Package: MRManalyzeR 0.99.8

Matthew J. Smith

MRManalyzeR: Process and Analyse Targeted LC-MS Lipidomics and Metabolomics Data

Reproducible post-acquisition processing of targeted LC-MS/MS lipidomics and metabolomics results exported from Waters TargetLynx or from Skyline, or supplied as a plain sample-by-analyte matrix. Reads the quantitative result tables, integrates them with sample and feature metadata, and applies configurable signal filtering (SNR, LOD or LOQ), blank filtering, batch correction, normalisation, internal-standard and volume adjustment of vendor-reported concentrations, and missing-value imputation. Each step is an exported function acting on a struct DatasetExperiment; a complete workflow can additionally be driven from a single YAML configuration via runMRManalyzeR(), which also renders self-contained HTML data-quality and statistical reports. Chromatographic peak detection, peak integration and calibration-curve fitting from raw mass-spectrometry data are outside its scope.

Authors:Matthew J. Smith [aut, cre], Antonio Checa [aut]

MRManalyzeR_0.99.8.tar.gz
MRManalyzeR_0.99.8.zip(r-4.7-any)MRManalyzeR_0.99.8.zip(r-4.6-any)MRManalyzeR_0.99.8.zip(r-4.5-any)
MRManalyzeR_0.99.8.tgz(r-4.6-any)MRManalyzeR_0.99.8.tgz(r-4.5-any)
MRManalyzeR_0.99.8.tar.gz(r-4.7-any)MRManalyzeR_0.99.8.tar.gz(r-4.6-any)
MRManalyzeR_0.99.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MRManalyzeR/json (API)

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

Bug tracker:https://github.com/mjs-708/mrmanalyzer/issues

On CRAN:

Conda:

metabolomicslipidomicsmassspectrometryqualitycontrolnormalizationbatcheffectvisualizationworkflowstep

3.90 score 6 scripts 37 exports 89 dependencies

Last updated from:15a43ca65d. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE210
linux-develOK465
source / vignettesOK248
linux-releaseOK374
macos-releaseOK269
macos-oldrelOK323
windows-develOK230
windows-releaseOK189
windows-oldrelOK268
wasm-releaseOK148

Exports:addCVMetricsadjustConcentrationassembleDatasetcombineDatasetscorrectBatchfilterBlanksfilterFeaturesfilterSamplesimputeMissingloadConfigloadDatasetmrmPlotHeatmapmrmPlotPCAmrmRunExamplemrmRunPCAmrmValidateConfigmrmValidateDesignmrmValidateInputnewConfignormaliseMatrixplotBoxplotplotCorrelationplotDriftplotGroupHeatmapplotLoadingsplotQQplotVolcanoprocessDatasetreadPeakMatrixreadSkylinereadTargetLynxrunMRManalyzeRrunMRManalyzeRCombinerunStatssubsetDatasetsummariseFeaturessummariseGroups

Dependencies:abindaskpassbase64encBiobaseBiocGenericsbslibcachemclicpp11crosstalkcurlDelayedArraydigestdplyrDTevaluatefarverfastmapfontawesomefsgenericsGenomicRangesggnewscaleggplot2gluegtablehighrhmshtmltoolshtmlwidgetshttr2IRangesisobandjanitorjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelubridatemagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslopenxlsxotelpheatmappillarpkgconfigpromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS4ArraysS4VectorsS7sassscalesSeqinfosnakecaseSparseArraystringistringrstructSummarizedExperimentsystibbletidyrtidyselecttimechangetinytexutf8vctrsviridisLitewithrxfunXVectoryamlzip

Processing in detail
Scope of vignette | Assumptions and limitations | Workflow organization | Stage 1: data parse | The curated workbook | The TargetLynx export | The bundled dataset | Assembling a DatasetExperiment | Stage 2: peak-matrix processing | Blank filter | Detection filters | Batch correction | Normalise | Concentration adjustment | Impute missing values | The whole matrix in one call | Stage 3: QC check | How well was each compound measured? | Analytical drift | Normality | PCA | Loadings | What was reported, and what was dropped | Stage 4: statistics | Selecting the biological samples | Group summaries | Comparisons | Per-compound plots | Volcano | Heatmap | The group-mean variant | Feature correlations | Metabolite ratios | References and sources | Session info

Last update: 2026-11-05
Started: 2026-10-15

Getting started with MRManalyzeR
Scope | Installation | Example | Run example | Data format | A configuration to edit | The run | Outputs | Reading the configuration | The two reports | Several data acquisition panels in one study | Working with other packages | Session info

Last update: 2026-10-15
Started: 2026-07-22

Readme and manuals

Help Manual

Help pageTopics
Add per-compound CV metrics to a datasetaddCVMetrics
Adjust a dataset to true sample concentrationsadjustConcentration
Assemble a struct::DatasetExperiment from a matrix + metadataassembleDataset
Combine multiple processed datasets into onecombineDatasets
Batch-correct a DatasetExperiment (median-ratio)correctBatch
Blank-filter a dataset (mask values at or below the blank level)filterBlanks
Filter features by detection frequencyfilterFeatures
Filter injections by missing-value fractionfilterSamples
Impute missing values below each compound's lowest measurementimputeMissing
Load a project YAML, resolving !concat_path and !concat_str anchorsloadConfig
Load a `DatasetExperiment` from `.RDS` or `.xlsx`loadDataset
The result of a validation checkmrm_validation
Sample-by-feature heatmap with class annotationsmrmPlotHeatmap
Plot PCA scores, coloured by a sample-metadata columnmrmPlotPCA
Render the bundled example MRManalyzeR reportsmrmRunExample
Pre-process a sample x feature matrix and run PCAmrmRunPCA
Validate a YAML configurationmrmValidateConfig
Validate a study design against the datamrmValidateDesign
Validate a workbook before processing itmrmValidateInput
Write a configuration file to start fromnewConfig
Normalise a dataset by a per-sample metadata columnnormaliseMatrix
Per-compound box or bar plot by groupplotBoxplot
Feature-by-feature correlation heatmapplotCorrelation
Plot one compound's intensity against injection orderplotDrift
Group-mean heatmap that shows which samples produced the meanplotGroupHeatmap
Plot PCA loadings as ranked bars, one panel per componentplotLoadings
Normal Q-Q plot for one compoundplotQQ
Volcano plot for one comparisonplotVolcano
Print a validation resultprint.mrm_validation
Build a sample x compound matrix from TargetLynx or Skyline inputprocessDataset
Read a plain sample x compound matrixreadPeakMatrix
Read Skyline sheet(s) into a wide sample x compound matrixreadSkyline
Read a TargetLynx workbook into a wide sample x compound matrixreadTargetLynx
Run the post-acquisition MRManalyzeR workflow from a YAML configrunMRManalyzeR
Run the combine-mode workflow from a dedicated combine YAMLrunMRManalyzeRCombine
Run all statistics defined by the YAML `stats_report:` blockrunStats
Subset a `DatasetExperiment` by sample-metadata conditionssubsetDataset
Summarise which compounds were reported and which were droppedsummariseFeatures
Per-group summary statistics for every compoundsummariseGroups