Function reference • metabolyseR

Analysis S4 classes

Analysis-class: Analysis S4 class

AnalysisData-class: AnalysisData S4 class

metabolyse() reAnalyse(): Perform an analysis

analysisData(): AnalysisData class constructor

dat() `dat<-`() sinfo() `sinfo<-`() raw() `raw<-`() preTreated() `preTreated<-`() features() nSamples() nFeatures() analysisResults(): AnalysisData and Analysis class accessors

clsAdd() clsArrange() clsAvailable() clsExtract() clsRemove() clsRename() clsReplace(): Sample meta information wrangling

Analysis parameters

AnalysisParameters-class: AnalysisParameters S4 class

analysisElements(): Analysis elements

analysisParameters(): Create an AnalysisParameters S4 class object

parameters() `parameters<-`(): Get or set analysis parameters

`changeParameter<-`(): Change analysis parameters

parseParameters() exportParameters(): Parse/export analysis parameters

preTreatmentElements() preTreatmentMethods() preTreatmentParameters(): Pre-treatment parameters

modellingMethods() modellingParameters(): Modelling parameters

correlationsParameters(): Correlations parameters

Pre-treatment

aggregateMean() aggregateMedian() aggregateSum(): Sample aggregation

correctionCenter(): Batch/block correction

imputeAll() imputeClass(): Missing data imputation

keepClasses() keepFeatures() keepSamples(): Keep samples, classes or features

occupancyMaximum() occupancyMinimum(): Feature occupancy filtering

QCimpute() QCoccupancy() QCremove() QCrsdFilter(): Quality control (QC) sample treatments

removeClasses() removeFeatures() removeSamples(): Remove samples, classes or features

transformArcSine() transformAuto() transformCenter() transformLevel() transformLn() transformLog10() transformPareto() transformPercent() transformRange() transformSQRT() transformTICnorm() transformVast(): Scaling, transformation and normalisation methods

Modelling

RandomForest-class: RandomForest S4 class

Univariate-class: Univariate S4 class

randomForest(): Random forest

anova(): ANOVA

ttest(): Welch's t-test

linearRegression(): Linear regression

binaryComparisons() mtry() type() response() metrics() predictions() importanceMetrics() importance() proximity() explanatoryFeatures(): Modelling accessor methods

tune(): Tune random forest parameters

mds(): Multidimensional scaling (MDS)

roc(): Receiver-operator characteristic (ROC) curves

predict(): Predict random forest model responses

Correlations

correlations(): Feature correlation analysis

Plotting

plotFeature(): Plot a feature

plotOccupancy(): Plot class occupancy distributions

plotRSD(): Plot RSD distributions

plotTIC(): Plot sample total ion counts

plotPCA(): Principle Component Analysis plot

plotLDA(): Principle Component - Linear Discriminant Analysis plot

plotUnsupervisedRF(): Unsupervised random forest MDS plot

plotSupervisedRF(): Supervised random forest MDS plot

plotMDS(): Multidimensional scaling (MDS) plot

plotROC(): Plot receiver operator characteristic (ROC) curves

plotMetrics(): Plot model performance metrics

plotImportance(): Plot feature importance

plotExplanatoryHeatmap(): Heatmap plot of explantory features

Miscellaneous

bindRows(): Bind AnalysisData objects by row

split(): Split an AnalysisData object

rsd(): Calculate feature relative standard deviations

occupancy(): Calculate feature class occupancies