Discover mouse gene coexpression landscapes using dictionary learning and sparse coding
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.
KeywordsGene coexpression network Sparse coding Transcriptome
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.
- Bando SY, Silva FN, Costa LDF, Silva AV, Pimentel-Silva LR, Castro LH et al (2013) Complex network analysis of CA3 transcriptome reveals pathogenic and compensatory pathways in refractory temporal lobe epilepsy. PLoS One 8(11):e79913. doi: 10.1371/journal.pone.0079913 CrossRefPubMedPubMedCentralGoogle Scholar
- Blalock EM, Geddes JW, Chen KC, Porter NM, Markesbery WR, Landfield PW (2004) Incipient Alzheimer’s disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci USA 101(7):2173–2178. doi: 10.1073/pnas.0308512100 CrossRefPubMedPubMedCentralGoogle Scholar
- Cahoy J, Emery B, Kaushal A, Foo L, Zamanian J, Christopherson K et al (2004) A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J Neuronsci 28(1):264–278. doi: 10.1523/JNEUROSCI.4178-07.2008 CrossRefGoogle Scholar
- Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60. http://portal.acm.org/citation.cfm?id=1756008
- Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E et al (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96(6):2907–2912. doi: 10.1073/pnas.96.6.2907 CrossRefPubMedPubMedCentralGoogle Scholar
- Wright E, Ng L, Guillozet-Bongarts A (2007) Annotation report on cerebellar cortex, pukinje cell layer. http://community.brain-map.org/download/attachments/798/cbxpu.pdf?version=1