Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes

  • Panagiota I Kontou
  • Athanasia Pavlopoulou
  • Pantelis G. BagosEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1793)


Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.

Key words

Gene expression Microarrays Differentially expressed genes Meta-analysis Statistical tests Multiple comparisons 


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

Authors and Affiliations

  • Panagiota I Kontou
    • 1
  • Athanasia Pavlopoulou
    • 1
    • 2
  • Pantelis G. Bagos
    • 1
    Email author
  1. 1.Department of Computer Science and Biomedical InformaticsUniversity of ThessalyLamiaGreece
  2. 2.International Biomedicine and Genome Institute (iBG-Izmir)Dokuz Eylul UniversityIzmirTurkey

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