Document Type

Article

Publication Date

2011

DOI

10.1093/bioinformatics/btr558

Publication Title

Bioinformatics

Volume

27

Issue

23

Pages

3293-3299

Abstract

Motivation: The mammalian central nervous system (CNS) generates high-level behavior and cognitive functions. Elucidating the anatomical and genetic organizations in the CNS is a key step toward understanding the functional brain circuitry. The CNS contains an enormous number of cell types, each with unique gene expression patterns. Therefore, it is of central importance to capture the spatial expression patterns in the brain. Currently, genome-wide atlas of spatial expression patterns in the mouse brain has been made available, and the data are in the form of aligned 3D data arrays. The sheer volume and complexity of these data pose significant challenges for efficient computational analysis.

Results: We employ data reduction and network modeling techniques to explore the anatomical and genetic organizations in the mouse brain. First, to reduce the volume of data, we propose to apply tensor factorization techniques to reduce the data volumes. This tensor formulation treats the stack of 3D volumes as a 4D data array, thereby preserving the mouse brain geometry. We then model the anatomical and genetic organizations as graphical models. To improve the robustness and efficiency of network modeling, we employ stable model selection and efficient sparsity-regularized formulation. Results on network modeling show that our efforts recover known interactions and predicts novel putative correlations.

Comments

Web of Science: "Free full-text from publisher.'

Original Publication Citation

Ji, S. W. (2011). Computational network analysis of the anatomical and genetic organizations in the mouse brain. Bioinformatics, 27(23), 3293-3299. doi:10.1093/bioinformatics/btr558

Download

Share

COinS