Paper Info
Title
Discover Mouse Gene Coexpression Landscape Using Dictionary Learning and Sparse Coding.
Abstract
Gene coexpression patterns carry rich 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 using dictionary learning and sparse coding, we derived coexpression networks from the space-resolved anatomical comprehensive in situ hybridization data from Allen Mouse Brain Atlas dataset. The key idea is that if two genes use the same dictionary to represent their original signals, then their gene expressions must share similar patterns, thereby considering them as "coexpressed." For each network, we have simultaneous knowledge of spatial distributions, the genes in the network and the extent a particular gene conforms to the coexpression pattern. 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.
Year
DOI
Venue
2016
10.1007/s00429-017-1460-9
Brain structure & function
Keywords
Field
DocType
Gene coexpression network,Sparse coding,Transcriptome
Neuroscience,Brain atlas,Dictionary learning,Gene,Biology,Neural coding,Transcriptome,Brain size,Computational biology,Genetics,Transcriptome Profiles
Conference
Volume
Issue
ISSN
222.0
9
1863-2661
Citations 
PageRank 
References 
0
0.34
8
Authors
8
Name
Order
Citations
PageRank
Yujie Li125742.93
Hanbo Chen228727.40
Xi Jiang331137.88
Xiang Li441.55
Jinglei Lv520526.70
Hanchuan Peng63930182.27
Joe Tsien7162.45
Tianming Liu81033112.95