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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Data Treatment for LC-MS Untargeted Analysis.

Samantha Riccadonna1, Pietro Franceschi2

  • 1Computational Biology Unit, Research and Innovation Centre, Fondazione E. Mach, Trento, Italy.

Methods in Molecular Biology (Clifton, N.J.)
|April 15, 2018
PubMed
Summary
This summary is machine-generated.

Data preprocessing is essential for liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics. Optimizing these procedures maximizes the information extracted from complex experimental data for accurate statistical analysis.

Keywords:
MetadataMissing valuesPeak pickingPreprocessingQuality checkRetention time correction

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Chemometrics

Background:

  • Untargeted liquid chromatography-mass spectrometry (LC-MS) experiments generate complex datasets.
  • Extracting meaningful biological insights requires sophisticated data analysis strategies.

Purpose of the Study:

  • To highlight the critical role of data preprocessing in untargeted metabolomics.
  • To emphasize the need for careful control and optimization of preprocessing steps.

Main Methods:

  • Discussion of data preprocessing techniques.
  • Focus on procedures transforming raw LC-MS data into a usable data matrix.

Main Results:

  • Data preprocessing is a foundational step for statistical analysis.
  • Properly executed preprocessing is key to successful knowledge extraction.

Conclusions:

  • Optimizing data preprocessing is crucial for maximizing the output of untargeted metabolomics investigations.
  • Careful control over these procedures enhances the reliability of downstream analyses.