Analysis of Global Gene Expression Profiles

  • Alboukadel Kassambara
  • Jerome MoreauxEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1792)


DNA microarrays have considerably helped to improve the understanding of biological processes and diseases including multiple myeloma (MM). GEP analyses have been successful to classify MM, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance.

This generated large amounts of publicly available data that could benefit from easy-to-use bioinformatics resources to analyze them. Here we present easy-to-use and open-access bioinformatics tools to extract and visualize the most prominent information from GEP data.

Key words

Microarrays Data mining Bioinformatics Multiple myeloma GenomicScape Molecular heterogeneity 



This work was supported by grants from French INCA (Institut National du Cancer) Institute (PLBIO15-256) and ITMO Cancer (MM&TT).


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

Authors and Affiliations

  1. 1.Department of Biological HematologyCHU MontpellierMontpellierFrance
  2. 2.Institute of Human Genetics, UMR 9002, CNRS and University of MontpellierMontpellierFrance
  3. 3.UFR de Médecine, University of MontpellierMontpellierFrance
  4. 4.Department of Biological Hematology, Laboratory for Monitoring Innovative TherapiesHopital Saint-Eloi—CHRU de MontpellierMontpellierFrance

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