Microarray Bioinformatics

  • Robert P. Loewe
  • Peter J. NelsonEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 671)


Bioinformatics has become an increasingly important tool for molecular biologists, especially for the analysis of microarray data. Microarrays can produce vast amounts of information requiring a series of consecutive analyses to render the data interpretable. The direct output of microarrays cannot be directly interpreted to show differences in settings, conditions of samples, or time points. To make microarray experiments interpretable, it is necessary that a series of algorithms and approaches be applied. After normalization of generated data, which is necessary to make a comparison feasible, significance analysis, clustering of samples and biological compounds of interest and visualization are generally performed. This chapter will focus on providing a basic understanding of the generally approaches and algorithms currently employed in microarray bioinformatics.

Key words:

Microarray Bioinformatics Normalization Clustering SAM RMA PCA 



This work was supported by the Deutsche Forschungsgemeinschaft SFB 571 C2, FP6 EU grant INNOCHEM to PJN and BMBF BioChance to PJN.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Medical Policlinic, Ludwig MaximilliansUniversity of MunichMunichGermany

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