Advertisement

Brain Structure and Function

, Volume 222, Issue 9, pp 4253–4270 | Cite as

Discover mouse gene coexpression landscapes using dictionary learning and sparse coding

  • Yujie Li
  • Hanbo Chen
  • Xi Jiang
  • Xiang Li
  • Jinglei Lv
  • Hanchuan Peng
  • Joe Z. Tsien
  • Tianming Liu
Original Article

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.

Keywords

Gene coexpression network Sparse coding Transcriptome 

Notes

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.

Supplementary material

429_2017_1460_MOESM1_ESM.docx (42.9 mb)
Supplementary material 1 (DOCX 43955 kb)

References

  1. 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 CrossRefGoogle Scholar
  2. 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
  3. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 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
  5. 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 CrossRefPubMedGoogle Scholar
  6. Brown CD, Johnson DS, Sidow A (2007) Functional architecture and evolution of transcriptional elements that drive gene coexpression. Science 317(September):1557–1560CrossRefPubMedGoogle Scholar
  7. 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
  8. 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 CrossRefPubMedGoogle Scholar
  9. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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 CrossRefPubMedGoogle Scholar
  11. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 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 CrossRefPubMedGoogle Scholar
  13. Efron B, Hastie T, Johnstone I, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407–499CrossRefGoogle Scholar
  14. 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 Google Scholar
  15. 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 CrossRefPubMedGoogle Scholar
  16. 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 CrossRefPubMedGoogle Scholar
  17. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 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 CrossRefPubMedGoogle Scholar
  19. 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 PubMedPubMedCentralGoogle Scholar
  20. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 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 CrossRefGoogle Scholar
  22. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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 CrossRefPubMedGoogle Scholar
  24. 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 CrossRefPubMedGoogle Scholar
  25. 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 CrossRefPubMedGoogle Scholar
  26. Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416. doi: 10.1007/s11222-007-9033-z CrossRefGoogle Scholar
  27. 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 CrossRefPubMedGoogle Scholar
  28. 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
  29. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Miller J (2014) Transcriptional landscape of the prenatal human brain. Nature 508(7495):199–206. doi: 10.1038/nature13185.Transcriptional CrossRefPubMedPubMedCentralGoogle Scholar
  31. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 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 CrossRefGoogle Scholar
  33. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 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 CrossRefPubMedGoogle Scholar
  35. 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 CrossRefGoogle Scholar
  36. 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 CrossRefPubMedGoogle Scholar
  37. 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 CrossRefPubMedGoogle Scholar
  38. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 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 CrossRefPubMedGoogle Scholar
  42. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 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 CrossRefPubMedGoogle Scholar
  44. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 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 CrossRefPubMedGoogle Scholar
  46. 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 CrossRefPubMedGoogle Scholar
  47. 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
  48. 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 CrossRefPubMedGoogle Scholar
  49. 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 PubMedPubMedCentralGoogle Scholar
  50. 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

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensUSA
  2. 2.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Allen Institute for Brain ScienceSeattleUSA
  4. 4.Brain and Behavior Discovery InstituteMedical College of Georgia at Augusta UniversityAugustaUSA

Personalised recommendations