Skip to main content
SpringerLink
Log in

Understanding the Functionality of a Biological System as a Whole: Comparative Data Analysis

  • Protocol
  • First Online:
Plant Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1778))

  • 2527 Accesses

Abstract

As part of a systems biology approach, metabolomics often aim at broadening our understanding of the functionality of biological systems as a whole. Observations from stand-alone experiments may reveal interesting changes in metabolites of a specific pathway or metabolite class. However, bringing these observations into context with more general biological processes requires the integration and comparison of different datasets. This chapter aims at introducing and explaining methods of comparative data analysis for plant metabolomics using the statistical software framework R.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tilly C (1984) Big structures, large processes, huge comparisons. Russell Sage Foundation, New York

    Google Scholar 

  2. Pickvance CG (2001) Four varieties of comparative analysis. J Housing Built Environ 16:7–28

    Article  Google Scholar 

  3. Stekhoven DJ, Bühlmann P (2012) MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28:112–118

    Article  CAS  PubMed  Google Scholar 

  4. Orf I, Timm S, Bauwe H et al (2016) Can cyanobacteria serve as a model of plant photorespiration? A comparative meta-analysis of metabolite profiles. J Exp Bot 67:2941–2952

    Article  CAS  PubMed  Google Scholar 

  5. Steinhaus H (1956) Sur la division des corp materiels en parties. Bull Acad Polon Sci 1:801–804

    Google Scholar 

  6. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1: Statistics. University of California Press, Berkeley, CA

    Google Scholar 

  7. Reynolds AP, Richards G, de la Iglesia B, Rayward-Smith VJ (2006) Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. J Math Model Algorithm 5:475–504

    Article  Google Scholar 

  8. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(C):53–65

    Article  Google Scholar 

  9. Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3:1–27

    Google Scholar 

  10. Kass RE, Wasserman L (1995) A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. J Am Stat Assoc 90:928–934

    Article  Google Scholar 

  11. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315:972–976

    Article  CAS  PubMed  Google Scholar 

  12. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Statist Soc B 63:411–423

    Article  Google Scholar 

  13. Liu Y, Li Z, Xiong H et al (2010) Understanding of internal clustering validation measures. In: Proceedings of the 2010 I.E. international conference on data mining. IEEE Computer Society, pp 911–916

    Google Scholar 

  14. Warnes G, Bolker B, Bonebakker L, Gentleman R, Liaw WHA, Lumley T, Maechler M, Magnusson A, Moeller S, Schwartz M, Venables B (2016) gplots: various R programming tools for plotting data. R package version 3.0.1

    Google Scholar 

  15. Hijmans RJ, van Etten J (2016) raster: geographic data analysis and modeling. R package version 2.5–8

    Google Scholar 

  16. Feltz CJ, Miller GE (1996) An asymptotic test for the equality of coefficients of variation from k populations. Stat Med 15:646–658

    Article  CAS  PubMed  Google Scholar 

  17. Krishnamoorthy K, Lee M (2014) Improved tests for the equality of normal coefficients of variation. Comput Stat 29:215–232

    Article  Google Scholar 

  18. Marwick B (2016) cvequality: tests for the equality of coefficients of variation from multiple groups. R package version 0.1.1

    Google Scholar 

  19. Robert P, Escoufier Y (1976) A unifying tool for linear multivariate statistical methods: The RV-coefficient. J R Statist Soc C 25:257–265

    Google Scholar 

  20. Husson F, Josse J, Lê S, Mazet J (2017) FactoMineR: multivariate exploratory data analysis and data mining. R package version 1.38

    Google Scholar 

  21. Mahalanobis PC (1936) On the generalised distance in statistics. In: Proceedings National Institute of Science, India

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Minerva Fellowship Program of the Max Planck Society.

Author information

Authors and Affiliations

  1. Ben-Gurion University of the Negev, Jacob Blaustein Institutes for Desert Research, French Associates Institute for Agriculture & Biotechnology of Drylands, Midreshet Ben-Gurion, Israel

    Isabel Orf

  2. Bactevo Ltd, The Merrifield Centre, Rosemary Lane, Cambridge, UK

    Isabel Orf

Authors
  1. Isabel Orf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isabel Orf .

Editor information

Editors and Affiliations

  1. Plant Metabolomics Laboratory, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, Portugal

    Carla António

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Orf, I. (2018). Understanding the Functionality of a Biological System as a Whole: Comparative Data Analysis. In: António, C. (eds) Plant Metabolomics. Methods in Molecular Biology, vol 1778. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7819-9_22

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7819-9_22

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7818-2

  • Online ISBN: 978-1-4939-7819-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation