Package: pRoloc 1.47.0
Laurent Gatto
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:
pRoloc_1.47.0.tar.gz
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pRoloc.pdf |pRoloc.html✨
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
- andy2011params - Class '"AnnotationParams"'
- dunkley2006params - Class '"AnnotationParams"'
On BioConductor:pRoloc-1.47.0(bioc 3.21)pRoloc-1.46.0(bioc 3.20)
immunooncologyproteomicsmassspectrometryclassificationclusteringqualitycontrolbioconductorproteomics-dataspatial-proteomicsvisualisation
Last updated 23 days agofrom:c501225b36. Checks:OK: 1 WARNING: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 18 2024 |
| R-4.5-win-x86_64 | WARNING | Nov 18 2024 |
| R-4.5-linux-x86_64 | WARNING | Nov 18 2024 |
| R-4.4-win-x86_64 | WARNING | Nov 18 2024 |
| R-4.4-mac-x86_64 | WARNING | Nov 18 2024 |
| R-4.4-mac-aarch64 | WARNING | Nov 18 2024 |
| R-4.3-win-x86_64 | WARNING | Nov 18 2024 |
| R-4.3-mac-x86_64 | WARNING | Nov 18 2024 |
| R-4.3-mac-aarch64 | WARNING | Nov 18 2024 |
Exports:addGoAnnotationsaddLegendaddMarkerschainscheckFeatureNamesOverlapcheckFvarOverlapchi2classWeightsclustDistClustDistListcol1col2combineThetaRegResdata1data2empPvaluesf1CountfavourPrimaryfDataToUnknownfilterBinMSnSetfilterMaxMarkersfilterMinMarkersfilterZeroColsfilterZeroRowsflipGoTermIdgetAnnotationParamsgetF1ScoresgetGOEvidenceCodesgetGOFromFeaturesgetLisacolgetMarkerClassesgetMarkersgetNormDistgetOldcolgetParamsgetPredictionsgetRegularisedParamsgetRegularizedParamsgetSeedgetStockcolgetStockpchgetUnknowncolgetUnknownpchgetWarningsgeweke_testgoIdToTermgoTermToIdhighlightOnPlothighlightOnPlot3DisMrkMatisMrkVecknnClassificationknnOptimisationknnOptimizationknnPredictionknnRegularisationknntlClassificationknntlOptimisationksvmClassificationksvmOptimisationksvmOptimizationksvmPredictionksvmRegularisationlevelPlotlogPosteriorsmakeGoSetmarkerMSnSetmcmc_burn_chainsmcmc_get_meanComponentmcmc_get_meanoutliersProbmcmc_get_outliersmcmc_pool_chainsmcmc_thin_chainsminClassScoreminMarkersMLearnmove2DsmrkConsProfilesmrkHClustmrkMatAndVecmrkMatToVecmrkVecToMatnbClassificationnbOptimisationnbOptimizationnbPredictionnbRegularisationnndistnnetClassificationnnetOptimisationnnetOptimizationnnetPredictionnnetRegularisationorderGoAnnotationsorgQuantsperTurboClassificationperTurboOptimisationperTurboOptimizationphenoDiscoplotplot2Dplot2Dmethodsplot2Dsplot3DplotConsProfilesplotDistplotEllipseplsdaClassificationplsdaOptimisationplsdaOptimizationplsdaPredictionplsdaRegularisationprettyGoTermIdpRolocmarkersqsepQSeprfClassificationrfOptimisationrfOptimizationrfPredictionrfRegularisationsampleMSnSetsetAnnotationParamssetLisacolsetOldcolsetStockcolsetStockpchsetUnknowncolsetUnknownpchshowshowGOEvidenceCodesshowMrkMatspatial2DSpatProtVissubsetMarkerssvmClassificationsvmOptimisationsvmOptimizationsvmPredictionsvmRegularisationtagmMapPredicttagmMapTraintagmMcmcPredicttagmMcmcProcesstagmMcmcTraintagmPredicttestMarkerstestMSnSetthetasunknownMSnSetzerosInBinMSnSet
Dependencies:abindaffyaffyioannotateAnnotationDbiAnnotationFilteraskpassassertthatbase64encBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocManagerBiocParallelbiomaRtBiostringsbitbit64blobbslibcachemcaretclasscliclockclueclustercodacodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tableDBIdbplyrDelayedArraydendextendDEoptimRdiagramdigestdiptestdoParalleldplyre1071evaluatefansifarverfastmapfilelockflexmixFNNfontawesomeforeachformatRfpcfsfutile.loggerfutile.optionsfuturefuture.applygbmgdatagenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggvisglobalsgluegowergridExtragtablegtoolshardhathexbinhighrhmshtmltoolshtmlwidgetshttpuvhttrhttr2hwriterigraphimputeipredIRangesisobanditeratorsjquerylibjsonliteKEGGRESTkernlabKernSmoothknitrlabelinglambda.rLaplacesDemonlaterlatticelavalazyevallifecyclelimmalistenvlpSolvelubridatemagrittrMALDIquantMASSMatrixMatrixGenericsmatrixStatsmclustmemoisemgcvmimemixtoolsmlbenchMLInterfacesModelMetricsmodeltoolsMsCoreUtilsMSnbaseMultiAssayExperimentmunsellmvtnormmzIDmzRncdf4nlmennetnumDerivopensslparallellypcaMethodspillarpkgconfigplogrplotlyplsplyrpngprabcluspreprocessCoreprettyunitspROCprodlimprogressprogressrpromisesProtGenericsproxyPSMatchpurrrQFeaturesR6randomForestrappdirsRColorBrewerRcppRcppArmadillorecipesreshape2Rhdf5librlangrmarkdownrobustbaserpartRSQLiteS4ArraysS4VectorssamplingsassscalessegmentedsfsmiscshapeshinysnowsourcetoolsSparseArraySQUAREMstatmodstringistringrSummarizedExperimentsurvivalsysthreejstibbletidyrtidyselecttimechangetimeDatetinytextzdbUCSC.utilsutf8vctrsviridisviridisLitevsnwithrxfunXMLxml2xtableXVectoryamlzlibbioc
A transfer learning algorithm for spatial proteomics
Rendered fromv05-pRoloc-transfer-learning.Rmdusingknitr::rmarkdownon Nov 18 2024.Last update: 2023-01-31
Started: 2018-07-07
Annotating spatial proteomics data
Rendered fromv04-pRoloc-goannotations.Rmdusingknitr::rmarkdownon Nov 18 2024.Last update: 2019-04-12
Started: 2018-07-07
Bayesian Analysis of Spatial Proteomics data using pRoloc
Rendered fromv03-pRoloc-bayesian.Rmdusingknitr::rmarkdownon Nov 18 2024.Last update: 2019-04-11
Started: 2018-07-07
Machine learning techniques available in pRoloc
Rendered fromv02-pRoloc-ml.Rmdusingknitr::rmarkdownon Nov 18 2024.Last update: 2019-03-14
Started: 2018-07-07
Using pRoloc for spatial proteomics data analysis
Rendered fromv01-pRoloc-tutorial.Rmdusingknitr::rmarkdownon Nov 18 2024.Last update: 2020-03-17
Started: 2018-07-07
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Add GO annotations | addGoAnnotations |
| Adds a legend | addLegend |
| Adds markers to the data | addMarkers |
| Class '"AnnotationParams"' | andy2011params AnnotationParams AnnotationParams-class class:AnnotationParams dunkley2006params 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 |
| Retrieve GO terms for feature names | getGOFromFeatures |
| 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 |
| Convert GO ids to/from terms | flipGoTermId goIdToTerm goTermToId prettyGoTermId |
| 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 |
| Creates a GO feature 'MSnSet' | makeGoSet |
| 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 consenses 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 getStockcol getStockpch getUnknowncol getUnknownpch setLisacol setOldcol setStockcol setStockpch setUnknowncol setUnknownpch |
| GO Evidence Codes | getGOEvidenceCodes showGOEvidenceCodes |
| 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 |
