NEWS

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MRManalyzeR 0.99.0

• Initial Bioconductor submission.
• Post-acquisition processing of targeted LC-MS/MS lipidomics and metabolomics results exported from Waters TargetLynx or Skyline, or supplied as a plain sample-by-analyte matrix: peak-matrix assembly, S/N and LOD/LOQ filtering, blank filtering, missing-value imputation, normalisation, internal-standard and volume adjustment of vendor-reported concentrations, and batch correction. Chromatographic peak detection, integration and calibration-curve fitting are outside its scope.
• Every step is an exported function operating on a struct::DatasetExperiment; a whole workflow can additionally be driven from a single YAML config via run_MRManalyzeR().
• Self-contained HTML data-quality and statistical reports: group summaries, PCA, sample x feature heatmaps, volcano plots, feature-feature correlations, linear models, and enzyme-activity ion ratios.
• run_MRManalyzeR_combine() merges multiple acquisition panels into a single analysis.
• run_example() runs the full workflow on the bundled example dataset (a subset of Kolmert et al. 2018, doi:10.1016/j.prostaglandins.2018.05.005).
• Every processing step is also an exported function operating on a struct::DatasetExperiment: read_targetlynx() / read_skyline(), assemble_dataset(), filter_blanks(), normalise_matrix(), adjust_concentration(), impute_missing() and correct_batch(), composed by process_dataset().
• Per-compound quality metrics (CV_QC, CV_sample and their ratio) are written into the dataset's variable_meta.
• A second bundled dataset, example_synthetic.xlsx (plus the processed example_synthetic.RDS), is entirely simulated from a fixed seed: two chromatographic batches, per-sample protein amounts and reconstitution volumes, and a planted treatment effect on 5 of its 20 analytes, so normalisation, concentration adjustment, batch correction and the statistics can each be demonstrated against a known truth.