Matlab Scientific Computing

MATLAB-based scientific computing for mass spectrometry data processing

Summary

Execute parameter-driven computational pipelines in MATLAB R2024a to perform ROI detection, preprocessing, and augmentation on converted mass spectrometry data (mzXML/mzML). This skill integrates specialized toolboxes (signal processing, image processing, statistics) with domain-specific functions to transform raw MS signals into augmented feature matrices.

When to use

You have mass spectrometry data in mzXML or mzML format and need to systematically extract regions of interest (ROIs) from multi-dimensional m/z-intensity-time arrays, normalize feature values, and augment datasets for untargeted metabolomics workflows. Use this skill when manual peak picking is infeasible and parameter-driven automation of the full preprocessing chain is required.

When NOT to use

• Input is already a pre-processed feature table or peak list — skip ROI detection and preprocessing stages.
• Data is in incompatible format (e.g., raw vendor binary, NetCDF) without prior conversion via msconvert.
• MATLAB R2024a or required toolboxes are not available; consider open-source alternatives (e.g., R xcms, Python pymzml-based workflows).

Inputs

• Mass spectrometry data in mzXML or mzML format (converted from raw instrument output)
• Configuration parameters (ROI search thresholds, preprocessing settings, augmentation rules)
• Single or multiple datasets for batch processing

Outputs

• Augmented feature matrix (samples × detected features) with normalized intensities
• ROI metadata (m/z values, retention times, feature identifiers)
• Processing logs and quality control metrics

How to apply

Load converted MS data (mzXML/mzML) into MATLAB R2024a with required toolboxes installed (Bioinformatic, Statistics and Machine Learning, Wavelet, Image Processing, Signal Processing, Parallel Computing). Configure ROI search parameters (e.g., intensity thresholds, m/z tolerance windows) and pass the raw data through the sequential three-stage pipeline: (1) apply ROIpeaks function to detect and localize regions of interest in the mass-intensity-retention-time space; (2) execute data preprocessing on identified ROIs to normalize intensities and remove noise artifacts; (3) apply MSroiaug function with augmentation parameters to expand the feature set across multiple datasets. Export the resulting augmented feature matrix and processing metadata. The rationale is that parameter-driven sequential execution ensures reproducibility, enables batch processing of multiple experiments, and preserves the interpretability of preprocessing stages.

Related tools

• ROIpeaks (Core function for region-of-interest detection in multi-dimensional MS data; identifies m/z-retention-time clusters above noise threshold)
• MSroiaug (Data augmentation function that expands and replicates preprocessed ROI features across multiple datasets to increase training signal)
• MATLAB R2024a (Host computing environment providing numerical array operations, parallel processing, and graphical output)
• Bioinformatic Toolbox (Provides MS-specific I/O routines and sequence/mass analysis utilities)
• Signal Processing Toolbox (Enables filtering, wavelet decomposition, and noise reduction on MS intensity profiles)
• Image Processing Toolbox (Supports 2D/3D imaging operations for m/z-retention-time heatmaps and morphological ROI refinement)
• Wavelet Toolbox (Provides multi-scale decomposition for baseline removal and feature extraction from noisy MS spectra)
• Statistics and Machine Learning Toolbox (Enables statistical normalization, dimensionality reduction, and quality assessment of augmented features)
• Parallel Computing Toolbox (Accelerates batch processing of multiple datasets using multi-core parallelization)
• msconvert (Pre-processing tool for converting vendor-specific raw MS files to standardized mzXML or mzML interchange formats) — http://proteowizard.sourceforge.net/download.html
• ZMat toolbox (Optional data compression/decompression utility for efficient storage and transmission of large MS feature matrices) — http://github.com/fangq/zmat

Evaluation signals

• Output feature matrix dimensions match expected sample count × detected ROI count; no NaN or infinite values.
• Preprocessing stage successfully normalizes ROI intensities to unit range or z-score; verify by sampling intensity statistics before/after.
• MSroiaug augmentation increases feature count without introducing duplicates; cross-check augmented feature IDs for uniqueness.
• Processing metadata logs contain timestamps, parameter values, and stage-completion flags for each dataset; log file size > 0 and contains expected keywords.
• Exported files (feature matrix, ROI table, metadata) conform to expected schema (e.g., comma-separated or tab-delimited text); load and verify row/column counts match pipeline input specification.

Limitations

• Requires MATLAB R2024a (or newer) and all seven listed toolboxes; licensing cost and availability may restrict adoption in resource-limited settings.
• Performance on very large datasets (>1 GB m/z-intensity arrays) depends on available RAM and parallel computing setup; out-of-core processing not explicitly documented.
• ROIpeaks and MSroiaug function behavior is parameter-dependent and may require empirical tuning for new instrument types or ionization modes (CE-MS vs. LC-MS); no adaptive parameter selection mechanism described.
• Data must be pre-converted to mzXML/mzML via msconvert; incompatible with raw vendor formats (e.g., .raw, .d folders) without external conversion step.
• AriumMS repository carries no documented changelog or version history, limiting reproducibility across releases; community support or bug-fix turnaround unclear.

Evidence

• [other] AriumMS implements a three-stage pipeline where user-set parameters drive sequential execution of ROI search, data preprocessing, and data augmentation stages.: "Set parameters are then used to perform ROI search, data preprocessing and data augmentation."
• [readme] The pipeline processes converted mzXML and mzML format MS data files.: "For MS Data conversion to .mzXML or .mzML file format use msconvert, distributed with the ProteoWizard Project"
• [readme] The core functions ROIpeaks and MSroiaug were developed by recognized chemometricians (Tauler, Gorrochategui, Jaumot).: "It uses functions (ROIpeaks, MSroiaug) developed by Romà Tauler, Eva Gorrochategui and Joaquim Jaumot"
• [readme] Seven specific MATLAB toolboxes are required to run the AriumMS application.: "Required toolboxes for the app version: Bioinformatic Toolbox, Statistics And Machine Learning Toolbox, Wavelet Toolbox, Image Processing Toolbox, Signal Processing Toolbox, Parallel Computing"
• [readme] MATLAB R2024a or newer is the required computing environment.: "MATLAB R2024a or newer"
• [readme] AriumMS is an integrated tool for untargeted metabolomics applied to multi-platform (CE-MS and LC-MS) datasets.: "All in one tool for untargeted Metabolomics by ROI and augmentation of multiple Data sets."
• [readme] The ZMat toolbox is integrated for optional data compression within the AriumMS pipeline.: "and ZMat toolbox written by Qianqian Fang http://github.com/fangq/zmat."