Computational neuroanatomy and gene expression: optimal sets of marker genes for brain regions

TL;DR

This paper develops quantitative methods to identify optimal sets of marker genes for brain regions using the Allen Gene Expression Atlas, enhancing neuroanatomical mapping accuracy.

Contribution

It introduces criteria for ranking and selecting gene sets as markers based on localization and shape fitting, generalizing to sets with positive and negative weights.

Findings

Sets of weighted genes achieve near-perfect localization in major brain regions.

Generalized criteria produce sparser gene sets with comparable localization.

Method improves identification of brain region markers using gene expression data.

Abstract

The three-dimensional data-driven Allen Gene Expression Atlas of the adult mouse brain consists of numerized in-situ hybridization data for thousands of genes, co-registered to the Allen Reference Atlas. We propose quantitative criteria to rank genes as markers of a brain region, based on the localization of the gene expression and on its functional fitting to the shape of the region. These criteria lead to natural generalizations to sets of genes. We find sets of genes weighted with coefficients of both signs with almost perfect localization in all major regions of the left hemisphere of the brain, except the pallidum. Generalization of the fitting criterion with positivity constraint provides a lesser improvement of the markers, but requires sparser sets of genes.