MsCoreUtils

Dependencies & Environment

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

• Version: 1.24.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: S4Vectors, MASS, clue
• Install: BiocManager::install("MsCoreUtils")

When to Use

• Quantitative Aggregation: Calculating the robust summary of matrix columns (e.g., summarizing peptide quantitation values into protein intensities) using robustSummary().
• Spectra Processing: Performing low-level mass spectra processing such as noise estimation, smoothing, and binning using functions like noise(), smooth(), and bin().
• Missing Data Imputation: Imputing missing data in quantitative matrices using methods like impute_knn(), impute_RF(), or impute_matrix().
• Data Normalization: Normalizing data matrices using normalize_matrix().

When NOT to Use

• For high-level, formal data structure manipulation of MS data, use Spectra or MSnbase instead because MsCoreUtils only provides low-level functions independent of high-level data structures.
• For complete end-to-end LC-MS preprocessing workflows, use xcms instead because MsCoreUtils provides individual utility functions rather than a full pipeline.

Data Requirements

• Basic R classes such as matrix or numeric vectors.
• For robustSummary(), a numeric matrix where columns represent samples and rows represent features (e.g., peptides).

Key Parameters

• x (matrix): A numeric matrix provided to functions like robustSummary().
• nrow (numeric): Number of rows when constructing a matrix() for testing or processing.

Best Practices

• Use robustSummary() to aggregate quantitative values robustly, which is typically used internally by high-level functions like MSnbase::combineFeatures().
• Check available imputation and normalization methods using imputeMethods() and normalizeMethods().
• Ensure input data is in standard base R formats (like matrix) before applying MsCoreUtils functions.

Common Pitfalls

• High-level object errors: Passing a Spectra or QFeatures object directly to MsCoreUtils functions. Fix: Extract the underlying matrix or numeric vectors first, as MsCoreUtils is independent of high-level data structures.
• Missing dependencies: Attempting to use advanced imputation methods without required packages. Fix: Ensure all suggested packages for specific impute_* functions are installed.
• Incorrect matrix orientation: Summarizing across the wrong dimension. Fix: Remember that robustSummary() calculates the robust summary of the columns of a matrix.

Alternatives

• MSnbase: Provides high-level data structures and methods (like combineFeatures()) that wrap MsCoreUtils functions.
• QFeatures: Provides aggregateFeatures() for high-level quantitative feature aggregation.
• Spectra: For high-level mass spectrometry data handling and processing.

Citations

• Sticker et al. "Robust summarization and inference in proteome-wide label-free quantification." https://doi.org/10.1101/668863.
• Rainer et al. (2022). "A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R."

References

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

<!-- biomate-cta -->

---

Run this on BioMate

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

▶ Open mscoreutils on BioMate →