Performance Comparison of Multiple Microarray Platforms for Gene Expression Profiling

  • Fang Liu
  • Winston P. Kuo
  • Tor-Kristian Jenssen
  • Eivind HovigEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 802)


With genome-wide gene expression microarrays being increasingly applied in various areas of biomedical research, the diversity of platforms and analytical methods has made comparison of data from multiple platforms very challenging. In this chapter, we describe a generalized framework for systematic comparisons across gene expression profiling platforms, which could accommodate both the available commercial arrays and “in-house” platforms, with both one-dye and two-dye platforms. It includes experimental design, data preprocessing protocols, cross-platform gene matching approaches, measures of data consistency comparisons, and considerations in biological validation. In the design of this framework, we considered the variety of platforms available, the need for uniform quality control procedures, real-world practical limitations, statistical validity, and the need for flexibility and extensibility of the framework. Using this framework, we studied ten diverse microarray platforms, and we conclude that using probe sequences matched at the exon level is important to improve cross-platform data consistency compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values, as confirmed by QRT-PCR. After stringent preprocessing, commercial arrays were more consistent than “in-house” arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.

Key words

Microarray Gene expression profiling Bioinformatics Data consistency Probe matching 



The authors would like to thank all the microarray vendors and facilities/laboratories which have actively participated this large-scale study. The authors were supported by the functional genomics program (FUGE) in the Research council of Norway for this work.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Fang Liu
    • 1
    • 2
  • Winston P. Kuo
    • 3
    • 4
  • Tor-Kristian Jenssen
    • 2
  • Eivind Hovig
    • 5
    Email author
  1. 1.Department of Tumor BiologyInstitute for Cancer Research, Norwegian Radium HospitalMontebelloNorway
  2. 2.PubGene ASVinderenNorway
  3. 3.Harvard Catalyst – Laboratory for Innovative TranslationalTechnologies, Harvard Medical SchoolBostonUSA
  4. 4.Department of Developmental BiologyHarvard School of Dental MedicineBostonUSA
  5. 5.Departments of Tumor Biology and Medical InformaticsInstitute for Cancer Research, Norwegian Radium HospitalMontebelloNorway

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