Discover mouse gene coexpression landscape 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: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gene coexpression patterns carry rich information of complex brain structures and functions. Characterization of these patterns in an unbiased and integrated manner will illuminate the higher order transcriptome organization and offer molecular foundations of functional circuitry. Here we demonstrate a data-driven method that can effectively extract coexpression networks from transcriptome profiles using the Allen Mouse Brain Atlas dataset. For each of the obtained networks,both genetic compositions and spatial distributions in brain volume are learned. A simultaneous knowledge of precise spatial distributions of specific gene as well as the networks the gene plays in and the weights it carries can bring insights into the molecular mechanism of brain formation and functions. Gene ontologies and the comparisons with published data reveal interesting functions of the identified coexpression networks,including major cell types,biological functions,brain regions,and/or brain diseases.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Mert R. Sabuncu, William Wells, Sebastian Ourselin, Leo Joskowicz
PublisherSpringer Verlag
Pages63-71
Number of pages9
ISBN (Print)9783319467191
DOIs
StatePublished - Jan 1 2016
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: Oct 21 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period10/21/1610/21/16

Keywords

  • Gene coexpression network
  • Sparse coding
  • Transcriptome

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Li, Y., Chen, H., Jiang, X., Li, X., Lv, J., Peng, H., Tsien, J. Z., & Liu, T. (2016). Discover mouse gene coexpression landscape using dictionary learning and sparse coding. In G. Unal, M. R. Sabuncu, W. Wells, S. Ourselin, & L. Joskowicz (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 63-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_8