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Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses.

Harsha Gowda1, Julijana Ivanisevic, Caroline H Johnson

  • 1Scripps Center for Metabolomics and Mass Spectrometry and ‡Department of Cell Biology, The Scripps Research Institute , 10550 North Torrey Pines Road, La Jolla, California 92037, United States.

Analytical Chemistry
|June 18, 2014
PubMed
Summary
This summary is machine-generated.

XCMS Online now supports paired, meta, and multigroup metabolomic data analysis. This enhanced platform offers interactive visualization and comprehensive statistical output for complex mass spectrometry data.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Computational Biology

Background:

  • XCMS Online is a cloud-based platform for processing untargeted metabolomic data.
  • The initial version focused on independent two-group comparisons (e.g., control vs. disease).

Purpose of the Study:

  • To introduce an enhanced XCMS Online interface with expanded analytical capabilities.
  • To enable dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons.
  • To provide comprehensive statistical output and interactive visualization tools for complex metabolomic datasets.

Main Methods:

  • Incorporation of new statistical tests for univariate analyses.
  • Development of multigroup comparison for identifying differentially expressed metabolites across multiple data classes.
  • Implementation of higher-order meta-analysis for detecting shared metabolic patterns.
  • Creation of an interactive platform for real-time monitoring of statistical outputs (cloud plots, PCA plots) by adjusting parameters.

Main Results:

  • The enhanced interface supports paired, meta, and multigroup analyses.
  • Interactive tools allow real-time data exploration and filtering based on significance, fold change, and spectral properties.
  • Visualization options include cloud plots, PCA plots, extracted-ion chromatograms, and box plots.
  • Demonstrated utility in analyzing bacterial stress response and lymphoblastic leukemia cell line metabolomics.

Conclusions:

  • The enhanced XCMS Online platform significantly expands the analytical power for metabolomic data.
  • Interactive features facilitate deeper insights into complex biological systems through advanced statistical analysis and visualization.
  • The updated tool supports a wider range of experimental designs, from simple comparisons to complex meta-analyses.