Brain Structure and Function Discover Mouse Gene Co-expression Landscapes Using Dictionary Learning and Sparse Coding

Gene coexpression patterns carry a rich amount of valuable information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated and anatomically comprehensive manner will illuminate the higher order transcriptome organization and offer genetic foundations of functional circuitry. Here we demonstrate a data-driven method to extract coexpression networks from transcriptome profiles in the Allen Mouse Brain Atlas dataset. For each of the obtained networks, both the genetic compositions and the spatial distributions in brain volume are learned. A simultaneous knowledge of spatial distributions of a specific gene, the networks in which the gene plays and the weights it carries, can bring insights into the molecular mechanism of brain formation and functions. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways and/or anatomical regions. Suggested Reviewers: Shuiwang Ji Associate Professor, Washington State University Spokane sji@eecs.wsu. edu Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation Heng Huang Professor, University of Texas at Arlington heng@uta.edu Guorong Wu Assistant Professor, University of North Carolina at Chapel Hill guorong_wu@med.unc. edu Jing Zhang Assistant Professor, Georgia state university jing.maria. zhang@gmail.com Opposed Reviewers: Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation Discover Mouse Gene Co-expression Landscapes Using Dictionary Learning and Sparse Coding Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng, Joe Z. Tsien, Tianming Liu Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA; School of Automation, Northwestern Polytechnical University, Xi’an, China; Allen Institute for Brain Science, Seattle, WA, USA; Brain and Behavior Discovery Institute, Medical College of Georgia at Augusta University, USA; Co-first Authors, Joint Corresponding Authors. Abstract: Gene coexpression patterns carry a rich amount of valuable information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated and anatomically comprehensive manner will illuminate the higher order transcriptome organization and offer genetic foundations of functional circuitry. Here we demonstrate a data-driven method to extract coexpression networks from transcriptome profiles in the Allen Mouse Brain Atlas dataset. For each of the obtained networks, both the genetic compositions and the spatial distributions in brain volume are learned. A simultaneous knowledge of spatial distributions of a specific gene, the networks in which the gene plays and the weights it carries, can bring insights into the molecular mechanism of brain formation and functions. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways and/or anatomical regions. Gene coexpression patterns carry a rich amount of valuable information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated and anatomically comprehensive manner will illuminate the higher order transcriptome organization and offer genetic foundations of functional circuitry. Here we demonstrate a data-driven method to extract coexpression networks from transcriptome profiles in the Allen Mouse Brain Atlas dataset. For each of the obtained networks, both the genetic compositions and the spatial distributions in brain volume are learned. A simultaneous knowledge of spatial distributions of a specific gene, the networks in which the gene plays and the weights it carries, can bring insights into the molecular mechanism of brain formation and functions. Gene ontologies and the comparisons with published gene lists reveal biologically identified coexpression networks, some of which correspond to major cell types, biological pathways and/or anatomical regions.