Mesoscale transcriptome analysis

  • Alessandro Cellerino
  • Michele Sanguanini
Part of the CRM Series book series (PSNS, volume 17)


In the current chapter, we will discuss some publications that applied the previously described data analysis methods to genome-scale expression datasets in order to investigate aspects of brain organisation at the mesoscale level (i.e. at the level of areas and their connections). The high-throughput technique for quantification of gene expression used in most of these works is the cDNA microarray that, until very recently, represented the technique of choice to obtain genome-scale gene expression data. The output of a microarray experiment is an n×r matrix, where n is the number of (oligonucleotide) probes printed in the microarray chip (note that multiple probes may be associated with a single mRNA) and r is the number of samples (brain regions) analyzed, and the elements of the matrix are normalised hybridisation signal intensities for the different probes (again, a single transcript may be represented by multiple probes). The structure of the dataset is very similar to the output of RNA-seq, so the down-stream analysis uses the same methods. A notable difference is that normalised signal intensities for microarrays are near-normally distributed.


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Copyright information

© Scuola Normale Superiore Pisa 2018

Authors and Affiliations

  • Alessandro Cellerino
    • 1
  • Michele Sanguanini
    • 2
  1. 1.Scuola Normale SuperiorePisaItaly
  2. 2.Gonville and Caius CollegeUniversity of CambridgeCambridge, CambridgeshireUnited Kingdom

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