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Metabolomics

, Volume 11, Issue 6, pp 1492–1513 | Cite as

Computational and statistical analysis of metabolomics data

  • Sheng Ren
  • Anna A. Hinzman
  • Emily L. Kang
  • Rhonda D. Szczesniak
  • Long Jason Lu
Review Article

Abstract

Metabolomics is the comprehensive study of small molecule metabolites in biological systems. By assaying and analyzing thousands of metabolites in biological samples, it provides a whole picture of metabolic status and biochemical events happening within an organism and has become an increasingly powerful tool in the disease research. In metabolomics, it is common to deal with large amounts of data generated by nuclear magnetic resonance (NMR) and/or mass spectrometry (MS). Moreover, based on different goals and designs of studies, it may be necessary to use a variety of data analysis methods or a combination of them in order to obtain an accurate and comprehensive result. In this review, we intend to provide an overview of computational and statistical methods that are commonly applied to analyze metabolomics data. The review is divided into five sections. The first two sections will introduce the background and the databases and resources available for metabolomics research. The third section will briefly describe the principles of the two main experimental methods that produce metabolomics data: MS and NMR, followed by the fourth section that describes the preprocessing of the data from these two approaches. In the fifth and the most important section, we will review four main types of analysis that can be performed on metabolomics data with examples in metabolomics. These are unsupervised learning methods, supervised learning methods, pathway analysis methods and analysis of time course metabolomics data. We conclude by providing a table summarizing the principles and tools that we discussed in this review.

Keywords

Computational Statistical Unsupervised learning Supervised learning Pathway analysis Time course data 

Notes

Acknowledgments

We would like to express our great appreciation to Dr. Lilliam Ambroggio and Dr. Lindsey Romick-Rosendale for their valuable and constructive suggestions to our review. Their willingness to give their time so generously has been very much appreciated. This study is funded by the NIH Grant R01 HL116226 to RDS and LJL.

Compliance with Ethical Standards

Conflict of interest

Sheng Ren, Anna A. Hinzman, Emily L. Kang, Rhonda D. Szczesniak and L. Jason Lu declare that we have no conflict of interest and we have included separately signed conflict of interest forms in this manuscript.

Supplementary material

11306_2015_823_MOESM1_ESM.docx (1.4 mb)
Supplementary material 1 (DOCX 1395 kb)

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Sheng Ren
    • 1
    • 2
    • 3
  • Anna A. Hinzman
    • 2
    • 4
  • Emily L. Kang
    • 3
  • Rhonda D. Szczesniak
    • 5
    • 6
  • Long Jason Lu
    • 1
    • 2
    • 4
    • 6
    • 7
    • 8
  1. 1.Institute for Systems BiologyJianghan UniversityWuhanChina
  2. 2.Division of Biomedical InformaticsCincinnati Children’s Hospital Research FoundationCincinnatiUSA
  3. 3.Department of Mathematical Sciences, McMicken College of Arts & SciencesUniversity of CincinnatiCincinnatiUSA
  4. 4.Department of Biomedical Engineering, College of MedicineUniversity of CincinnatiCincinnatiUSA
  5. 5.Division of Pulmonary MedicineCincinnati Children’s Hospital Research FoundationCincinnatiUSA
  6. 6.Division of Biostatistics and EpidemiologyCincinnati Children’s Hospital Research FoundationCincinnatiUSA
  7. 7.Department of Environmental Health, College of MedicineUniversity of CincinnatiCincinnatiUSA
  8. 8.Department of Computer Science, College of MedicineUniversity of CincinnatiCincinnatiUSA

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