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Open Access Highly Accessed Research article

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering

Shuiwang Ji

Author Affiliations

Department of Computer Science, Old Dominion University, 4700 Elkhorn Avenue, Suite 3300, Norfolk, VA 23529-0162, USA

BMC Bioinformatics 2013, 14:222  doi:10.1186/1471-2105-14-222

Published: 11 July 2013



The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development.


In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space.


Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship.