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MSPrep--summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data.

Grant Hughes1, Charmion Cruickshank-Quinn, Richard Reisdorph

  • 1Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO, Department of Immunology, National Jewish Health Center, Denver, CO, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN and Department of Pulmonary Medicine, National Jewish Health Center, Denver, CO, USA.

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A new R package, MSPrep, streamlines metabolomic data analysis by adding summarization, filtering, and normalization steps. It offers diagnostic tools to aid researchers in interpreting their findings effectively.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Computational Biology

Background:

  • Existing R packages for metabolomic data pre-processing lack essential downstream analysis steps.
  • Key steps like data summarization, filtering, and normalization are often performed manually or with disparate tools.

Purpose of the Study:

  • To introduce MSPrep, an R package designed to integrate crucial post-alignment analysis for metabolomic data.
  • To provide a comprehensive solution for metabolomic data summarization, filtering, and normalization within a single R package.

Main Methods:

  • Development of the MSPrep R package.
  • Implementation of popular normalization algorithms within MSPrep.
  • Generation of diagnostic plots to guide data analysis.

Main Results:

  • MSPrep successfully integrates summarization, filtering, and normalization capabilities for aligned metabolomic data.
  • The package includes multiple normalization methods to accommodate diverse experimental needs.
  • Diagnostic tools are generated to assist users in selecting appropriate analysis parameters.

Conclusions:

  • MSPrep enhances the pre-processing pipeline for metabolomic data analysis in R.
  • The package offers a user-friendly and efficient approach to essential data analysis steps.
  • MSPrep facilitates more robust and reproducible metabolomic studies.