Integrated data analysis for genome-wide research

  • Matthias Steinfath
  • Dirk Repsilber
  • Matthias Scholz
  • Dirk Walther
  • Joachim Selbig
Part of the Experientia Supplementum book series (EXS, volume 97)


Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different ‘omicsrs datasets, i.e., genome-wide measurements of transcripts, protein levels or protein—protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques.


Mutual Information Independent Component Analysis Canonical Correlation Analysis Independent Component Analysis Biological Organisation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Birkhäuser Verlag/Switzerland 2007

Authors and Affiliations

  • Matthias Steinfath
    • 1
  • Dirk Repsilber
    • 1
  • Matthias Scholz
    • 2
  • Dirk Walther
    • 2
  • Joachim Selbig
    • 1
    • 2
  1. 1.Institute for Biology and BiochemistryUniversity PotsdamPotsdam-GolmGermany
  2. 2.Max Planck Institute of Molecular Plant PhysiologyPotsdam-GolmGermany

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