MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation),
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Use bioconductor-mscoreutils to robustly summarize the sample columns in [numeric matrix] into quantitative feature summaries, and flag any input-orientation or missing-data assumptions.
Required inputs
[numeric matrix]
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SKILL.md
name: bioconductor-mscoreutilsdescription: MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation),when_to_use: "Use when: 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. Not for: 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"user-invocable: false
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."
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