MetaboCoreUtils

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.20.1 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: MsCoreUtils, BiocParallel
• Install: BiocManager::install("MetaboCoreUtils")

When to Use

• Mass and m/z Conversions: Converting between exact compound masses and ion mass-to-charge ratios ($m/z$) using mass2mz and mz2mass.
• Chemical Formula Manipulation: Standardizing chemical formulas to Hill notation (standardizeFormula), adding/subtracting elements (addElements, subtractElements), and calculating exact masses (calculateMass).
• Signal Drift Adjustment: Modeling and adjusting for injection order-dependent signal drift in LC-MS feature abundances using fit_lm and adjust_lm.
• Kendrick Mass Defect Calculation: Identifying homologous series (e.g., lipids) by calculating Kendrick mass defects using calculateKmd and calculateRkmd.

When NOT to Use

• Full LC-MS Preprocessing: For end-to-end raw data processing (peak picking, alignment, grouping); use high-level packages like xcms instead because MetaboCoreUtils only provides low-level utility functions.
• Complex Statistical Modeling: For modeling non-linear batch effects or complex multi-covariate experimental designs; use dedicated statistical modeling packages because fit_lm is designed for simple linear drift adjustments.

Data Requirements

• Input Format: Character vectors for chemical formulas (e.g., "C6H12O6"), numeric vectors for masses or $m/z$ values, and matrices for feature abundances.
• Retention Indexing: A data.frame containing retention times and corresponding index values is required for indexRtime and correctRindex.
• Signal Drift: A matrix of feature abundances (features in rows, samples in columns) and a data.frame of covariates (e.g., injection index).

Key Parameters

• adduct: Character string or vector specifying the ESI ion adduct (e.g., "[M+H]+" or "[M+Na]+") for mass conversions.
• data: A data.frame containing the covariates (e.g., injection_index) passed to fit_lm and adjust_lm.
• minVals: The minimum number of non-missing values required in QC samples to fit a linear model in fit_lm.
• y: The response variable (e.g., log2-transformed abundances) passed to fit_lm.
• lm: A list of fitted linear models passed to adjust_lm to correct the data.

Best Practices

• Always standardize chemical formulas using standardizeFormula before comparing them or counting elements (countElements) to ensure consistent formatting.
• Use adductNames to view the exact spelling of all supported ESI ion adduct definitions before performing $m/z$ conversions.
• When adjusting for signal drift, fit the linear models (fit_lm) exclusively on QC samples to ensure the estimated drift is independent of biological covariates.
• Evaluate the estimated signal drifts (e.g., check p-values and R-squared) and filter out poor fits (e.g., p-value > 0.05) before applying adjust_lm.

Common Pitfalls

• Unstable Drift Models: Fitting linear models on features with too few QC measurements leads to unstable adjustments; Fix: Increase the minVals parameter in fit_lm to require a strict minimum of non-missing QC values.
• Adjusting Non-Drifting Features: Blindly applying adjust_lm to all features can introduce noise to features without true signal drift; Fix: Filter the list of models returned by fit_lm to remove fits with poor p-values before adjustment.
• Incorrect Adduct Syntax: Conversions fail if the adduct string is misspelled; Fix: Query adductNames() to verify the exact string format (e.g., "[M+H]+").

Alternatives

• xcms: Provides a comprehensive, high-level framework for LC-MS data preprocessing (peak picking, retention time alignment) rather than low-level utilities.
• MsCoreUtils: Offers core utilities specifically for mass spectrometry data (e.g., spectra processing, noise estimation) rather than chemical formula and adduct calculations.
• enviPat: Focuses on fine-grained isotopic pattern calculations and profile simulations rather than basic exact mass and $m/z$ conversions.

Citations

• Rainer, M., Vicini, P., & Sigurdsson, S. (2022). MetaboCoreUtils: Core Utils for Metabolomics Data.
• Wehrens, R., et al. (2016). Improved batch correction in untargeted MS-based metabolomics. Metabolomics, 12(5), 88.

References

• Homepage: https://bioconductor.org/packages/MetaboCoreUtils
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html

<!-- biomate-cta -->

---

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use metabocoreutils. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open metabocoreutils on BioMate →