An R package for metabolomic data analysis


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Documentation for package ‘metaX’ version 1.4.2

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A B C D F G H I K M N O P Q R S T V X Z

-- A --

addIdentInfo Add identification result into metaXpara object
addValueNorm<- addValueNorm
autoRemoveOutlier Automatically detect outlier samples

-- B --

bootPLSDA Fit predictive models for PLS-DA

-- C --

calcAUROC Classical univariate ROC analysis
calcVIP Calculate the VIP for PLS-DA
center<- center
checkPvaluePlot checkPvaluePlot
checkQCPlot checkQCPlot
cor.network Correlation network analysis
createModels Create predictive models

-- D --

dataClean dataClean
dir.case<- dir.case
dir.ctrl<- dir.ctrl
doQCRLSC Using the QC samples to do the quality control-robust spline signal correction

-- F --

featureSelection Feature selection and modeling
filterPeaks filterPeaks
filterQCPeaks filterQCPeaks
filterQCPeaksByCV Filter peaks according to the RSD of peaks in QC samples

-- G --

getPeaksTable Get a data.frame which contained the peaksData in metaXpara
group.bw0<- group.bw0
group.bw<- group.bw
group.max<- group.max
group.minfrac<- group.minfrac
group.minsamp<- group.minsamp
group.mzwid0<- group.mzwid0
group.mzwid<- group.mzwid
group.sleep<- group.sleep

-- H --

hasQC Judge whether the data has QC samples

-- I --

idres<- idres
importDataFromMetaboAnalyst importDataFromMetaboAnalyst
importDataFromQI importDataFromQI
importDataFromXCMS importDataFromXCMS

-- K --

kfold<- kfold

-- M --

makeDirectory Create directory
makeMetaboAnalystInput Export a csv file which can be used for MetaboAnalyst
metaboliteAnnotation Metabolite identification
metaXpara-class An S4 class to represent the parameters and data for data processing
metaXpipe metaXpipe
method<- method
missingValueImpute Missing value imputation
missValueImputeMethod<- missValueImputeMethod
myCalcAUROC Classical univariate ROC analysis
myPLSDA Perform PLS-DA analysis

-- N --

ncomp<- ncomp
normalize Normalisation of peak intensity
nperm<- nperm

-- O --

outdir<- outdir

-- P --

pathwayAnalysis Pathway analysis
peakFinder Peak detection by using XCMS package
peaksData<- peaksData
peakStat Do the univariate and multivariate statistical analysis
permutePLSDA permutePLSDA
plotCorHeatmap Plot correlation heatmap
plotCV Plot the CV distribution of peaks in each group
plotHeatMap Plot heatmap
plotIntDistr Plot the distribution of the peaks intensity
plotLoading Plot figures for PCA/PLS-DA loadings
plotMissValue Plot missing value distribution
plotNetwork Plot correlation network map
plotPCA Plot PCA figure
plotPeakBox Plot boxplot for each feature
plotPeakNumber Plot the distribution of the peaks number
plotPeakSN Plot the distribution of the peaks S/N
plotPeakSumDist Plot the total peak intensity distribution
plotPLSDA Plot PLS-DA figure
plotQC Plot the correlation change of the QC samples.
plotQCRLSC Plot figures for QC-RLSC
plotTreeMap Plot Phylogenies for samples
plsDAPara-class An S4 class to represent the parameters for PLS-DA analysis
powerAnalyst Power Analysis
prefix<- prefix
preProcess Pre-Processing

-- Q --

qcRlscSpan<- qcRlscSpan

-- R --

ratioPairs<- ratioPairs
rawPeaks<- rawPeaks
removeSample Remove samples from the metaXpara object
reSetPeaksData reSetPeaksData
retcor.method<- retcor.method
retcor.plottype<- retcor.plottype
retcor.profStep<- retcor.profStep
runPLSDA runPLSDA

-- S --

sampleListFile<- sampleListFile
scale<- scale
selectBestComponent Select the best component for PLS-DA

-- T --

t<- t
transformation Data transformation

-- V --

validation<- validation

-- X --

xcmsSet.fitgauss<- xcmsSet.fitgauss
xcmsSet.fwhm<- xcmsSet.fwhm
xcmsSet.integrate<- xcmsSet.integrate
xcmsSet.max<- xcmsSet.max
xcmsSet.method<- xcmsSet.method
xcmsSet.mzCenterFun<- xcmsSet.mzCenterFun
xcmsSet.mzdiff<- xcmsSet.mzdiff
xcmsSet.noise<- xcmsSet.noise
xcmsSet.nSlaves<- xcmsSet.nSlaves
xcmsSet.peakwidth<- xcmsSet.peakwidth
xcmsSet.polarity<- xcmsSet.polarity
xcmsSet.ppm<- xcmsSet.ppm
xcmsSet.prefilter<- xcmsSet.prefilter
xcmsSet.profparam<- xcmsSet.profparam
xcmsSet.sleep<- xcmsSet.sleep
xcmsSet.snthresh<- xcmsSet.snthresh
xcmsSet.step<- xcmsSet.step
xcmsSet.verbose.columns<- xcmsSet.verbose.columns
xcmsSetObj<- xcmsSetObj

-- Z --

zero2NA Convert the value <=0 to NA