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automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning.

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Automated preprocessing of liquid chromatography-mass spectrometry (LC-MS) data using machine learning improves peak detection and quality control. The automRm R package offers a robust, fully automatic solution for targeted LC-MS data analysis, outperforming existing methods.

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Area of Science:

  • Analytical Chemistry
  • Biotechnology
  • Computational Biology

Background:

  • Consistent chromatographic peak recognition is crucial for targeted LC-MS data analysis, especially for low-abundance signals.
  • Manual peak review is time-consuming and operator-dependent, hindering high-throughput analysis.
  • Existing automated methods struggle with variations in peak shape and retention time, particularly in methods like HILIC-MS.

Purpose of the Study:

  • To introduce automRm, an R package for fully automatic preprocessing of targeted LC-MS data in MRM mode.
  • To leverage machine learning for accurate chromatographic peak detection and quality control.
  • To provide a generally applicable solution for diverse LC-MS analytical methods, including HILIC.

Main Methods:

  • Development of the automRm R package utilizing machine learning algorithms for peak detection and quality control.
  • Application of automRm to targeted LC-MS data, including HILIC-MS datasets.
  • Evaluation of ML algorithm choice, training data, and peak characteristics on automRm performance.
  • Comparison of automRm with manual peak review and alternative software solutions.

Main Results:

  • automRm successfully automates the recognition of complex patterns in raw LC-MS data.
  • The package demonstrates the ability to replicate results from manual peak review on published datasets.
  • automRm shows superior performance over alternative software in terms of peak integration consistency and accuracy for HILIC-MS data.
  • The ML approach enhances general applicability across various analytical methods.

Conclusions:

  • automRm provides a fast, reliable, and operator-independent solution for targeted LC-MS data preprocessing.
  • The machine learning-based approach enhances the accuracy and consistency of chromatographic peak reporting.
  • automRm is a valuable tool for researchers working with complex LC-MS datasets, particularly in HILIC-MS applications.