Data Integration

  • Aaron Fait
  • Alisdair R. Fernie

In the last decade, an unprecedented amount of post-genomic experimental information has become available. Datasets originating from transcriptomic analysis, metabolite profiling, and proteomics can be produced faster, with ever increasing accuracy and decreasing cost. However, putting the pieces together is not trivial. Our understanding of cellular phenomena based on “omics” data depends on – and is limited by – our capability to implement appropriate analysis tools able to integrate the different “omics” approaches [75, 78]. Bringing together such disparate datasets presents a considerable challenge [76]. Such analysis is time consuming and prone to both error and speculation. Consequently, there is a substantial need to consider both the methods currently being used and the statistical principles involved in the analysis of post-genomic experimental data.


Independent Component Analysis Metabolic Network Principle Component Analysis Cold Acclimation Canonical Correlation Analysis 
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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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Ben-Gurion University of the Negev, Jacob Blaustein Insts. for Desert Research, French Associates Institute for Agriculture & Biotechnology of DrylandsIsrael
  2. 2.Max Planck Institute for Molecular Plant PhysiologyGermany

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