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Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing.

Erik Müller1,2, Carolin Elisabeth Huber1,2, Werner Brack1,2

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Analytical Chemistry
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This study introduces a new gap filling algorithm for nontargeted mass spectrometry (MS) data. The novel method, based on Symbolic Aggregation Approximation (SAX), significantly improves the detection of missing peaks in large datasets.

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Area of Science:

  • Analytical Chemistry
  • Data Science
  • Biotechnology

Background:

  • Nontargeted mass spectrometry (MS) is crucial for large-scale studies in life sciences and environmental chemistry.
  • Data gaps in aligned MS datasets are a significant challenge, stemming from sample absence, analytical issues, or software limitations.
  • Existing peak detection algorithms have limitations, necessitating gap filling strategies to manage missing data.

Purpose of the Study:

  • To develop and present a novel gap filling algorithm for nontargeted high-resolution MS data.
  • To address the issue of missing values in aligned mass spectral datasets.
  • To enhance the detection efficiency of missing peaks in complex samples.

Main Methods:

  • The study adapted the Symbolic Aggregation Approximation (SAX) algorithm, originally from time series data mining.
  • SAX was applied to liquid chromatography-high-resolution MS (LC-HRMS) data for nontarget screening.
  • The performance was evaluated against existing gap filling methods, including MZmine's Peak Finder.

Main Results:

  • The SAX-based gap filling algorithm demonstrated a considerable improvement in detection efficiency.
  • The method effectively identifies missing peaks in aligned mass spectral data.
  • Superior performance was observed compared to conventional gap filling techniques.

Conclusions:

  • The SAX-based algorithm offers a robust solution for reducing data gaps in nontargeted MS studies.
  • This approach enhances the reliability and completeness of large-scale MS data analysis.
  • The method shows promise for broader application in analytical chemistry and data mining.