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pRoloc (version 1.12.3)

A unifying bioinformatics framework for spatial proteomics

Description

This package implements pattern recognition techniques on quantitiative mass spectrometry data to infer protein sub-cellular localisation.

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Version

Version

1.12.3

License

GPL-2

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Repository

https://github.com/lgatto/pRoloc

Maintainer

Laurent Gatto

Last Published

February 15th, 2017

Functions in pRoloc (1.12.3)

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

GenRegRes-class

Class "GenRegRes" and "ThetaRegRes"

filterMinMarkers

Removes class/annotation information from a matrix of candidate markers that appear in the fData.

ClustDistList-class

Storing multiple ClustDist instances

Organelle markers

knntlOptimisation

theta parameter optimisation

checkFvarOverlap

Compare a feature variable overlap

ClustDist-class

Class "ClustDist"

filterBinMSnSet

Filter a binary MSnSet

addGoAnnotations

Add GO annotations

highlightOnPlot

Highlight features of interest on a plot2D figure

filterMaxMarkers

Removes class/annotation information from a matrix of candidate markers that appear in the fData.

SpatProtVis-class

Class SpatProtVis

Extract marker/unknown subsets

AnnotationParams-class

Class "AnnotationParams"

Remove 0 columns/rows

plsdaOptimisation

plsda parameter optimisation

The PCP 'chi square' method

exprsToRatios-methods

Calculate all ratio pairs

Update a feature variable

Manage default colours and point characters

pRoloc Deprecated and Defunct

getGOFromFeatures

Retrieve GO terms for feature names

Undocumented/unexported entries

Calculate class weights

A complete LOPIMS pipeline

Draw 2 data sets on one PCA plot

Get the organelle markers in an MSnSet

Convert GO ids to/from terms

ksvmOptimisation

ksvm parameter optimisation

Extract Distances from a "ClustDistList" object

ksvmClassification

ksvm classification

Displays a spatial proteomics animation

svmOptimisation

svm parameter optimisation

Creates a reduced marker variable

nnetOptimisation

nnet parameter optimisation

nbClassification

nb classification

rfClassification

rf classification

knnClassification

knn classification

Tests marker class sizes

Plots the distribution of features across fractions

perTurboOptimisation

PerTurbo parameter optimisation

Returns organelle-specific quantile scores

Subsets markers

knntlClassification

knn transfer learning classification

Interactive visualisation of spatial proteomics data

svmClassification

svm classification

svm parameter optimisation

showGOEvidenceCodes

GO Evidence Codes

The MLearn interface for machine learning

Pairwise Distance Computation for Protein Information Sets

getMarkerClasses

Returns the organelle classes in an 'MSnSet'

plsdaClassification

plsda classification

Plot organelle assignment data and results.

Extract a stratified sample of an MSnSet

Adds markers to the data

nb paramter optimisation

checkFeatureNamesOverlap

Check feature names overlap

nnetClassification

nnet classification

MartInstance-class

Class "MartInstance"

Nearest neighbour distances

Create a stratified 'test' MSnSet

orderGoAnnotations

Orders annotation information

perTurboClassification

perTurbo classification

Runs the phenoDisco algorithm.

Create a marker vector or matrix.

Creates a GO feature MSnSet

Draw matrix of thetas to test

Returns the predictions in an 'MSnSet'

knnOptimisation

knn parameter optimisation