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metaX (version 1.4.0)

An R package for metabolomic data analysis

Description

The package provides a integrated pipeline for mass spectrometry- based metabolomic data analysis. It includes the stages peak detection, data preprocessing, normalization, missing value imputation, univariate statistical analysis, multivariate statistical analysis such as PCA and PLS-DA, metabolite identification, pathway analysis, power analysis, feature selection and modeling, data quality assessment.

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Version

Version

1.4.0

License

LGPL-2

Maintainer

Bo Wen

Last Published

February 15th, 2017

Functions in metaX (1.4.0)

checkPvaluePlot

checkPvaluePlot
center<-

center
filterPeaks

filterPeaks
group.mzwid0<-

group.mzwid0
group.sleep<-

group.sleep
filterQCPeaks

filterQCPeaks
createModels

Create predictive models
dataClean

dataClean
metaXpipe

metaXpipe
plotMissValue

Plot missing value distribution
method<-

method
plotNetwork

Plot correlation network map
ratioPairs<-

ratioPairs
rawPeaks<-

rawPeaks
importDataFromMetaboAnalyst

importDataFromMetaboAnalyst
importDataFromQI

importDataFromQI
missingValueImpute

Missing value imputation
missValueImputeMethod<-

missValueImputeMethod
permutePLSDA

permutePLSDA
plotCorHeatmap

Plot correlation heatmap
xcmsSet.mzdiff<-

xcmsSet.mzdiff
t<-

t
selectBestComponent

Select the best component for PLS-DA
xcmsSetObj<-

xcmsSetObj
zero2NA

Convert the value <=0 to NA
xcmsSet.noise<-

xcmsSet.noise
plotPLSDA

Plot PLS-DA figure
plotPeakSumDist

Plot the total peak intensity distribution
retcor.method<-

retcor.method
xcmsSet.fitgauss<-

xcmsSet.fitgauss
retcor.plottype<-

retcor.plottype
xcmsSet.fwhm<-

xcmsSet.fwhm
xcmsSet.polarity<-

xcmsSet.polarity
xcmsSet.ppm<-

xcmsSet.ppm
addIdentInfo

Add identification result into metaXpara object
addValueNorm<-

addValueNorm
hasQC

Judge whether the data has QC samples
idres<-

idres
makeDirectory

Create directory
pathwayAnalysis

Pathway analysis
makeMetaboAnalystInput

Export a csv file which can be used for MetaboAnalyst
peakFinder

Peak detection by using XCMS package
plotCV

Plot the CV distribution of peaks in each group
plotHeatMap

Plot heatmap
powerAnalyst

Power Analysis
plotQCRLSC

Plot figures for QC-RLSC
prefix<-

prefix
plotQC

Plot the correlation change of the QC samples.
checkQCPlot

checkQCPlot
dir.case<-

dir.case
dir.ctrl<-

dir.ctrl
cor.network

Correlation network analysis
group.mzwid<-

group.mzwid
ncomp<-

ncomp
group.minsamp<-

group.minsamp
xcmsSet.nSlaves<-

xcmsSet.nSlaves
xcmsSet.peakwidth<-

xcmsSet.peakwidth
normalize

Normalisation of peak intensity
peaksData<-

peaksData
peakStat

Do the univariate and multivariate statistical analysis
plotPeakNumber

Plot the distribution of the peaks number
transformation

Data transformation
plotPeakSN

Plot the distribution of the peaks S/N
validation<-

validation
xcmsSet.profparam<-

xcmsSet.profparam
xcmsSet.max<-

xcmsSet.max
xcmsSet.integrate<-

xcmsSet.integrate
xcmsSet.prefilter<-

xcmsSet.prefilter
calcAUROC

Classical univariate ROC analysis
calcVIP

Calculate the VIP for PLS-DA
filterQCPeaksByCV

Filter peaks according to the RSD of peaks in QC samples
getPeaksTable

Get a data.frame which contained the peaksData in metaXpara
importDataFromXCMS

importDataFromXCMS
group.bw0<-

group.bw0
group.bw<-

group.bw
kfold<-

kfold
nperm<-

nperm
outdir<-

outdir
plotIntDistr

Plot the distribution of the peaks intensity
plotLoading

Plot figures for PCA/PLS-DA loadings
plotPCA

Plot PCA figure
plotPeakBox

Plot boxplot for each feature
preProcess

Pre-Processing
retcor.profStep<-

retcor.profStep
qcRlscSpan<-

qcRlscSpan
runPLSDA

runPLSDA
xcmsSet.step<-

xcmsSet.step
xcmsSet.verbose.columns<-

xcmsSet.verbose.columns
autoRemoveOutlier

Automatically detect outlier samples
bootPLSDA

Fit predictive models for PLS-DA
doQCRLSC

Using the QC samples to do the quality control-robust spline signal correction
featureSelection

Feature selection and modeling
group.max<-

group.max
group.minfrac<-

group.minfrac
metaboliteAnnotation

Metabolite identification
metaXpara-class

An S4 class to represent the parameters and data for data processing
myCalcAUROC

Classical univariate ROC analysis
myPLSDA

Perform PLS-DA analysis
plotTreeMap

Plot Phylogenies for samples
removeSample

Remove samples from the metaXpara object
plsDAPara-class

An S4 class to represent the parameters for PLS-DA analysis
reSetPeaksData

reSetPeaksData
sampleListFile<-

sampleListFile
scale<-

scale
xcmsSet.mzCenterFun<-

xcmsSet.mzCenterFun
xcmsSet.method<-

xcmsSet.method
xcmsSet.sleep<-

xcmsSet.sleep
xcmsSet.snthresh<-

xcmsSet.snthresh