Error analysis and propagation in metabolomics data analysis

Research output: Contribution to journalReview articlepeer-review

32 Scopus citations

Abstract

Error analysis plays a fundamental role in describing the uncertainty in experimental results. It has several fundamental uses in metabolomics including experimental design, quality control of experiments, the selection of appropriate statistical methods, and the determination of uncertainty in results. Furthermore, the importance of error analysis has grown with the increasing number, complexity, and heterogeneity of measurements characteristic of 'omics research. The increase in data complexity is particularly problematic for metabolomics, which has more heterogeneity than other omics technologies due to the much wider range of molecular entities detected and measured. This review introduces the fundamental concepts of error analysis as they apply to a wide range of metabolomics experimental designs and it discusses current methodologies for determining the propagation of uncertainty in appropriate metabolomics data analysis. These methodologies include analytical derivation and approximation techniques, Monte Carlo error analysis, and error analysis in metabolic inverse problems. Current limitations of each methodology with respect to metabolomics data analysis are also discussed.

Original languageEnglish
Article numbere201301006
Pages (from-to)e201301006
JournalComputational and Structural Biotechnology Journal
Volume4
Issue number5
DOIs
StatePublished - Jan 2013

Bibliographical note

Funding Information:
This work was supported in part by NIH grant NCRR P20RR016481S1 , DOE grant DE-EM0000197 , and NIH NIEHS grant 1R01ES022191-01 .

Keywords

  • Error analysis
  • Error propagation
  • Mass spectrometry
  • Metabolomics
  • Nuclear magnetic resonance

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

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