Integrative Analysis of Omics Big Data

  • Xiang-Tian Yu
  • Tao Zeng
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)


The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.

Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is “bottom-up integration” mode with follow-up manual integration, and the other one is “top-down integration” mode with follow-up in silico integration.

This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.

Key words

Integration Omics High throughput Big data Complex diseases Bayesian Matrix decomposition Machine learning Subtype Precision medicine 


  1. 1.
    Field D, Sansone SA, Collis A, Booth T, Dukes P, Gregurick SK, Kennedy K, Kolar P, Kolker E, Maxon M, Millard S, Mugabushaka AM, Perrin N, Remacle JE, Remington K, Rocca-Serra P, Taylor CF, Thorley M, Tiwari B, Wilbanks J (2009) Megascience. ‘Omics data sharing’. Science 326(5950):234–236. Scholar
  2. 2.
    Vo TV, Das J, Meyer MJ, Cordero NA, Akturk N, Wei X, Fair BJ, Degatano AG, Fragoza R, Liu LG, Matsuyama A, Trickey M, Horibata S, Grimson A, Yamano H, Yoshida M, Roth FP, Pleiss JA, Xia Y, Yu H (2016) A proteome-wide fission yeast interactome reveals network evolution principles from yeasts to human. Cell 164(1–2):310–323. Scholar
  3. 3.
    Madhani HD, Francis NJ, Kingston RE, Kornberg RD, Moazed D, Narlikar GJ, Panning B, Struhl K (2008) Epigenomics: a roadmap, but to where? Science 322(5898):43–44. Scholar
  4. 4.
    Romanoski CE, Glass CK, Stunnenberg HG, Wilson L, Almouzni G (2015) Epigenomics: roadmap for regulation. Nature 518(7539):314–316. Scholar
  5. 5.
    Lage K, Karlberg EO, Storling ZM, Olason PI, Pedersen AG, Rigina O, Hinsby AM, Tumer Z, Pociot F, Tommerup N, Moreau Y, Brunak S (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25(3):309–316. Scholar
  6. 6.
    Nicholson JK, Lindon JC (2008) Systems biology: metabonomics. Nature 455(7216):1054–1056. Scholar
  7. 7.
    Rolland T, Tasan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, Mosca R, Kamburov A, Ghiassian SD, Yang X, Ghamsari L, Balcha D, Begg BE, Braun P, Brehme M, Broly MP, Carvunis AR, Convery-Zupan D, Corominas R, Coulombe-Huntington J, Dann E, Dreze M, Dricot A, Fan C, Franzosa E, Gebreab F, Gutierrez BJ, Hardy MF, Jin M, Kang S, Kiros R, Lin GN, Luck K, MacWilliams A, Menche J, Murray RR, Palagi A, Poulin MM, Rambout X, Rasla J, Reichert P, Romero V, Ruyssinck E, Sahalie JM, Scholz A, Shah AA, Sharma A, Shen Y, Spirohn K, Tam S, Tejeda AO, Trigg SA, Twizere JC, Vega K, Walsh J, Cusick ME, Xia Y, Barabasi AL, Iakoucheva LM, Aloy P, De Las Rivas J, Tavernier J, Calderwood MA, Hill DE, Hao T, Roth FP, Vidal M (2014) A proteome-scale map of the human interactome network. Cell 159(5):1212–1226. Scholar
  8. 8.
    Friedel CC, Zimmer R (2006) Toward the complete interactome. Nat Biotechnol 24(6):614–615.; Author reply 615. Scholar
  9. 9.
    Buxton B, Hayward V, Pearson I, Karkkainen L, Greiner H, Dyson E, Ito J, Chung A, Kelly K, Schillace S (2008) Big data: the next Google. Interview by Duncan Graham-Rowe. Nature 455(7209):8–9. Scholar
  10. 10.
    Kirk P, Griffin JE, Savage RS, Ghahramani Z, Wild DL (2012) Bayesian correlated clustering to integrate multiple datasets. Bioinformatics 28(24):3290–3297. Scholar
  11. 11.
    Mo Q, Wang S, Seshan VE, Olshen AB, Schultz N, Sander C, Powers RS, Ladanyi M, Shen R (2013) Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc Natl Acad Sci U S A 110(11):4245–4250. Scholar
  12. 12.
    Rapport DJ, Maffi L (2013) A call for integrative thinking. Science 339(6123):1032. Scholar
  13. 13.
    Wen Y, Wei Y, Zhang S, Li S, Liu H, Wang F, Zhao Y, Zhang D, Zhang Y (2016) Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature. Brief Bioinform.
  14. 14.
    Voillet V, Besse P, Liaubet L, San Cristobal M, Gonzalez I (2016) Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework. BMC Bioinformatics 17(1):402. Scholar
  15. 15.
    Weischenfeldt J, Simon R, Feuerbach L, Schlangen K, Weichenhan D, Minner S, Wuttig D, Warnatz HJ, Stehr H, Rausch T, Jager N, Gu L, Bogatyrova O, Stutz AM, Claus R, Eils J, Eils R, Gerhauser C, Huang PH, Hutter B, Kabbe R, Lawerenz C, Radomski S, Bartholomae CC, Falth M, Gade S, Schmidt M, Amschler N, Hass T, Galal R, Gjoni J, Kuner R, Baer C, Masser S, von Kalle C, Zichner T, Benes V, Raeder B, Mader M, Amstislavskiy V, Avci M, Lehrach H, Parkhomchuk D, Sultan M, Burkhardt L, Graefen M, Huland H, Kluth M, Krohn A, Sirma H, Stumm L, Steurer S, Grupp K, Sultmann H, Sauter G, Plass C, Brors B, Yaspo ML, Korbel JO, Schlomm T (2013) Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell 23(2):159–170. Scholar
  16. 16.
    Shen R, Mo Q, Schultz N, Seshan VE, Olshen AB, Huse J, Ladanyi M, Sander C (2012) Integrative subtype discovery in glioblastoma using iCluster. PLoS One 7(4):e35236. Scholar
  17. 17.
    Zeng T, Wang DC, Wang X, Xu F, Chen L (2014) Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling. Drug Resist Updates 17(3):64–76. Scholar
  18. 18.
    Shi X, Shen S, Liu J, Huang J, Zhou Y, Ma S (2014) Similarity of markers identified from cancer gene expression studies: observations from GEO. Brief Bioinform 15(5):671–684. Scholar
  19. 19.
    Shi X, Yi H, Ma S (2015) Measures for the degree of overlap of gene signatures and applications to TCGA. Brief Bioinform 16(5):735–744. Scholar
  20. 20.
    Bebek G, Koyuturk M, Price ND, Chance MR (2012) Network biology methods integrating biological data for translational science. Brief Bioinform 13(4):446–459. Scholar
  21. 21.
    Zhang S, Liu CC, Li W, Shen H, Laird PW, Zhou XJ (2012) Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res 40(19):9379–9391. Scholar
  22. 22.
    Liu Y, Devescovi V, Chen S, Nardini C (2013) Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties. BMC Syst Biol 7:14. Scholar
  23. 23.
    Hieke S, Benner A, Schlenl RF, Schumacher M, Bullinger L, Binder H (2016) Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information. BMC Bioinformatics 17(1):327. Scholar
  24. 24.
    Shen R, Olshen AB, Ladanyi M (2009) Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25(22):2906–2912. Scholar
  25. 25.
    Wang W, Baladandayuthapani V, Morris JS, Broom BM, Manyam G, Do KA (2013) iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics 29(2):149–159. Scholar
  26. 26.
    Yuan Y, Savage RS, Markowetz F (2011) Patient-specific data fusion defines prognostic cancer subtypes. PLoS Comput Biol 7(10):e1002227. Scholar
  27. 27.
    Speicher NK, Pfeifer N (2015) Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery. Bioinformatics 31(12):i268–i275. Scholar
  28. 28.
    Narayanan M, Vetta A, Schadt EE, Zhu J (2010) Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Comput Biol 6(4):e1000742. Scholar
  29. 29.
    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. Scholar
  30. 30.
    Le Van T, van Leeuwen M, Carolina Fierro A, De Maeyer D, Van den Eynden J, Verbeke L, De Raedt L, Marchal K, Nijssen S (2016) Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinformatics 32(17):i445–i454. Scholar
  31. 31.
    Seely JS, Kaufman MT, Ryu SI, Shenoy KV, Cunningham JP, Churchland MM (2016) Tensor analysis reveals distinct population structure that parallels the different computational roles of areas M1 and V1. PLoS Comput Biol 12(11):e1005164. Scholar
  32. 32.
    Hore V, Vinuela A, Buil A, Knight J, McCarthy MI, Small K, Marchini J (2016) Tensor decomposition for multiple-tissue gene expression experiments. Nat Genet 48(9):1094–1100. Scholar
  33. 33.
    Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C, Castellani G, Milanesi L (2016) Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17(Suppl 2):15. Scholar
  34. 34.
    Meng C, Zeleznik OA, Thallinger GG, Kuster B, Gholami AM, Culhane AC (2016) Dimension reduction techniques for the integrative analysis of multi-omics data. Brief Bioinform 17(4):628–641. Scholar
  35. 35.
    Luo Y, Wang F, Szolovits P (2016) Tensor factorization toward precision medicine. Brief Bioinform.
  36. 36.
    Vargas AJ, Harris CC (2016) Biomarker development in the precision medicine era: lung cancer as a case study. Nat Rev Cancer 16(8):525–537. Scholar
  37. 37.
    Lahti L, Schafer M, Klein HU, Bicciato S, Dugas M (2013) Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review. Brief Bioinform 14(1):27–35. Scholar
  38. 38.
    Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422):56–65. Scholar
  39. 39.
    Gerstein M (2012) Genomics: ENCODE leads the way on big data. Nature 489(7415):208. Scholar
  40. 40.
    Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, Laue ED, Tanay A, Fraser P (2013) Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502(7469):59–64. Scholar
  41. 41.
    Dekker J, Marti-Renom MA, Mirny LA (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet 14(6):390–403. Scholar
  42. 42.
    Yun X, Xia L, Tang B, Zhang H, Li F, Zhang Z (2016) 3CDB: a manually curated database of chromosome conformation capture data. Database (Oxford). Scholar
  43. 43.
    Teng L, He B, Wang J, Tan K (2016) 4DGenome: a comprehensive database of chromatin interactions. Bioinformatics 32(17):2727. Scholar
  44. 44.
    Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A (2013) NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41(Database issue):D991–D995. Scholar
  45. 45.
    Kim HS, Minna JD, White MA (2013) GWAS meets TCGA to illuminate mechanisms of cancer predisposition. Cell 152(3):387–389. Scholar
  46. 46.
    International Cancer Genome C, Hudson TJ, Anderson W, Artez A, Barker AD, Bell C, Bernabe RR, Bhan MK, Calvo F, Eerola I, Gerhard DS, Guttmacher A, Guyer M, Hemsley FM, Jennings JL, Kerr D, Klatt P, Kolar P, Kusada J, Lane DP, Laplace F, Youyong L, Nettekoven G, Ozenberger B, Peterson J, Rao TS, Remacle J, Schafer AJ, Shibata T, Stratton MR, Vockley JG, Watanabe K, Yang H, Yuen MM, Knoppers BM, Bobrow M, Cambon-Thomsen A, Dressler LG, Dyke SO, Joly Y, Kato K, Kennedy KL, Nicolas P, Parker MJ, Rial-Sebbag E, Romeo-Casabona CM, Shaw KM, Wallace S, Wiesner GL, Zeps N, Lichter P, Biankin AV, Chabannon C, Chin L, Clement B, de Alava E, Degos F, Ferguson ML, Geary P, Hayes DN, Hudson TJ, Johns AL, Kasprzyk A, Nakagawa H, Penny R, Piris MA, Sarin R, Scarpa A, Shibata T, van de Vijver M, Futreal PA, Aburatani H, Bayes M, Botwell DD, Campbell PJ, Estivill X, Gerhard DS, Grimmond SM, Gut I, Hirst M, Lopez-Otin C, Majumder P, Marra M, McPherson JD, Nakagawa H, Ning Z, Puente XS, Ruan Y, Shibata T, Stratton MR, Stunnenberg HG, Swerdlow H, Velculescu VE, Wilson RK, Xue HH, Yang L, Spellman PT, Bader GD, Boutros PC, Campbell PJ, Flicek P, Getz G, Guigo R, Guo G, Haussler D, Heath S, Hubbard TJ, Jiang T, Jones SM, Li Q, Lopez-Bigas N, Luo R, Muthuswamy L, Ouellette BF, Pearson JV, Puente XS, Quesada V, Raphael BJ, Sander C, Shibata T, Speed TP, Stein LD, Stuart JM, Teague JW, Totoki Y, Tsunoda T, Valencia A, Wheeler DA, Wu H, Zhao S, Zhou G, Stein LD, Guigo R, Hubbard TJ, Joly Y, Jones SM, Kasprzyk A, Lathrop M, Lopez-Bigas N, Ouellette BF, Spellman PT, Teague JW, Thomas G, Valencia A, Yoshida T, Kennedy KL, Axton M, Dyke SO, Futreal PA, Gerhard DS, Gunter C, Guyer M, Hudson TJ, McPherson JD, Miller LJ, Ozenberger B, Shaw KM, Kasprzyk A, Stein LD, Zhang J, Haider SA, Wang J, Yung CK, Cros A, Liang Y, Gnaneshan S, Guberman J, Hsu J, Bobrow M, Chalmers DR, Hasel KW, Joly Y, Kaan TS, Kennedy KL, Knoppers BM, Lowrance WW, Masui T, Nicolas P, Rial-Sebbag E, Rodriguez LL, Vergely C, Yoshida T, Grimmond SM, Biankin AV, Bowtell DD, Cloonan N, deFazio A, Eshleman JR, Etemadmoghadam D, Gardiner BB, Kench JG, Scarpa A, Sutherland RL, Tempero MA, Waddell NJ, Wilson PJ, McPherson JD, Gallinger S, Tsao MS, Shaw PA, Petersen GM, Mukhopadhyay D, Chin L, DePinho RA, Thayer S, Muthuswamy L, Shazand K, Beck T, Sam M, Timms L, Ballin V, Lu Y, Ji J, Zhang X, Chen F, Hu X, Zhou G, Yang Q, Tian G, Zhang L, Xing X, Li X, Zhu Z, Yu Y, Yu J, Yang H, Lathrop M, Tost J, Brennan P, Holcatova I, Zaridze D, Brazma A, Egevard L, Prokhortchouk E, Banks RE, Uhlen M, Cambon-Thomsen A, Viksna J, Ponten F, Skryabin K, Stratton MR, Futreal PA, Birney E, Borg A, Borresen-Dale AL, Caldas C, Foekens JA, Martin S, Reis-Filho JS, Richardson AL, Sotiriou C, Stunnenberg HG, Thoms G, van de Vijver M, van't Veer L, Calvo F, Birnbaum D, Blanche H, Boucher P, Boyault S, Chabannon C, Gut I, Masson-Jacquemier JD, Lathrop M, Pauporte I, Pivot X, Vincent-Salomon A, Tabone E, Theillet C, Thomas G, Tost J, Treilleux I, Calvo F, Bioulac-Sage P, Clement B, Decaens T, Degos F, Franco D, Gut I, Gut M, Heath S, Lathrop M, Samuel D, Thomas G, Zucman-Rossi J, Lichter P, Eils R, Brors B, Korbel JO, Korshunov A, Landgraf P, Lehrach H, Pfister S, Radlwimmer B, Reifenberger G, Taylor MD, von Kalle C, Majumder PP, Sarin R, Rao TS, Bhan MK, Scarpa A, Pederzoli P, Lawlor RA, Delledonne M, Bardelli A, Biankin AV, Grimmond SM, Gress T, Klimstra D, Zamboni G, Shibata T, Nakamura Y, Nakagawa H, Kusada J, Tsunoda T, Miyano S, Aburatani H, Kato K, Fujimoto A, Yoshida T, Campo E, Lopez-Otin C, Estivill X, Guigo R, de Sanjose S, Piris MA, Montserrat E, Gonzalez-Diaz M, Puente XS, Jares P, Valencia A, Himmelbauer H, Quesada V, Bea S, Stratton MR, Futreal PA, Campbell PJ, Vincent-Salomon A, Richardson AL, Reis-Filho JS, van de Vijver M, Thomas G, Masson-Jacquemier JD, Aparicio S, Borg A, Borresen-Dale AL, Caldas C, Foekens JA, Stunnenberg HG, van't Veer L, Easton DF, Spellman PT, Martin S, Barker AD, Chin L, Collins FS, Compton CC, Ferguson ML, Gerhard DS, Getz G, Gunter C, Guttmacher A, Guyer M, Hayes DN, Lander ES, Ozenberger B, Penny R, Peterson J, Sander C, Shaw KM, Speed TP, Spellman PT, Vockley JG, Wheeler DA, Wilson RK, Hudson TJ, Chin L, Knoppers BM, Lander ES, Lichter P, Stein LD, Stratton MR, Anderson W, Barker AD, Bell C, Bobrow M, Burke W, Collins FS, Compton CC, DePinho RA, Easton DF, Futreal PA, Gerhard DS, Green AR, Guyer M, Hamilton SR, Hubbard TJ, Kallioniemi OP, Kennedy KL, Ley TJ, Liu ET, Lu Y, Majumder P, Marra M, Ozenberger B, Peterson J, Schafer AJ, Spellman PT, Stunnenberg HG, Wainwright BJ, Wilson RK, Yang H (2010) International network of cancer genome projects. Nature 464(7291):993–998. Scholar
  47. 47.
    Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42(Database issue):D68–D73. Scholar
  48. 48.
    Quek XC, Thomson DW, Maag JL, Bartonicek N, Signal B, Clark MB, Gloss BS, Dinger ME (2015) lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs. Nucleic Acids Res 43(Database issue):D168–D173. Scholar
  49. 49.
    Lebron R, Gomez-Martin C, Carpena P, Bernaola-Galvan P, Barturen G, Hackenberg M, Oliver JL (2017) NGSmethDB 2017: enhanced methylomes and differential methylation. Nucleic Acids Res 45(D1):D97–D103. Scholar
  50. 50.
    Xin Y, Chanrion B, O'Donnell AH, Milekic M, Costa R, Ge Y, Haghighi FG (2012) MethylomeDB: a database of DNA methylation profiles of the brain. Nucleic Acids Res 40(Database issue):D1245–D1249. Scholar
  51. 51.
    Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807. Scholar
  52. 52.
    Mitchell A, Bucchini F, Cochrane G, Denise H, ten Hoopen P, Fraser M, Pesseat S, Potter S, Scheremetjew M, Sterk P, Finn RD (2016) EBI metagenomics in 2016—an expanding and evolving resource for the analysis and archiving of metagenomic data. Nucleic Acids Res 44(D1):D595–D603. Scholar
  53. 53.
    Friedman A, Perrimon N (2007) Genetic screening for signal transduction in the era of network biology. Cell 128(2):225–231. Scholar
  54. 54.
    Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101–113. Scholar
  55. 55.
    Goymer P (2008) Network biology: why do we need hubs? Nat Rev Genet 9(9):650CrossRefGoogle Scholar
  56. 56.
    Hu JX, Thomas CE, Brunak S (2016) Network biology concepts in complex disease comorbidities. Nat Rev Genet 17(10):615–629. Scholar
  57. 57.
    New AM, Lehner B (2015) Systems biology: network evolution hinges on history. Nature 523(7560):297–298. Scholar
  58. 58.
    Chatr-Aryamontri A, Oughtred R, Boucher L, Rust J, Chang C, Kolas NK, O'Donnell L, Oster S, Theesfeld C, Sellam A, Stark C, Breitkreutz BJ, Dolinski K, Tyers M (2017) The BioGRID interaction database: 2017 update. Nucleic Acids Res 45(D1):D369–D379. Scholar
  59. 59.
    Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue):D447–D452. Scholar
  60. 60.
    Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):D353–D361. Scholar
  61. 61.
    Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R, Jassal B, Jupe S, Korninger F, McKay S, Matthews L, May B, Milacic M, Rothfels K, Shamovsky V, Webber M, Weiser J, Williams M, Wu G, Stein L, Hermjakob H, D'Eustachio P (2016) The reactome pathway knowledgebase. Nucleic Acids Res 44(D1):D481–D487. Scholar
  62. 62.
    Bohler A, Wu G, Kutmon M, Pradhana LA, Coort SL, Hanspers K, Haw R, Pico AR, Evelo CT (2016) Reactome from a WikiPathways perspective. PLoS Comput Biol 12(5):e1004941. Scholar
  63. 63.
    Tyner C, Barber GP, Casper J, Clawson H, Diekhans M, Eisenhart C, Fischer CM, Gibson D, Gonzalez JN, Guruvadoo L, Haeussler M, Heitner S, Hinrichs AS, Karolchik D, Lee BT, Lee CM, Nejad P, Raney BJ, Rosenbloom KR, Speir ML, Villarreal C, Vivian J, Zweig AS, Haussler D, Kuhn RM, Kent WJ (2017) The UCSC Genome Browser database: 2017 update. Nucleic Acids Res 45(D1):D626–D634. Scholar
  64. 64.
    Koch A, De Meyer T, Jeschke J, Van Criekinge W (2015) MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data. BMC Genomics 16:636. Scholar
  65. 65.
    van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536. Scholar
  66. 66.
    Zeng T, Li J (2010) Maximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathways. Nucleic Acids Res 38(1):e1. Scholar
  67. 67.
    Zeng T, Guo X, Liu J (2014) Negative correlation based gene markers identification in integrative gene expression data. Int J Data Min Bioinform 10(1):1–17CrossRefGoogle Scholar
  68. 68.
    Deng M, Bragelmann J, Schultze JL, Perner S (2016) Web-TCGA: an online platform for integrated analysis of molecular cancer data sets. BMC Bioinformatics 17:72. Scholar
  69. 69.
    Huang Y, Zaas AK, Rao A, Dobigeon N, Woolf PJ, Veldman T, Oien NC, McClain MT, Varkey JB, Nicholson B, Carin L, Kingsmore S, Woods CW, Ginsburg GS, Hero AO III (2011) Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection. PLoS Genet 7(8):e1002234. Scholar
  70. 70.
    Brawand D, Soumillon M, Necsulea A, Julien P, Csardi G, Harrigan P, Weier M, Liechti A, Aximu-Petri A, Kircher M, Albert FW, Zeller U, Khaitovich P, Grutzner F, Bergmann S, Nielsen R, Paabo S, Kaessmann H (2011) The evolution of gene expression levels in mammalian organs. Nature 478(7369):343–348. Scholar
  71. 71.
    Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3(9):1724–1735. Scholar
  72. 72.
    Manimaran S, Selby HM, Okrah K, Ruberman C, Leek JT, Quackenbush J, Haibe-Kains B, Bravo HC, Johnson WE (2016) BatchQC: interactive software for evaluating sample and batch effects in genomic data. Bioinformatics. Scholar
  73. 73.
    Vandenbon A, Dinh VH, Mikami N, Kitagawa Y, Teraguchi S, Ohkura N, Sakaguchi S (2016) Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system. Proc Natl Acad Sci U S A 113(17):E2393–E2402. Scholar
  74. 74.
    Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118–127. Scholar
  75. 75.
    Stein CK, Qu P, Epstein J, Buros A, Rosenthal A, Crowley J, Morgan G, Barlogie B (2015) Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat. BMC Bioinformatics 16:63. Scholar
  76. 76.
    Reese SE, Archer KJ, Therneau TM, Atkinson EJ, Vachon CM, de Andrade M, Kocher JP, Eckel-Passow JE (2013) A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics 29(22):2877–2883. Scholar
  77. 77.
    Song R, Huang J, Ma S (2012) Integrative prescreening in analysis of multiple cancer genomic studies. BMC Bioinformatics 13:168. Scholar
  78. 78.
    Huang X, Stern DF, Zhao H (2016) Transcriptional profiles from paired normal samples offer complementary information on cancer patient survival—evidence from TCGA pan-cancer data. Sci Rep 6:20567. Scholar
  79. 79.
    Hwang TH, Atluri G, Kuang R, Kumar V, Starr T, Silverstein KA, Haverty PM, Zhang Z, Liu J (2013) Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers. BMC Genomics 14:440. Scholar
  80. 80.
    Li Q, Seo JH, Stranger B, McKenna A, Pe'er I, Laframboise T, Brown M, Tyekucheva S, Freedman ML (2013) Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152(3):633–641. Scholar
  81. 81.
    Peifer M, Fernandez-Cuesta L, Sos ML, George J, Seidel D, Kasper LH, Plenker D, Leenders F, Sun R, Zander T, Menon R, Koker M, Dahmen I, Muller C, Di Cerbo V, Schildhaus HU, Altmuller J, Baessmann I, Becker C, de Wilde B, Vandesompele J, Bohm D, Ansen S, Gabler F, Wilkening I, Heynck S, Heuckmann JM, Lu X, Carter SL, Cibulskis K, Banerji S, Getz G, Park KS, Rauh D, Grutter C, Fischer M, Pasqualucci L, Wright G, Wainer Z, Russell P, Petersen I, Chen Y, Stoelben E, Ludwig C, Schnabel P, Hoffmann H, Muley T, Brockmann M, Engel-Riedel W, Muscarella LA, Fazio VM, Groen H, Timens W, Sietsma H, Thunnissen E, Smit E, Heideman DA, Snijders PJ, Cappuzzo F, Ligorio C, Damiani S, Field J, Solberg S, Brustugun OT, Lund-Iversen M, Sanger J, Clement JH, Soltermann A, Moch H, Weder W, Solomon B, Soria JC, Validire P, Besse B, Brambilla E, Brambilla C, Lantuejoul S, Lorimier P, Schneider PM, Hallek M, Pao W, Meyerson M, Sage J, Shendure J, Schneider R, Buttner R, Wolf J, Nurnberg P, Perner S, Heukamp LC, Brindle PK, Haas S, Thomas RK (2012) Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet 44(10):1104–1110. Scholar
  82. 82.
    Cancer Genome Atlas N (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407):330–337. Scholar
  83. 83.
    Cancer Genome Atlas Research N (2013) Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499(7456):43–49. Scholar
  84. 84.
    Cancer Genome Atlas Research N (2014) Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513(7517):202–209. Scholar
  85. 85.
    Cancer Genome Atlas Research N (2014) Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507(7492):315–322. Scholar
  86. 86.
    Cancer Genome Atlas N (2015) Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517(7536):576–582. Scholar
  87. 87.
    Cancer Genome Atlas Research N (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216):1061–1068. Scholar
  88. 88.
    Cancer Genome Atlas Research N (2012) Comprehensive genomic characterization of squamous cell lung cancers. Nature 489(7417):519–525. Scholar
  89. 89.
    Akbani R, Ng PK, Werner HM, Shahmoradgoli M, Zhang F, Ju Z, Liu W, Yang JY, Yoshihara K, Li J, Ling S, Seviour EG, Ram PT, Minna JD, Diao L, Tong P, Heymach JV, Hill SM, Dondelinger F, Stadler N, Byers LA, Meric-Bernstam F, Weinstein JN, Broom BM, Verhaak RG, Liang H, Mukherjee S, Lu Y, Mills GB (2014) A pan-cancer proteomic perspective on The Cancer Genome Atlas. Nat Commun 5:3887. Scholar
  90. 90.
    Ciriello G, Gatza ML, Beck AH, Wilkerson MD, Rhie SK, Pastore A, Zhang H, McLellan M, Yau C, Kandoth C, Bowlby R, Shen H, Hayat S, Fieldhouse R, Lester SC, Tse GM, Factor RE, Collins LC, Allison KH, Chen YY, Jensen K, Johnson NB, Oesterreich S, Mills GB, Cherniack AD, Robertson G, Benz C, Sander C, Laird PW, Hoadley KA, King TA, Network TR, Perou CM (2015) Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163(2):506–519. Scholar
  91. 91.
    Cancer Genome Atlas N (2012) Comprehensive molecular portraits of human breast tumours. Nature 490(7418):61–70. Scholar
  92. 92.
    Drake JM, Paull EO, Graham NA, Lee JK, Smith BA, Titz B, Stoyanova T, Faltermeier CM, Uzunangelov V, Carlin DE, Fleming DT, Wong CK, Newton Y, Sudha S, Vashisht AA, Huang J, Wohlschlegel JA, Graeber TG, Witte ON, Stuart JM (2016) Phosphoproteome integration reveals patient-specific networks in prostate cancer. Cell 166(4):1041–1054. Scholar
  93. 93.
    Cancer Genome Atlas Research N, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45(10):1113–1120. Scholar
  94. 94.
    Neapolitan R, Horvath CM, Jiang X (2015) Pan-cancer analysis of TCGA data reveals notable signaling pathways. BMC Cancer 15:516. Scholar
  95. 95.
    Ruau D, Dudley JT, Chen R, Phillips NG, Swan GE, Lazzeroni LC, Clark JD, Butte AJ, Angst MS (2012) Integrative approach to pain genetics identifies pain sensitivity loci across diseases. PLoS Comput Biol 8(6):e1002538. Scholar
  96. 96.
    Liu P, Sanalkumar R, Bresnick EH, Keles S, Dewey CN (2016) Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq. Genome Res 26(8):1124–1133. Scholar
  97. 97.
    Knouf EC, Garg K, Arroyo JD, Correa Y, Sarkar D, Parkin RK, Wurz K, O'Briant KC, Godwin AK, Urban ND, Ruzzo WL, Gentleman R, Drescher CW, Swisher EM, Tewari M (2012) An integrative genomic approach identifies p73 and p63 as activators of miR-200 microRNA family transcription. Nucleic Acids Res 40(2):499–510. Scholar
  98. 98.
    Yan Z, Shah PK, Amin SB, Samur MK, Huang N, Wang X, Misra V, Ji H, Gabuzda D, Li C (2012) Integrative analysis of gene and miRNA expression profiles with transcription factor-miRNA feed-forward loops identifies regulators in human cancers. Nucleic Acids Res 40(17):e135. Scholar
  99. 99.
    Berghoff BA, Konzer A, Mank NN, Looso M, Rische T, Forstner KU, Kruger M, Klug G (2013) Integrative “omics”–approach discovers dynamic and regulatory features of bacterial stress responses. PLoS Genet 9(6):e1003576. Scholar
  100. 100.
    Kim M, Rai N, Zorraquino V, Tagkopoulos I (2016) Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat Commun 7:13090. Scholar
  101. 101.
    Meng C, Helm D, Frejno M, Kuster B (2016) moCluster: identifying joint patterns across multiple omics data sets. J Proteome Res 15(3):755–765. Scholar
  102. 102.
    Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, Haibe-Kains B, Goldenberg A (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11(3):333–337. Scholar
  103. 103.
    Shi Q, Zhang C, Peng M, Yu X, Zeng T, Liu J, Chen L (2017) Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data. Bioinformatics.
  104. 104.
    Lee CH, Alpert BO, Sankaranarayanan P, Alter O (2012) GSVD comparison of patient-matched normal and tumor aCGH profiles reveals global copy-number alterations predicting glioblastoma multiforme survival. PLoS One 7(1):e30098. Scholar
  105. 105.
    Xiao X, Moreno-Moral A, Rotival M, Bottolo L, Petretto E (2014) Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules. PLoS Genet 10(1):e1004006. Scholar
  106. 106.
    Kersey PJ, Staines DM, Lawson D, Kulesha E, Derwent P, Humphrey JC, Hughes DS, Keenan S, Kerhornou A, Koscielny G, Langridge N, McDowall MD, Megy K, Maheswari U, Nuhn M, Paulini M, Pedro H, Toneva I, Wilson D, Yates A, Birney E (2012) Ensembl genomes: an integrative resource for genome-scale data from non-vertebrate species. Nucleic Acids Res 40(Database issue):D91–D97. Scholar
  107. 107.
    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269):pl1. Scholar
  108. 108.
    He S, He H, Xu W, Huang X, Jiang S, Li F, He F, Bo X (2016) ICM: a web server for integrated clustering of multi-dimensional biomedical data. Nucleic Acids Res 44(W1):W154–W159. Scholar
  109. 109.
    Xia J, Fjell CD, Mayer ML, Pena OM, Wishart DS, Hancock RE (2013) INMEX—a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res 41(Web Server issue):W63–W70. Scholar
  110. 110.
    Tuncbag N, McCallum S, Huang SS, Fraenkel E (2012) SteinerNet: a web server for integrating ‘omic’ data to discover hidden components of response pathways. Nucleic Acids Res 40(Web Server issue):W505–W509. Scholar
  111. 111.
    Ovaska K, Laakso M, Haapa-Paananen S, Louhimo R, Chen P, Aittomaki V, Valo E, Nunez-Fontarnau J, Rantanen V, Karinen S, Nousiainen K, Lahesmaa-Korpinen AM, Miettinen M, Saarinen L, Kohonen P, Wu J, Westermarck J, Hautaniemi S (2010) Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Med 2(9):65. Scholar
  112. 112.
    Krasnov GS, Dmitriev AA, Melnikova NV, Zaretsky AR, Nasedkina TV, Zasedatelev AS, Senchenko VN, Kudryavtseva AV (2016) CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms. Nucleic Acids Res 44(7):e62. Scholar
  113. 113.
    Yu X, Li G, Chen L (2014) Prediction and early diagnosis of complex diseases by edge-network. Bioinformatics 30(6):852–859. Scholar
  114. 114.
    Zhang Q, Burdette JE, Wang JP (2014) Integrative network analysis of TCGA data for ovarian cancer. BMC Syst Biol 8:1338. Scholar
  115. 115.
    Zhu R, Zhao Q, Zhao H, Ma S (2016) Integrating multidimensional omics data for cancer outcome. Biostatistics 17(4):605–618. Scholar
  116. 116.
    Wang XV, Verhaak RG, Purdom E, Spellman PT, Speed TP (2011) Unifying gene expression measures from multiple platforms using factor analysis. PLoS One 6(3):e17691. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Systems Biology, Institute of Biochemistry and Cell BiologyChinese Academy ScienceShanghaiChina

Personalised recommendations