MRManalyzeR

Process and analyse targeted lipidomics / metabolomics data exported from Waters TargetLynx. Reads the TargetLynx xlsx workbook, builds a peak-area or concentration matrix (with optional SNR filtering, blank filtering, missing-value imputation, normalisation and batch correction) and renders self-contained HTML reports for data quality and statistical inference. Driven from a single YAML config — no driver script needed.

Primarily for internal use at the Wheelock lab, Karolinska Institutet (Sweden).

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Installation

Bioconductor dependencies (do this first)

MRManalyzeR depends on three Bioconductor packages (struct, structToolbox, pmp) that aren't on CRAN, so install_github() / devtools::install() cannot pull them automatically. Install them once:

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

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

res <- MRManalyzeR::run_example()
attr(res, "results_dir")    # where the xlsx + html reports were written

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.

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.

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Usage on your own data

Step 1 — prepare the data xlsx

The input workbook needs at least three sheets:

• feature_metadata — one row per compound. Required columns: Processing_name (TargetLynx ID, used internally), Compound (display name), and Report populated with YES / NO based on manual peak interpretation.
• sample_metadata — one row per acquisition. The acquisition name must match the TargetLynx 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.

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, datatype to report (Area / Response / ng/mL / …)
PeakMatrixProcessing:	SNR threshold, blank/MV filters, batch correction, normalisation
data_quality_report:	per-compound intensity-vs-injection-order plots, QQ plots, QC PCA
stats_report:	comparisons (auto t-test/Wilcoxon/ANOVA), boxplots, correlations, linear models, PCA, volcano, ion ratios, heatmap

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:

• <fn>_<datatype>_.xlsx — processed peak matrix + the resolved YAML parameters
• <fn>_<datatype>__stats.xlsx — stats, correlations, linear_models tabs from the comparisons defined in the YAML
• <fn>_<datatype>__data_quality_report.html
• <fn>_<datatype>__stats_report.html

To re-render reports from an already-processed RDS (skipping the xlsx ingest), set PeakMatrixProcessing.execute: False and re-run.

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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 intensity vs injection order; per-compound Q-Q normality; QC PCA (one tab per coloring)
stats_report	global summaries; per-comparison p-value 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

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License

See LICENSE.