Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas

Natalie Weed, Trygve Bakken, Nile Graddis, Nathan Gouwens, Daniel Millman, Michael Hawrylycz and Jack Waters

PLOS ONE, 2019, vol. 14, issue 9, 1-13

Abstract: The mammalian neocortex is subdivided into a series of cortical areas that are functionally and anatomically distinct and are often distinguished in brain sections using histochemical stains and other markers of protein expression. We searched the Allen Mouse Brain Atlas, a database of gene expression, for novel markers of cortical areas. To screen for genes that change expression at area borders, we employed a random forest algorithm and binary region classification. Novel genetic markers were identified for 19 of 39 areas and provide code that quickly and efficiently searches the Allen Mouse Brain Atlas. Our results demonstrate the utility of the random forest algorithm for cortical area classification and we provide code that may be used to facilitate the identification of genetic markers of cortical and subcortical structures and perhaps changes in gene expression in disease states.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0212898

DOI: 10.1371/journal.pone.0212898

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