Discover mouse gene coexpression landscapes using dictionary learning and sparse coding

Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng, Joseph Zhuo Tsien, Tianming Liu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4253-4270
Number of pages18
JournalBrain Structure and Function
Issue number9
StatePublished - Dec 1 2017


  • Gene coexpression network
  • Sparse coding
  • Transcriptome

ASJC Scopus subject areas

  • Anatomy
  • Neuroscience(all)
  • Histology


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