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01-12-2017 | Original Article

Discover mouse gene coexpression landscapes using dictionary learning and sparse coding

Authors: Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng, Joe Z. Tsien, Tianming Liu

Published in: Brain Structure and Function | Issue 9/2017

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.

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Allocco DJ, Kohane IS, Butte AJ (2004) Quantifying the relationship between co-expression, co-regulation and gene function. BMC Bioinform 5:18. doi:10.1186/1471-2105-5-18 CrossRef

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 CrossRefPubMedPubMedCentral

Bernard A, Lubbers LS, Tanis KQ, Luo R, Podtelezhnikov AA, Finney EM et al (2012) Transcriptional architecture of the primate neocortex. Neuron 73(6):1083–1099. doi:10.1016/j.neuron.2012.03.002.Transcriptional CrossRefPubMedPubMedCentral

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 CrossRefPubMedPubMedCentral

Bohland JW, Bokil H, Pathak SD, Lee C-K, Ng L, Lau C et al (2010) Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy. Methods 50(2):105–112. doi:10.1016/j.ymeth.2009.09.001 CrossRefPubMed

Brown CD, Johnson DS, Sidow A (2007) Functional architecture and evolution of transcriptional elements that drive gene coexpression. Science 317(September):1557–1560CrossRefPubMed

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 CrossRef

Carter H, Hofree M, Ideker T (2013) Genotype to phenotype via network analysis. Curr Opin Genet Dev 23(6):611–621. doi:10.1016/j.gde.2013.10.003 CrossRefPubMed

Chen H, Li K, Zhu D, Jiang X, Yuan Y, Lv P et al (2013) Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. IEEE Trans Med Imaging 32(9):1576–1586. doi:10.1109/TMI.2013.2259248 CrossRefPubMedPubMedCentral

Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3. doi:10.1186/gb-2003-4-5-p3 CrossRefPubMed

Dobrin R, Zhu J, Molony C, Argman C, Parrish ML, Carlson S et al (2009) Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease. Genome Biol 10(5):R55. doi:10.1186/gb-2009-10-5-r55 CrossRefPubMedPubMedCentral

Dong S, Li C, Wu P, Tsien JZ, Hu Y (2007) Environment enrichment rescues the neurodegenerative phenotypes in presenilins-deficient mice. Eur J Neurosci 26(1):101–112. doi:10.1111/j.1460-9568.2007.05641.x CrossRefPubMed

Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407–499CrossRef

Eisen MB, Spellman PT, Brown PO, Botstein D (1999) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(22):12930–12933. doi:10.1073/pnas.95.25.14863

Gaiteri C, Ding Y, French B, Tseng GC, Sibille E (2014) Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav 13(1):13–24. doi:10.1111/gbb.12106 CrossRefPubMed

Ge H, Liu Z, Church GM, Vidal M (2001) Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet 29(4):482–486. doi:10.1038/ng776 CrossRefPubMed

Grange P, Bohland JW, Okaty BW, Sugino K, Bokil H, Nelson SB et al (2014) Cell-type-based model explaining coexpression patterns of genes in the brain. Proc Natl Acad Sci USA 111(14):5397–5402. doi:10.1073/pnas.1312098111 CrossRefPubMedPubMedCentral

Hawrylycz M, Bernard A, Lau C, Sunkin SM, Chakravarty MM, Lein ES et al (2010) Areal and laminar differentiation in the mouse neocortex using large scale gene expression data. Methods 50(2):113–121. doi:10.1016/j.ymeth.2009.09.005 CrossRefPubMed

Hawrylycz M, Miller JA, Menon V, Feng D, Dolbeare T, Guillozet-Bongaarts AL et al (2015) Canonical genetic signatures of the adult human brain. Nat Neurosci. doi:10.1038/nn.4171 PubMedPubMedCentral

Jiang CH, Tsien JZ, Schultz PG, Hu Y (2001) The effects of aging on gene expression in the hypothalamus and cortex of mice. PNAS 98(4):1930–1934. doi:10.1073/pnas.98.4.1930 CrossRefPubMedPubMedCentral

Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559. doi:10.1186/1471-2105-9-559 CrossRef

Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P (2004) Coexpression analysis of human genes across many microarray data sets. Genome Res 14:1085–1094. doi:10.1101/gr.1910904.1 CrossRefPubMedPubMedCentral

Lein ES, Zhao X, Gage FH (2004) Defining a molecular atlas of the hippocampus using DNA microarrays and high-throughput in situ hybridization. J Neurosci 24(15):3879–3889. doi:10.1523/JNEUROSCI.4710-03.2004 CrossRefPubMed

Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A, Bernard A et al (2007) Genome-wide atlas of gene expression in the adult mouse brain. Nature 445(7124):168–176. doi:10.1038/nature05453 CrossRefPubMed

Lu T, Pan Y, Kao S-Y, Li C, Kohane I, Chan J, Yankner BA (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429(June):883–891. doi:10.1038/nature02618.1 CrossRefPubMed

Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416. doi:10.1007/s11222-007-9033-z CrossRef

Mairal J, Elad M, Sapiro G (2008) Sparse representation for color image restoration. IEEE Trans Image Process 17(1):53–69. doi:10.1109/TIP.2007.911828 CrossRefPubMed

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

Miao H, Crabb AW, Hernandez MR, Lukas TJ (2010) Modulation of factors affecting optic nerve head astrocyte migration. Invest Ophthalmol Vis Sci 51(8):4096–4103. doi:10.1167/iovs.10-5177 CrossRefPubMedPubMedCentral

Miller J (2014) Transcriptional landscape of the prenatal human brain. Nature 508(7495):199–206. doi:10.1038/nature13185.Transcriptional CrossRefPubMedPubMedCentral

Miller JA, Horvath S, Geschwind DH (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci USA 107(28):12698–12703. doi:10.1073/pnas.0914257107 CrossRefPubMedPubMedCentral

Miller JA, Cai C, Langfelder P, Geschwind DH, Kurian SM, Salomon DR, Horvath S (2011) Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinform 12(1):322. doi:10.1186/1471-2105-12-322 CrossRef

Mody M, Cao Y, Cui Z, Tay KY, Shyong A, Shimizu E et al (2001) Genome-wide gene expression profiles of the developing mouse hippocampus. PNAS 98:8862–8867. doi:10.1073/pnas.141244998 CrossRefPubMedPubMedCentral

Molyneaux BJ, Arlotta P, Menezes JRL, Macklis JD (2007) Neuronal subtype specification in the cerebral cortex. Nat Rev Neurosci 8(6):427–437. doi:10.1038/nrn2151 CrossRefPubMed

Ng L, Pathak SD, Kuan C, Lau C, Dong H, Sodt A et al (2007) Neuroinformatics for genome-wide 3D gene expression mapping in the mouse brain. IEEE/ACM Trans Comput Biol Bioinf 4(3):382–392. doi:10.1109/TCBB.2007.1035 CrossRef

Ng L, Bernard A, Lau C, Overly CC, Dong H-W, Kuan C et al (2009) An anatomic gene expression atlas of the adult mouse brain. Nat Neurosci 12(3):356–362. doi:10.1038/nn.2281 CrossRefPubMed

O’Leary DD, Chou SJ, Sahara S (2007) Area patterning of the mammalian cortex. Neuron 56(2):252–269. doi:10.1016/j.neuron.2007.10.010 CrossRefPubMed

Oldham MC, Horvath S, Geschwind DH (2006) Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci USA 103(47):17973–17978. doi:10.1073/pnas.0605938103 CrossRefPubMedPubMedCentral

Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH (2008) Functional organization of the transcriptome in human brain. Nat Neurosci 11(11):1271–1282. doi:10.1038/nn.2207 CrossRefPubMedPubMedCentral

Oldham MC, Langfelder P, Horvath S (2012) Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Syst Biol 6(1):63. doi:10.1186/1752-0509-6-63 CrossRefPubMedPubMedCentral

Olshausen BA, Field DJ (2004) Sparse coding of sensory inputs. Curr Opin Neurobiol 14(4):481–487. doi:10.1016/j.conb.2004.07.007 CrossRefPubMed

Peng H, Long F, Zhou J, Leung G, Eisen MB, Myers EW (2007) Automatic image analysis for gene expression patterns of fly embryos. BMC Cell Biol 8(Suppl 1):S7. doi:10.1186/1471-2121-8-S1-S7 CrossRefPubMedPubMedCentral

Quinones-Hinojosa A, Chaichana K (2007) The human subventricular zone: a source of new cells and a potential source of brain tumors. Exp Neurol 205(2):313–324. doi:10.1016/j.expneurol.2007.03.016 CrossRefPubMed

Rampon C, Jiang CH, Dong H, Tang YP, Lockhart DJ, Schultz PG et al (2000) Effects of environmental enrichment on gene expression in the brain. Proc Natl Acad Sci USA 97(23):12880–12884. doi:10.1073/pnas.97.23.12880 CrossRefPubMedPubMedCentral

Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302(5643):249–255. doi:10.1126/science.1087447 CrossRefPubMed

Sugino K, Hempel CM, Miller MN, Hattox AM, Shapiro P, Wu C et al (2006) Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat Neurosci 9(1):99–107. doi:10.1038/nn1618 CrossRefPubMed

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 CrossRefPubMedPubMedCentral

Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) Systematic determination of genetic network architecture. Nat Genet 22(3):281–285. doi:10.1038/10343 CrossRefPubMed

Winden KD, Oldham MC, Mirnics K, Ebert PJ, Swan CH, Levitt P et al (2009) The organization of the transcriptional network in specific neuronal classes. Mol Syst Biol 5(291):291. doi:10.1038/msb.2009.46 PubMedPubMedCentral

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

Title: Discover mouse gene coexpression landscapes using dictionary learning and sparse coding
Authors: Yujie Li
Hanbo Chen
Xi Jiang
Xiang Li
Jinglei Lv
Hanchuan Peng
Joe Z. Tsien
Tianming Liu
Publication date: 01-12-2017
Publisher: Springer Berlin Heidelberg
Published in: Brain Structure and Function / Issue 9/2017
Print ISSN: 1863-2653
Electronic ISSN: 1863-2661
DOI: https://doi.org/10.1007/s00429-017-1460-9

At a glance: The STEP trials

Springer Medicine

Discover mouse gene coexpression landscapes using dictionary learning and sparse coding

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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