Package: MetaClean 1.0.1

Kelsey Chetnik

MetaClean: Detection of Low-Quality Peaks in Untargeted Metabolomics Data

Utilizes 11 peak quality metrics and 8 diverse machine learning algorithms to build a classifier for the automatic assessment of peak integration quality of peaks from untargeted metabolomics analyses. The 11 peak quality metrics were adapted from those defined in the following references: Zhang, W., & Zhao, P.X. (2014) <doi:10.1186/1471-2105-15-S11-S5> Toghi Eshghi, S., Auger, P., & Mathews, W.R. (2018) <doi:10.1186/s12014-018-9209-x>.

Authors:Kelsey Chetnik

MetaClean_1.0.1.tar.gz
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MetaClean.pdf |MetaClean.html✨
MetaClean/json (API)

# Install 'MetaClean' in R:

install.packages('MetaClean', repos = c('https://rickhelmus.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kelseychetnik/metaclean/issues

Datasets:

ex_peakData - Example peakData - value input to calculate... functions
ex_peakDataList - Example peakDataList - value input to calculteElutionShift and calculateRetentionTimeConsistency
ex_pts - Example pts - value input to caculate... functions
ex_ptsList - Example ptsList - value input to calculteElutionShift and calculateRetentionTimeConsistency
pqm_development - Example Peak Quality Metrics Data Frame for Development Dataset.
pqm_test - Example Peak Quality Metrics Data Frame for Test Dataset.

On CRAN:

s4vectors low-quality-integration machine-learning metabolomics

3.30 score 4 stars 4 scripts 20 downloads 22 exports 207 dependencies

Last updated 4 years agofrom:d0ce1bc0b2. Checks:OK: 1 WARNING: 6. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Oct 29 2024
R-4.5-win	WARNING	Sep 29 2024
R-4.5-linux	WARNING	Oct 29 2024
R-4.4-win	WARNING	Sep 29 2024
R-4.4-mac	WARNING	Sep 29 2024
R-4.3-win	WARNING	Oct 29 2024
R-4.3-mac	WARNING	Oct 29 2024

Exports:calculateApexMaxBoundaryRatio calculateElutionShift calculateEvaluationMeasures calculateFWHM calculateGaussianSimilarity calculateJaggedness calculateModality calculateRetentionTimeConsistency calculateSharpness calculateSymmetry calculateTPASR calculateZigZagIndex getBarPlots getCDPlots getEvalObj getEvaluationMeasures getPeakQualityMetrics getPredicitons rsdFilter runCrossValidation summaryStats trainClassifier

Dependencies:abind affy affyio AnnotationFilter askpass base64enc BH Biobase BiocBaseUtils BiocGenerics BiocManager BiocParallel BiocStyle bit bit64 bitops bookdown bslib cachem caret caTools class classInt cli clipr clock clue cluster codetools colorspace combinat commonmark cpp11 crayon crosstalk curl data.table DBI DelayedArray diagram digest doParallel dplyr e1071 evaluate fansi farver fastAdaboost fastmap fontawesome forcats foreach formatR fs futile.logger futile.options future future.apply generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 globals glue gower gplots graph gtable gtools hardhat haven highr hms htmltools htmlwidgets httpuv httr igraph impute ipred IRanges isoband iterators jquerylib jsonlite kernlab KernSmooth klaR knitr labeling labelled lambda.r later lattice lava lazyeval lifecycle limma listenv lubridate magrittr MALDIquant MASS MassSpecWavelet Matrix MatrixGenerics matrixStats memoise MetaboCoreUtils mgcv mime miniUI MLmetrics ModelMetrics MsCoreUtils MsExperiment MsFeatures MSnbase MultiAssayExperiment munsell mzID mzR ncdf4 nlme nnet numDeriv openssl parallelly pcaMethods pillar pkgconfig plotly plotrix plyr preprocessCore prettyunits pROC prodlim progress progressr promises ProtGenerics proxy PSMatch purrr QFeatures questionr R.cache R.methodsS3 R.oo R.utils R6 randomForest rappdirs RColorBrewer Rcpp readr recipes reshape2 Rgraphviz Rhdf5lib rlang rmarkdown ROCR rpart rprojroot rstudioapi S4Arrays S4Vectors sass scales shape shiny snow sourcetools SparseArray Spectra SQUAREM statmod stringi stringr styler SummarizedExperiment survival sys tibble tidyr tidyselect timechange timeDate tinytex tzdb UCSC.utils utf8 vctrs viridisLite vroom vsn withr xcms xfun XML xtable XVector yaml zlibbioc

MetaClean - Walkthrough

Kelsey Chetnik

Rendered fromMetaClean_WalkThrough.Rmdusingknitr::knitron Oct 29 2024.

Last update: 2021-05-13
Started: 2020-04-08

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate Apex-Max Boundary Ratio (of a Chromatographic Peak)	calculateApexMaxBoundaryRatio
Calculate Elution Shift (of a Peak Group)	calculateElutionShift
Calculate Evaluation Measures	calculateEvaluationMeasures
Calculate FWHM2Base (of a Chromatographic Peak)	calculateFWHM
Calculate Gaussian Similarity (of a Chromatographic Peak)	calculateGaussianSimilarity
Calculate Jaggedness (of a Chromatographic Peak)	calculateJaggedness
Calculate Modality (of a Chromatographic Peak)	calculateModality
Calculate Retention Time Consistency (of a Peak Group)	calculateRetentionTimeConsistency
Calculate Sharpness (of a Chromatographic Peak)	calculateSharpness
Calculate Symmetry (of a Chromatographic Peak)	calculateSymmetry
Calcualte Triangle Peak Area Similarity Ratio (TPASR) (of a Chromatographic Peak)	calculateTPASR
Calculate the Zig-Zag Index (of a Chromatographic Peak)	calculateZigZagIndex
A custom class for storing the chromatographic peak data required by the peak metric functions for each group of samples.	evalObj evalObj-class
Example peakData - value input to calculate... functions (except calculateElutionShift and calculateRetentionTimeConsistency)	ex_peakData
Example peakDataList - value input to calculteElutionShift and calculateRetentionTimeConsistency	ex_peakDataList
Example pts - value input to caculate... functions (except calculateElutionShift and calculateRetentionTimeConsistency)	ex_pts
Example ptsList - value input to calculteElutionShift and calculateRetentionTimeConsistency	ex_ptsList
Generate Bar Plots for the Seven Evaluation Measures	getBarPlots
Generate Bar Plots for the Seven Evaluation Measures	getCDPlots
Extract peak data object	getEvalObj
Calculate Evaluation Measures	getEvaluationMeasures
Calculate the 12 Peak Quality Metrics	getPeakQualityMetrics
Get MetaClean Predictions	getPredicitons
Example Peak Quality Metrics Data Frame for Development Dataset.	pqm_development
Example Peak Quality Metrics Data Frame for Test Dataset.	pqm_test
RSD Filteirng	rsdFilter
Run Cross-Validation for A List of Algoirthms with Peak Quality Metric Feature Sets	runCrossValidation
Calculate summary statistics for evaluation measures	summaryStats
Train MetaClean Classifier	trainClassifier