MRManalyzeR

Reproducible post-acquisition processing of targeted LC-MS/MS metabolomics results exported from Waters TargetLynx, Skyline and plain matrix formats (extendable to other vendors). It reads the quantitative result tables, integrates them with sample and feature metadata, applies configurable quality-control and matrix-processing steps, and returns an analysis-ready struct::DatasetExperiment together with self-contained data-quality and statistical reports.

Each processing and analysis step is available as an exported R function operating on that object. Complete workflows can additionally be run from a single YAML configuration, without writing a driver script.

MRManalyzeR takes vendor software outputs. It does not perform chromatographic peak detection, peak integration or calibration-curve fitting from raw mass-spectrometry data.

Scope

Built for targeted quantitative assays — methods of roughly >20 MRM transitions, where the analytes are known compounds carrying pathway or class annotation, and the experiment is structured around defined target lists, whether testing a specific hypothesis or conducting targeted exploratory profiling.

Two consequences follow, and they are deliberate:

• Reported values are commonly internal-standard normalised. Normalisation and batch correction are therefore intentionally limited. Signal-drift modelling is available in untargeted metabolomics tools.
• Feature metadata carries biology. Compound class and enzymatic pathway are first-class: they group and colour heatmap annotations, PCA loadings and correlation blocks throughout.

The primary input is TargetLynx. Skyline exports and plain sample-by-analyte matrices from any other software are supported through the same workflow, and the metadata schema is configurable for other laboratories.

Where MRManalyzeR fits

Task	Tool
Raw untargeted LC-MS feature detection and alignment	xcms
General metabolomics peak-matrix preprocessing	pmp
Metabolite annotation-table workflows	MetMashR
Targeted assay processing, QC and reporting	MRManalyzeR

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Installation

Bioconductor dependency (do this first)

MRManalyzeR depends on struct, which is distributed through Bioconductor rather than CRAN, so install it with BiocManager before installing MRManalyzeR from GitHub:

if(!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("struct")

(structToolbox and pmp are not required. Earlier versions of this README listed them; they are no longer used.)

Latest release

# install.packages("remotes")
remotes::install_github("MJS-708/MRManalyzeR", ref = "main")

Development branch:

remotes::install_github("MJS-708/MRManalyzeR", ref = "dev")

Local install (from a cloned working copy):

devtools::install("path/to/MRManalyzeR")

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Quick start — bundled example

Run the workflow against a small bundled oxylipin dataset to verify the install and produce both reports. No paths to configure:

res <- MRManalyzeR::run_example()
res$output_directory   # where the xlsx + html reports were written
res$datasetExperiment  # the processed dataset
res$stats_tables       # comparisons, correlations, linear models

run_example() copies the bundled YAML, points it at inst/extdata/example_data.xlsx and a fresh tempdir(), runs run_MRManalyzeR(), and (in interactive sessions) opens the rendered HTML reports in the RStudio viewer / default browser.

The returned object

Every entry point returns a list whose datasetExperiment element is a struct::DatasetExperiment holding four aligned pieces:

Slot	Contents
data	the sample × feature matrix, in the reported datatype
sample_meta	one row per injection, plus everything the YAML added
variable_meta	one row per compound: class, pathway, CV metrics, exclusion reasons
name / description	provenance carried through to the reports

Because it is a DatasetExperiment, the result drops straight into other Bioconductor tooling rather than being a private format.

Example dataset

A subset of the BAL fluid oxylipin panel from Kolmert et al. (2018), Prostaglandins & Other Lipid Mediators 137, 11–18. DOI: https://doi.org/10.1016/j.prostaglandins.2018.05.005.

The bundled xlsx (inst/extdata/example_data.xlsx) contains a TargetLynx-style workbook with feature_metadata, sample_metadata and lcms_data_* sheets; the bundled YAML (inst/extdata/example_config.yml) is annotated and ready to copy as a starting point for your own studies.

A fully synthetic two-batch dataset (example_synthetic.RDS) is also bundled, and is what every function's @examples block runs on.

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Step-by-step API

The YAML route below is a convenience layer over exported functions. Any step can be called directly on a DatasetExperiment:

library(MRManalyzeR)

de <- assemble_dataset(read_targetlynx("study.xlsx", datatype = "Area"),
                       feature_meta, sample_meta)
de <- filter_blanks(de)
de <- normalise_matrix(de, column = "protein_mg")
de <- impute_missing(de, scalar = 0.2)
de <- correct_batch(de, batch_head = "Chrom_Batch")

qc  <- add_cv_metrics(de)
pca <- run_pca(de, transform = "log2", scale = TRUE)
plot_pca(pca, de, colour_by = "Treatment")

See vignette("MRManalyzeR") for the full walkthrough, and ?MRManalyzeR for the four stages the functions are grouped into: data parse → peak-matrix processing → QC check → stats.

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Usage on your own data

Step 1 — prepare the data xlsx

The input workbook needs at least three sheets (all sheet names are configurable):

• feature_metadata — one row per compound. Required: Processing_name (the vendor's identifier, used to join to the source table) and Compound (display name). Optional Report column, YES / NO, indicating whether the feature passed prior chromatographic review in TargetLynx or Skyline and should be included downstream; if the column is absent every feature is reported. MRManalyzeR does not itself inspect chromatographic peak shape — that judgement stays in the vendor software.
• sample_metadata — one row per acquisition. The acquisition name must match the vendor export exactly. Add any study metadata you want available for QC plots and statistics (e.g. Injection_order, Chrom_Batch, Sex, Treatment, Sample_type).
• lcms_data_1 (and optionally lcms_data_2, …) — the TargetLynx summary table. All lcms_data_* sheets must share the same feature set. Skyline and generic-matrix inputs use their own sheets instead.

Step 2 — copy and edit the YAML template

file.copy(
  system.file("extdata", "example_config.yml", package = "MRManalyzeR"),
  "config.yml"
)

Then edit config.yml. The key blocks are:

Block	Purpose
paths:	input xlsx location, output directory, output filename stem
PeakMatrixProcessing:	data source and datatype, signal / blank / MV filters, normalisation, IS and volume adjustment, batch correction
data_quality_report:	per-compound measurement vs injection order, Q-Q normality, QC PCA
stats_report:	configured group comparisons and multigroup tests, boxplots, correlations, linear models, PCA, volcano, ion ratios, heatmaps

The statistical method is chosen explicitly per comparison (welch, student, wilcoxon for two groups; anova or kruskal for three or more) — nothing is selected automatically on your behalf.

Every option is documented inline in the bundled YAML — read it once before editing.

Step 3 — run

res <- MRManalyzeR::run_MRManalyzeR("config.yml")

This writes, into paths.result_dir:

• <fn>_<datatype><suffix>.xlsx — processed matrix, feature metadata and sample metadata
• <fn>_<datatype><suffix>_stats.xlsx — a key sheet defining every column, then stats, correlations, linear_models, ion_ratios and summary
• <fn>_<datatype><suffix>_data_quality_report.html
• <fn>_<datatype><suffix>_stats_report.html
• <fn>_<datatype><suffix>.RDS and .txt — the dataset and the resolved parameters

The bundled config sets suffix: _, which is why the shipped examples read example_data_ng_mL__stats.xlsx with two underscores. Set suffix: "" for single ones.

To re-render reports from an already-processed RDS (skipping the xlsx ingest), set PeakMatrixProcessing.execute: False and re-run — fn, datatype and suffix must then match the stored file.

---

Combining multiple acquisitions

To merge separately-acquired panels (e.g. GOM, cysLT, SPM, or PL-pos / PL-neg / SL) sharing biological samples, use the combine workflow:

file.copy(
  system.file("extdata", "example_combine_config.yml", package = "MRManalyzeR"),
  "combine_config.yml"
)
res <- MRManalyzeR::run_MRManalyzeR_combine("combine_config.yml")

The combine YAML lists per-panel paths (.RDS or .xlsx), optional QC remappings (e.g. SL QC1..5 ↔ PL QC1, QC2, QC5, QC7, QC8), feature prefixing, and the same stats_report: block as a regular run. PeakMatrixProcessing and the data quality report are skipped — the inputs are already-processed matrices.

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Reports

Both reports are self-contained HTML with tabset navigation and (by default) plotly hover tooltips that surface Sample_ID on points and boxplot jitter. Toggle off with interactive_plots: False in either report block of the YAML.

Report	Sections
data_quality_report	sample summary; per-compound measurement vs injection order, in the selected datatype; per-compound Q-Q normality; QC PCA (one tab per colouring)
stats_report	global summaries; per-comparison statistical tables; per-compound boxplots (faceted across comparisons + by free-form factor); per-comparison PCA (scores + loadings); volcano; ion ratios (e.g. EpOME/DiHOME for sEH activity); samples × features heatmap; correlations; linear models

Every figure in both reports is drawn by an exported plot_*() function, so any panel can be reproduced, restyled or subset in one line without editing an R Markdown file.

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License

See LICENSE.