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Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine.

Sanjeevan Jahagirdar1, Edoardo Saccenti1

  • 1Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands.

Journal of Proteome Research
|December 3, 2020
PubMed
Summary

Single sample network analysis methods, LIONESS and ssPCC, show promise for personalized medicine by revealing individual-specific metabolite-metabolite associations in metabolomics data.

Keywords:
biological networkscorrelationnecrotizing soft tissue infectionsnetwork inference

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Area of Science:

  • Systems biology
  • Metabolomics
  • Personalized medicine

Background:

  • Networks are key in systems biology but typically lose individual-specific information.
  • Constructing sample-specific networks from molecular profiles is gaining interest for personalized medicine.

Purpose of the Study:

  • To evaluate and compare LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) for metabolite-metabolite association networks in metabolomics.
  • To explore the characteristics of these methods using simulated, dynamic model, and real-world metabolomic datasets.
  • To demonstrate the application of single sample network inference in a clinical case study.

Main Methods:

  • Comparison of LIONESS and ssPCC methods.
  • Application to simulated data, data from a dynamic metabolic model, and 22 metabolomic datasets.
  • Case study application to necrotizing soft tissue infections data.

Main Results:

  • Both LIONESS and ssPCC were evaluated in the context of metabolomics.
  • The methods were tested on diverse datasets, including simulated and real-world metabolomic data.
  • Adaptations for data exploration were proposed.

Conclusions:

  • Single sample network inference is a promising tool for metabolomics data analysis.
  • These methods offer potential for systems and personalized medicine by providing individual-specific insights.
  • Further exploration and adaptations can enhance their utility in data exploration.