Package: metaboprep 1.0.1

David Hughes

metaboprep: Metabolomics data preparation and processing pipeline

Reads in raw Metabolon and Nightingale xls sheets and aids in data preparation of all metabolomics data sets.

Authors:Laura Corbin [aut], David Hughes [aut, cre]

metaboprep_1.0.1.tar.gz
metaboprep_1.0.1.zip(r-4.5)metaboprep_1.0.1.zip(r-4.4)metaboprep_1.0.1.zip(r-4.3)
metaboprep_1.0.1.tgz(r-4.4-any)metaboprep_1.0.1.tgz(r-4.3-any)
metaboprep_1.0.1.tar.gz(r-4.5-noble)metaboprep_1.0.1.tar.gz(r-4.4-noble)
metaboprep_1.0.1.tgz(r-4.4-emscripten)metaboprep_1.0.1.tgz(r-4.3-emscripten)
metaboprep.pdf |metaboprep.html✨
metaboprep/json (API)

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

Peer review:

Bug tracker:https://github.com/remlapmot/metaboprep/issues

Datasets:
  • ng_anno - Nightingale Health metabolomics annotation data set

On CRAN:

41 exports 2.00 score 88 dependencies 7 scripts

Last updated 7 months agofrom:ee1cdc7579 (on suggestions). Checks:OK: 1 NOTE: 6. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 02 2024
R-4.5-winNOTEOct 02 2024
R-4.5-linuxNOTEOct 02 2024
R-4.4-winNOTEOct 02 2024
R-4.4-macNOTEOct 02 2024
R-4.3-winNOTEOct 02 2024
R-4.3-macNOTEOct 02 2024

Exports:batch_normalizationcramerVeval.power.binaryeval.power.binary.imbalancedeval.power.contfeature_plotsfeature.describefeature.missingnessfeature.outliersfeature.sum.statsfeature.tree.independencefind.cont.effect.sizes.2.simfind.PA.effect.sizes.2.simgenerate_reportgreedy.pairwise.n.filterid.outliersmake.cor.matrixmake.treemedian_imputemet2batchmissingness.summultivariate.anovaoutlier.matrixoutlier.summaryoutlierspc.and.outlierspca.factor.analysispcapairs_bymooseperform.metabolite.qcread.in.metabolonread.in.nightingalerntransformrun.cont.power.make.plotrun.pa.imbalanced.power.make.plotsam.missingness.exclusionsample.missingnesssample.outlierssample.sum.statstotal.peak.areatree_and_independent_featuresvariable.by.factor

Dependencies:abindbackportsBiobaseBiocGenericsbootbroomcarcarDatacellrangerclicolorspacecorrplotcowplotcpp11crayonDerivdoBydplyrevaluatefansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifglueGPArotationgridExtragtablehighrhmsisobandknitrlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamnormtmodelrmunsellnFactorsnlmenloptrnnetnumDerivpbkrtestpcaMethodspillarpkgconfigpolynomprettyunitsprogresspsychpurrrpwrquantregR6RColorBrewerRcppRcppEigenreadxlrematchrlangrstatixscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
median batch normalizationbatch_normalization
Cramer's V (phi)cramerV
Estimate power for a binary variableeval.power.binary
Estimate power for a binary variable in an imbalanced designeval.power.binary.imbalanced
estimate power for continuous variableeval.power.cont
feature plots to filefeature_plots
summary statistics for featuresfeature.describe
estimate feature missingnessfeature.missingness
outlier sample count for a featuresfeature.outliers
feature summary statisticsfeature.sum.stats
identify independent featuresfeature.tree.independence
identify continuos trait effect sizesfind.cont.effect.sizes.2.sim
identify effect sizesfind.PA.effect.sizes.2.sim
generate metaboprep summary html reportgenerate_report
greedy selectiongreedy.pairwise.n.filter
identify outliersid.outliers
correlation matrixmake.cor.matrix
generate a hclust dendrogrammake.tree
median impute missing datamedian_impute
batch effect on numeric matrixmet2batch
missingness summary plotsmissingness.sum
multivariate analysismultivariate.anova
Nightingale Health metabolomics annotation data setng_anno
identify outlier sample indexes in a matrixoutlier.matrix
feature summary plotsoutlier.summary
identify outliersoutliers
principal component analysispc.and.outliers
PCA factor analysis and annotation enrichmentpca.factor.analysis
pca pairs plotpcapairs_bymoose
perform metabolomics quality controlperform.metabolite.qc
read in Metabolon (v1) metabolomics dataread.in.metabolon
read in Nightingale Health metabolomics dataread.in.nightingale
rank normal tranformationrntransform
continuous trait power analysis plotrun.cont.power.make.plot
binary trait imbalanced design power analysis plotrun.pa.imbalanced.power.make.plot
sample exlusions on missingness and total peak areasam.missingness.exclusion
estimate sample missingnesssample.missingness
outlier features count for samplessample.outliers
summary statistics for samplessample.sum.stats
estimates total peak abundancetotal.peak.area
identify independent features in a numeric matrixtree_and_independent_features
ggplot2 violin plotvariable.by.factor