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

PLoS One. 2019 Sep 4;14(9):e0212898. doi: 10.1371/journal.pone.0212898. eCollection 2019.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Animals
  • Cerebral Cortex / metabolism*
  • Gene Expression Profiling* / methods
  • Genetic Markers*
  • Humans
  • Mice
  • Models, Biological*
  • Models, Statistical*

Substances

  • Genetic Markers

Grants and funding

The study was funded by The Allen Institute for Brain Science. All the authors were paid employees of The Allen Institute for Brain Science through the completion of the study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.