerosInBinMSnSet