Package: MRManalyzeR 0.99.0

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, normalisation, internal-standard and volume adjustment of vendor-reported concentrations, missing-value imputation and batch correction. Each step is an exported function acting on a struct DatasetExperiment; a complete workflow can additionally be driven from a single YAML configuration via run_MRManalyzeR(), 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]

MRManalyzeR_0.99.0.tar.gz
MRManalyzeR_0.99.0.zip(r-4.7-any)MRManalyzeR_0.99.0.zip(r-4.6-any)MRManalyzeR_0.99.0.zip(r-4.5-any)
MRManalyzeR_0.99.0.tgz(r-4.6-any)MRManalyzeR_0.99.0.tgz(r-4.5-any)
MRManalyzeR_0.99.0.tar.gz(r-4.7-any)MRManalyzeR_0.99.0.tar.gz(r-4.6-any)
MRManalyzeR_0.99.0.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://biocstaging.r-universe.dev', 'https://cloud.r-project.org'))

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

On CRAN:

metabolomics lipidomics massspectrometry qualitycontrol normalization batcheffect visualization workflowstep

3.30 score 33 exports 81 dependencies

Last updated from:5e00a87a8b. Checks:1 WARNING, 9 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	192
linux-devel-x86_64	OK	288
source / vignettes	OK	298
linux-release-x86_64	OK	285
macos-release-arm64	OK	222
macos-oldrel-arm64	OK	193
windows-devel-x86_64	OK	236
windows-release-x86_64	OK	249
windows-oldrel-x86_64	OK	238
wasm-release	OK	960

Exports:add_cv_metrics adjust_concentration assemble_dataset combine_datasets correct_batch filter_blanks impute_missing load_config load_dataset normalise_matrix plot_boxplot plot_correlation plot_drift plot_heatmap plot_loadings plot_pca plot_qq plot_volcano process_dataset read_peak_matrix read_skyline read_targetlynx run_example run_MRManalyzeR run_MRManalyzeR_combine run_pca run_stats subset_dataset summarise_features summarise_groups validate_config validate_design validate_input

Dependencies:abind askpass base64enc Biobase BiocGenerics bslib cachem cli cpp11 curl DelayedArray digest dplyr evaluate farver fastmap fontawesome fs generics GenomicRanges ggplot2 glue gtable highr hms htmltools httr2 IRanges isoband janitor jquerylib jsonlite knitr labeling lattice lifecycle lubridate magrittr Matrix MatrixGenerics matrixStats memoise mime openssl openxlsx pheatmap pillar pkgconfig purrr R6 rappdirs RColorBrewer Rcpp rlang rmarkdown S4Arrays S4Vectors S7 sass scales Seqinfo snakecase SparseArray stringi stringr struct SummarizedExperiment sys tibble tidyr tidyselect timechange tinytex utf8 vctrs viridisLite withr xfun XVector yaml zip

Processing targeted LC-MS/MS lipidomics and metabolomics with MRManalyzeR

Rendered fromMRManalyzeR.Rmdusingknitr::rmarkdown

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

Help page	Topics
Add per-compound CV metrics to a dataset	add_cv_metrics
Adjust a dataset to true sample concentrations	adjust_concentration
Assemble a struct::DatasetExperiment from a matrix + metadata	assemble_dataset
Combine multiple processed datasets into one	combine_datasets
Batch-correct a DatasetExperiment (median-ratio)	correct_batch
Blank-filter a dataset (mask values at or below the blank level)	filter_blanks
Impute missing values with a fraction of the per-feature minimum	impute_missing
Load a project YAML, resolving !concat_path and !concat_str anchors	load_config
Load a `DatasetExperiment` from `.RDS` or `.xlsx`	load_dataset
Normalise a dataset by a per-sample metadata column	normalise_matrix
Per-compound box or bar plot by group	plot_boxplot
Feature-by-feature correlation heatmap	plot_correlation
Plot one compound's intensity against injection order	plot_drift
Sample-by-feature heatmap with class annotations	plot_heatmap
Plot PCA loadings as ranked bars, one panel per component	plot_loadings
Plot PCA scores, coloured by a sample-metadata column	plot_pca
Normal Q-Q plot for one compound	plot_qq
Volcano plot for one comparison	plot_volcano
Print a validation result	print.mrm_validation
Build a sample x compound matrix from TargetLynx or Skyline input	process_dataset
Read a plain sample x compound matrix	read_peak_matrix
Read Skyline sheet(s) into a wide sample x compound matrix	read_skyline
Read a TargetLynx workbook into a wide sample x compound matrix	read_targetlynx
Render the bundled example MRManalyzeR reports	run_example
Run the post-acquisition MRManalyzeR workflow from a YAML config	run_MRManalyzeR
Run the combine-mode workflow from a dedicated combine YAML	run_MRManalyzeR_combine
Pre-process a sample x feature matrix and run PCA	run_pca
Run all statistics defined by the YAML `stats_report:` block	run_stats
Subset a `DatasetExperiment` by sample-metadata conditions	subset_dataset
Summarise which compounds were reported and which were dropped	summarise_features
Per-group summary statistics for every compound	summarise_groups
Validate a YAML configuration	validate_config
Validate a study design against the data	validate_design
Validate a workbook before processing it	validate_input

Package: MRManalyzeR 0.99.0

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

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