Package: qmtools 1.11.0

Jaehyun Joo

qmtools: Quantitative Metabolomics Data Processing Tools

The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data.

Authors:Jaehyun Joo [aut, cre], Blanca Himes [aut]

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qmtools.pdf |qmtools.html✨
qmtools/json (API)
NEWS

# Install 'qmtools' in R:
install.packages('qmtools', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/himesgroup/qmtools/issues

Datasets:
  • faahko_se - FAAH knockout LC/MS data SummarizedExperiment

On BioConductor:qmtools-1.11.0(bioc 3.21)qmtools-1.10.0(bioc 3.20)

metabolomicspreprocessingnormalizationdimensionreductionmassspectrometry

4.48 score 1 stars 5 scripts 131 downloads 22 exports 152 dependencies

Last updated 20 days agofrom:d02f681966. Checks:OK: 1 ERROR: 1 WARNING: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winWARNINGNov 18 2024
R-4.5-linuxERRORNov 18 2024
R-4.4-winWARNINGNov 18 2024
R-4.4-macWARNINGNov 18 2024
R-4.3-winWARNINGNov 18 2024
R-4.3-macWARNINGNov 18 2024

Exports:clusterFeaturescompareSamplesimputeIntensityimputeKNNnormalizeIntensitynormalizePQNplotBoxplotCorrplotMissplotReducedplotRTgroupreduceFeaturesreducePCAreducePLSDAreduceTSNEremoveBlankRatioremoveFeaturesremoveICCremoveMissremoveRSDscaleColsscaleRows

Dependencies:abindaskpassassertthatbackportsbase64encBiobaseBiocGenericsbootbroombslibcacachemcallrcarcarDataclasscliclueclustercodetoolscolorspacecowplotcpp11crayoncrosstalkcurldata.tableDelayedArraydendextendDEoptimRDerivdigestdoBydplyre1071eggevaluatefansifarverfastmapfontawesomeforeachFormulafsgclusgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegridExtragtableheatmaplyhighrhtmltoolshtmlwidgetshttrigraphIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglaekenlaterlatticelazyevallifecyclelimmalme4lmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrMsCoreUtilsmunsellnlmenloptrnnetnumDerivopensslpatchworkpbkrtestpermutepillarpkgconfigplotlyplyrprocessxpromisesproxypspurrrqapquantregR6rangerrappdirsRColorBrewerRcppRcppEigenregistryreshape2rlangrmarkdownrobustbaseS4ArraysS4VectorssassscalesseriationspSparseArraySparseMstatmodstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselecttinytexTSPUCSC.utilsutf8vcdvctrsveganVIMviridisviridisLitewebshotwithrxfunXVectoryamlzlibbioczoo

Processing quantitative metabolomics data with the qmtools package

Jaehyun Joo

Rendered fromqmtools.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2023-04-02
Started: 2022-03-17

Readme and manuals

Help Manual

Help pageTopics
Feature clusteringclusterFeatures
Sample comparisoncompareSamples
FAAH knockout LC/MS data SummarizedExperimentfaahko_se
Imputation methodsimputeIntensity imputeIntensity,ANY-method imputeIntensity,SummarizedExperiment-method
k-nearest neighbor imputationimputeKNN
Normalization methodsnormalizeIntensity normalizeIntensity,ANY-method normalizeIntensity,SummarizedExperiment-method
Probabilistic quotient normalization (PQN)normalizePQN
Box plotplotBox
Correlation plotplotCorr
Missing value plotplotMiss
Score plot of dimension-reduced dataplotReduced
Helper to visualize feature groupingplotRTgroup
Dimension reduction methodsreduceFeatures reduceFeatures,ANY-method reduceFeatures,SummarizedExperiment-method
Principal component analysis (PCA)reducePCA
Partial least squares-discriminant analysis (PLS-DA)reducePLSDA
t-distributed stochastic neighbor embedding (t-SNE)reduceTSNE
Feature Filtering based on QC/blank ratioremoveBlankRatio
Feature Filtering methodsremoveFeatures removeFeatures,ANY-method removeFeatures,SummarizedExperiment-method
Feature Filtering based on ICCremoveICC
Feature filtering based on proportions of missing valuesremoveMiss
Feature Filtering based on RSDremoveRSD
Scale along columns (samples)scaleCols
Scale along rows (features)scaleRows