TY - JOUR
T1 - Discover mouse gene coexpression landscapes using dictionary learning and sparse coding
AU - Li, Yujie
AU - Chen, Hanbo
AU - Jiang, Xi
AU - Li, Xiang
AU - Lv, Jinglei
AU - Peng, Hanchuan
AU - Tsien, Joseph Zhuo
AU - Liu, Tianming
N1 - Funding Information:
Acknowledgements T. Liu is supported by NIH R01 DA-033393, NSF CAREER Award IIS-1149260, NIH R01 AG-042599, NSF BME-1302089, NSF BCS-1439051 and NSF DBI-1564736.
Publisher Copyright:
© 2017, Springer-Verlag GmbH Germany.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
KW - Gene coexpression network
KW - Sparse coding
KW - Transcriptome
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U2 - 10.1007/s00429-017-1460-9
DO - 10.1007/s00429-017-1460-9
M3 - Article
C2 - 28664394
AN - SCOPUS:85021738633
SN - 1863-2653
VL - 222
SP - 4253
EP - 4270
JO - Brain Structure and Function
JF - Brain Structure and Function
IS - 9
ER -