Journal Article

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

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

2019

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adult / Algorithms / Animals / Area classification / Biochemistry / Biology and Life Sciences / Brain / Brain mapping / Cerebral Cortex - metabolism / Classification / Computer and Information Sciences / Forests / Gene expression / Gene Expression Profiling - methods / Genes / Genetic aspects / Genetic Markers / Humans / Identification and classification / Markers / Medicine and Health Sciences / Methods / Mice / Models, Biological / Models, Statistical / Neocortex / Novels / Physical Sciences / Research and Analysis Methods / Rodents / Social Sciences