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Sparse Partial Least Squares Methods for Joint Modular Pattern Discovery

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eQTL Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2082))

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Abstract

The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. However, how to identify the multiple-to-multiple relationships between genomic factors and drug response among pharmacogenomics data is still a challenging issue. Here, we introduce a sparse partial least squares (SPLS) framework with or without the network-regularized penalty to identify joint modular patterns demonstrated with a large-scale pairwise gene-expression and drug-response data. The identified modular patterns reveal some coordinated gene–drug associations. SPLS methods could be applied to many biological problems such as the eQTL analysis, which is designed to discover genetic variants that influence downstream gene expression level. In summary, SPLS-based methods are a set of powerful tools to uncover the associations between different types of features.

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References

  1. Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6(10):813–823. https://doi.org/10.1038/nrc1951

    Article  CAS  PubMed  Google Scholar 

  2. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jane-Valbuena J, Mapa FA, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels IH, Cheng J, Yu GK, Yu JJ, Aspesi P, de Silva M, Jagtap K, Jones MD, Wang L, Hatton C, Palescandolo E, Gupta S, Mahan S, Sougnez C, Onofrio RC, Liefeld T, MacConaill L, Winckler W, Reich M, Li NX, Mesirov JP, Gabriel SB, Getz G, Ardlie K, Chan V, Myer VE, Weber BL, Porter J, Warmuth M, Finan P, Harris JL, Meyerson M, Golub TR, Morrissey MP, Sellers WR, Schlegel R, Garraway LA (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483(7391):603--609. https://doi.org/10.1038/nature11003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, Liu Q, Iorio F, Surdez D, Chen L, Milano RJ, Bignell GR, Tam AT, Davies H, Stevenson JA, Barthorpe S, Lutz SR, Kogera F, Lawrence K, McLaren-Douglas A, Mitropoulos X, Mironenko T, Thi H, Richardson L, Zhou W, Jewitt F, Zhang T, O’Brien P, Boisvert JL, Price S, Hur W, Yang W, Deng X, Butler A, Choi HG, Chang JW, Baselga J, Stamenkovic I, Engelman JA, Sharma SV, Delattre O, Saez-Rodriguez J, Gray NS, Settleman J, Futreal PA, Haber DA, Stratton MR, Ramaswamy S, McDermott U, Benes CH (2012) Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483(7391):570–575. https://doi.org/10.1038/nature11005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kutalik Z, Beckmann JS, Bergmann S (2008) A modular approach for integrative analysis of large-scale gene-expression and drug-response data. Nat Biotechnol 26(5):531–539. https://doi.org/10.1038/nbt1397

    Article  CAS  PubMed  Google Scholar 

  5. Chen J, Zhang S (2016) Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics 32(11):1724–1732. https://doi.org/10.1093/bioinformatics/btw059

    Article  CAS  PubMed  Google Scholar 

  6. Rosipal R, Kramer N (2006) Overview and recent advances in partial least squares. In: Saunders C., Grobelnik M, Gunn S, Shawe-Taylor J (eds) Subspace, latent structure and feature selection. SLSFS 2005. Lecture notes in computer science, vol 3940. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752790_2

    Chapter  Google Scholar 

  7. Li WY, Zhang SH, Liu CC, Zhou XJ (2012) Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics 28(19):2458–2466. https://doi.org/10.1093/bioinformatics/bts476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Albert FW, Kruglyak L (2015) The role of regulatory variation in complex traits and disease. Nat Rev Genet 16(4):197–212. https://doi.org/10.1038/nrg3891

    Article  CAS  PubMed  Google Scholar 

  9. Gilad Y, Rifkin SA, Pritchard JK (2008) Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet 24(8):408–415. https://doi.org/10.1016/j.tig.2008.06.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Michaelson JJ, Loguercio S, Beyer A (2009) Detection and interpretation of expression quantitative trait loci (eQTL). Methods 48(3):265–276. https://doi.org/10.1016/j.ymeth.2009.03.004

    Article  CAS  PubMed  Google Scholar 

  11. Lander ES, Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121(1):185–199. http://www.genetics.org/content/121/1/185.abstract

    Google Scholar 

  12. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324. https://doi.org/10.1038/hdy.1992.131

    Article  CAS  PubMed  Google Scholar 

  13. Broman KW, Speed TP (2002) A model selection approach for the identification of quantitative trait loci in experimental crosses. J Roy Stat Soc B 64:641–656. https://doi.org/10.1111/1467-9868.00354

    Article  Google Scholar 

  14. Bureau A, Dupuis J, Falls K, Lunetta KL, Hayward B, Keith TP, Van Eerdewegh P (2005) Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol 28(2):171–182. https://doi.org/10.1002/gepi.20041

    Article  PubMed  Google Scholar 

  15. Lee SSF, Sun L, Kustra R, Bull SB (2008) EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis. Bioinformatics 24(14):1603–1610. https://doi.org/10.1093/bioinformatics/btn239

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chun H, Keles S (2009) Expression quantitative trait loci mapping with multivariate sparse partial least squares regression. Genetics 182(1):79–90. https://doi.org/10.1534/genetics.109.100362

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res 39:D685–D690. https://doi.org/10.1093/nar/gkq1039

    Article  CAS  PubMed  Google Scholar 

  18. Chun H, Keles S (2010) Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J Roy Stat Soc B 72:3–25. https://doi.org/10.1111/j.1467-9868.2009.00723.x

    Article  Google Scholar 

  19. Li CY, Li HZ (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24(9):1175–1182. https://doi.org/10.1093/bioinformatics/btn081

    Article  CAS  PubMed  Google Scholar 

  20. Li C, Li H (2010) Variable selection and regression analysis for graph-structured covariates with an application to genomics. Ann Appl Stat 4(3):1498–1516. https://doi.org/10.1214/10-AOAS332

    Article  PubMed  PubMed Central  Google Scholar 

  21. Liu J, Huang J, Ma S (2013) Incorporating network structure in integrative analysis of cancer prognosis data. Genet Epidemiol 37(2):173–183. https://doi.org/10.1002/gepi.21697

    Article  PubMed  Google Scholar 

  22. Zhang SH, Li QJ, Liu J, Zhou XJ (2011) A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics 27(13):I401–I409. https://doi.org/10.1093/bioinformatics/btr206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  24. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu YF, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han BS, Zhou Y, Wishart DS (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42(D1):D1091–D1097. https://doi.org/10.1093/nar/gkt1068

    Article  CAS  PubMed  Google Scholar 

  25. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30. https://doi.org/10.1093/nar/28.1.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB, Klein TE (2012) Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 92(4):414–417. https://doi.org/10.1038/clpt.2012.96

    Article  CAS  PubMed  Google Scholar 

  27. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38:W214–W220. https://doi.org/10.1093/nar/gkq537

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR (2004) A census of human cancer genes. Nat Rev Cancer 4(3):177–183. https://doi.org/10.1038/nrc1299

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chen J, Zhang S (2018) Matrix integrative analysis (MIA) of multiple genomic data for modular patterns. Front Genet 9:194. https://doi.org/10.3389/fgene.2018.00194

    Google Scholar 

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Acknowledgment

This work has been supported by the National Natural Science Foundation of China, No. 61379092, 61422309, 61621003, and 11131009, the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDB13040600), the Outstanding Young Scientist Program of CAS, CAS Frontier Science Research Key Project for Top Young Scientist (No. QYZDB-SSW-SYS008), and the Key Laboratory of Random Complex Structures and Data Science, CAS (No. 2008DP173182).

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Authors and Affiliations

  1. NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Jinyu Chen & Shihua Zhang

Authors
  1. Jinyu Chen
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  2. Shihua Zhang
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Corresponding author

Correspondence to Shihua Zhang .

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Editors and Affiliations

  1. Temple University, Philadelphia, PA, USA

    Xinghua Mindy Shi

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Chen, J., Zhang, S. (2020). Sparse Partial Least Squares Methods for Joint Modular Pattern Discovery. In: Shi, X. (eds) eQTL Analysis. Methods in Molecular Biology, vol 2082. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0026-9_12

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  • DOI: https://doi.org/10.1007/978-1-0716-0026-9_12

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0025-2

  • Online ISBN: 978-1-0716-0026-9

  • eBook Packages: Springer Protocols

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