Network-Based Methods for Computational Diagnostics by Means of R

  • Laurin A. J. Mueller
  • Matthias Dehmer
  • Frank Emmert-Streib
Chapter

Abstract

Networks representing biomedical data have become a powerful approach in different research disciplines dealing with complex diseases. Also, R and Bioconductor have emerged as a standard research environment to investigate and analyze high-throughput data. Therefore, we present and discuss existing packages, available in R or Bioconductor, that provide methods for computational diagnostics by means of networks. In particular, we summarize packages to reconstruct and analyze networks from high-throughput data. Moreover, we discuss packages that provide comprehensive methods to visualize large-scale gene networks in order to support the field of computational diagnostics of complex diseases. The aim of this chapter is to support an interdisciplinary research community dealing with computational diagnostics to investigate novel hypothesis in a medical and clinical context to gain a better understanding of complex diseases.

References

  1. Ahn YY, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466(7307):761–764PubMedCrossRefGoogle Scholar
  2. Altay G, Emmert-Streib F (2010) Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol 4(1):132PubMedCrossRefGoogle Scholar
  3. Altay G, Emmert-Streib F (2011) Structural influence of gene networks on their inference: analysis of c3net. Biol Direct 6:31PubMedCrossRefGoogle Scholar
  4. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 206:509–512Google Scholar
  5. Beisser D, Klau GW, Dandekar T, Müller T, Dittrich MT (2010) BioNet: an R-package for the functional analysis of biological networks. Bioinformatics 26(8):1129PubMedCrossRefGoogle Scholar
  6. Bender C, Heyde S, Henjes F, Wiemann S, Korf U, Beißbarth T (2011) Inferring signalling networks from longitudinal data using sampling-based approaches in the R-package ddepn. BMC Bioinformatics 12(1):291PubMedCrossRefGoogle Scholar
  7. Berg J, Lässig M (2006) Cross-species analysis of biological networks by Bayesian alignment. Proc Natl Acad Sci 103(29):10967PubMedCrossRefGoogle Scholar
  8. Birmelé, E. (2012) Detecting local network motifs, Electronic Journal of Statistics 6:908–933Google Scholar
  9. Bonchev D (1983) Information theoretic indices for characterization of chemical structures. Research Studies Press, ChichesterGoogle Scholar
  10. Bonchev D, Mekenyan O, Trinajsiteć N (1981) Isomer discrimination by topological information approach. J Comput Chem 2(2):127–148CrossRefGoogle Scholar
  11. Bornholdt S, Schuster HG (eds) (2003) Handbook of graphs and networks. From the genome to the internet. Wiley-VCH, WeinheimGoogle Scholar
  12. Carey V, Long L, Gentleman R (2011) RBGL: an interface to the BOOST graph library. http://CRAN.R-project.org/package=RBGL, R package version 1.28.0
  13. Chiquet J, Smith A, Grasseau G, Matias C, Ambroise C (2009) Simone: statistical inference for modular networks. Bioinformatics 25(3):417PubMedCrossRefGoogle Scholar
  14. Costa LF, Rodrigues FA, Travieso G, Boas PRV (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56:167–242CrossRefGoogle Scholar
  15. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Syst 1695–1704Google Scholar
  16. Dehmer M, Mowshowitz A (2011) A history of graph entropy measures. Inform Sci 181(1):57–78CrossRefGoogle Scholar
  17. Dehmer M, Borgert S, Emmert-Streib F (2008) Entropy bounds for hierarchical molecular networks. PLoS One 3(8):e3079PubMedCrossRefGoogle Scholar
  18. Dehmer M, Barbarini N, Varmuza K, Graber A (2009) A large scale analysis of information-theoretic network complexity measures using chemical structures. PLoS One 4(12):e8057Google Scholar
  19. Dehmer M, Varmuza K, Borgert S, Emmert-Streib F (2009b) On entropy-based molecular descriptors: statistical analysis of real and synthetic chemical structures. J Chem Inf Model 49:1655–1663PubMedCrossRefGoogle Scholar
  20. Dehmer M, Sivakumar L, Varmuza K (2012) Uniquely discriminating molecular structures using novel eigenvalue based descriptors. MATCH Commun Math 67(1):147–172Google Scholar
  21. Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I, Wu G, D’Eustachio P, Schaefer C, Luciano J et al (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28(9):935–942PubMedCrossRefGoogle Scholar
  22. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN: 3-900051-07-0Google Scholar
  23. Ellson J, Gansner E, Koutsofios L, North S, Woodhull G (2002) Graphviz—open source graph drawing tools. In: Graph drawing. Springer, Heidelberg, pp 594–597Google Scholar
  24. Emmert-Streib F, Dehmer M (2011) Networks for systems biology: conceptual connection of data and function. IET Syst Biol 5(3):185–207PubMedCrossRefGoogle Scholar
  25. Fang Z, Tian W, Ji H (2012) A network-based gene-weighting approach for pathway analysis. Cell Res 22(3):565–580PubMedCrossRefGoogle Scholar
  26. Gallo G, Longo G, Pallottino S (1993) Directed hypergraphs and applications. Discr Appl Math 42(2):177–201CrossRefGoogle Scholar
  27. Garcia O, Saveanu C, Cline M, Fromont-Racine M, Jacquier A, Schwikowski B, Aittokallio T (2007) GOlorize: a cytoscape plug-in for network visualization with gene ontology-based layout and coloring. Bioinformatics 23(3):394PubMedCrossRefGoogle Scholar
  28. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Horthon T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini G, Sawitzki AJ, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10):R80PubMedCrossRefGoogle Scholar
  29. Gentleman R, Whalen E, Huber W, Falcon S (2009) Graph: a package to handle graph data structures. R package version 1.26.0Google Scholar
  30. Gentry J, Long L, Gentleman R, Falcon S (2007) Rgraphviz: plotting capabilities for R graph objects. http://www.bioconductor.org/, R package version 1.34.1
  31. Harary F (1994) Graph theory. Addison-Wesley series in mathematics. Perseus Books, Boulder, Colorado, USAGoogle Scholar
  32. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA, Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M et al (2004) The gene ontology (GO) database and informatics resource. Nucleic Acids Res 32(Database issue):D258PubMedGoogle Scholar
  33. Henegar C, Tordjman J, Achard V, Lacasa D, Cremer I, Guerre-Millo M, Poitou C, Basdevant A, Stich V, Viguerie N et al (2008) Adipose tissue transcriptomic signature highlights the pathological relevance of extracellular matrix in human obesity. Genome Biol 9(1):R14PubMedCrossRefGoogle Scholar
  34. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524PubMedCrossRefGoogle Scholar
  35. Jacob L (2011) NCIgraph: networks from the NCI pathway integrated database as graphNEL objects.http://www.bioconductor.org/, R package version 1.0.0
  36. Jonker R, Volgenant A (1987) A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 38(4):325–340CrossRefGoogle Scholar
  37. Kalinka AT, Tomancak P (2011) Linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics 27(14):2011–2012Google Scholar
  38. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27PubMedCrossRefGoogle Scholar
  39. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A et al (2009) Human protein reference database—2009 update. Nucleic Acids Res 37(suppl 1):D767PubMedCrossRefGoogle Scholar
  40. Kim J, Wilhelm T (2008) What is a complex graph? Phys A Stats Mech Appl 387(11):2637–2652CrossRefGoogle Scholar
  41. Kones JK, Soetaert K, van Oevelen D, Owino JO (2009) Are network indices robust indicators of food Web functioning? a Monte Carlo approach. Ecol Model 220(3):370–382CrossRefGoogle Scholar
  42. Kraskov A, Stögbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E 69(6):066138CrossRefGoogle Scholar
  43. Lahti L, Knuuttila JEA, Kaski S (2010) Global modeling of transcriptional responses in interaction networks. Bioinformatics 26(21):2713PubMedCrossRefGoogle Scholar
  44. Lai Y, Wu B, Chen L, Zhao H (2004) A statistical method for identifying differential gene–gene Co-expression patterns. Bioinformatics 20(17):3146PubMedCrossRefGoogle Scholar
  45. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559PubMedCrossRefGoogle Scholar
  46. Loots G, Ovcharenko I (2006) ECRbase: database of evolutionary conserved regions, promoters, and transcription factor binding sites in bertebrate genomes. Bioinformatics 23(1):122PubMedCrossRefGoogle Scholar
  47. Ma H, Schadt EE, Kaplan LM, Zhao H (2011) COSINE: condition-specific Sub-network identification using a global optimization method. Bioinformatics 27(9):1290PubMedCrossRefGoogle Scholar
  48. Mangan S, Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100(21):11980PubMedCrossRefGoogle Scholar
  49. Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera R, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7:S7PubMedCrossRefGoogle Scholar
  50. Mazurie A, Bonchev D, Schwikowski B, Buck GA (2008) Phylogenetic distances are encoded in networks of interacting pathways. Bioinformatics 24(22):2579PubMedCrossRefGoogle Scholar
  51. Meyer PE, Kontos K, Lafitte F, Bontempi G (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinformatics Syst Biol 2007:8–8Google Scholar
  52. Meyer P, Lafitte F, Bontempi G (2008) Minet: a R/bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics 9:461PubMedCrossRefGoogle Scholar
  53. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824PubMedCrossRefGoogle Scholar
  54. Motulsky H (1995) Intuitive biostatistics, vol 173. Oxford University Press, New YorkGoogle Scholar
  55. Mowshowitz A (1968) Entropy and the complexity of the graphs I: an index of the relative complexity of a graph. Bull Math Biophys 30:175204Google Scholar
  56. Mueller LAJ, Kugler KG, Dander A, Graber A, Dehmer M (2011a) QuACN: an R package for analyzing complex biological networks quantitatively. Bioinformatics 27(1):140PubMedCrossRefGoogle Scholar
  57. Mueller LAJ, Kugler KG, Netzer M, Graber A, Dehmer M (2011b) A network-based approach to classify the three domains of life. Biol Direct 6(1):53PubMedCrossRefGoogle Scholar
  58. Opgen-Rhein R, Strimmer K (2006) Inferring gene dependency networks from genomic longitudinal data: a functional data approach. REVSTAT Stat J 4(1):53–65Google Scholar
  59. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(9):1226–1238Google Scholar
  60. Perroud B, Lee J, Valkova N, Dhirapong A, Lin PY, Fiehn O, Kültz D, Weiss R (2006) Pathway analysis of kidney cancer using proteomics and metabolic profiling. Mol Cancer 5(1):64PubMedCrossRefGoogle Scholar
  61. Picard F, Daudin JJ, Koskas M, Schbath S, Robin S (2008) Assessing the exceptionality of network motifs. J Comput Biol 15(1):1–20PubMedCrossRefGoogle Scholar
  62. Prifti E, Zucker JD, Clement K, Henegar C (2008) FunNet: an integrative tool for exploring transcriptional interactions. Bioinformatics 24(22):2636PubMedCrossRefGoogle Scholar
  63. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci USA 101(9):2658PubMedCrossRefGoogle Scholar
  64. Rashevsky N (1955) Life, information theory, and topology. Bull Math Biophys 17:229–235CrossRefGoogle Scholar
  65. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551PubMedCrossRefGoogle Scholar
  66. Sales G, Romualdi C (2011) Parmigene - a parallel R package for mutual information estimation and gene network reconstruction. Bioinformatics 27(13):1876PubMedCrossRefGoogle Scholar
  67. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH (2009) PID: the pathway interaction database. Nucleic Acids Res 37:D674PubMedCrossRefGoogle Scholar
  68. Schäfer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754PubMedCrossRefGoogle Scholar
  69. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498PubMedCrossRefGoogle Scholar
  70. Siek J, Lee LQ, Lumsdaine A (2002) The boost graph library. Addison-Wesley, BostonGoogle Scholar
  71. Skorobogatov VA, Dobrynin AA (1988) Metrical analysis of graphs. MATCH 23:105–155Google Scholar
  72. Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci 100(21):12123PubMedCrossRefGoogle Scholar
  73. Tao H, Lei L, Ziliang Q, Kang T, Yixue L, Lu X (2010) Using GeneReg to construct time delay gene regulatory networks. BMC Res Notes 3:142Google Scholar
  74. Todeschini R, Consonni V, Mannhold R (2002) Handbook of molecular descriptors. Wiley-VCH, WeinheimGoogle Scholar
  75. Tourassi GD, Frederick ED, Markey MK, Floyd CE Jr (2001) Application of the mutual information criterion for feature selection in computer-aided diagnosis. Med Phys 28:2394PubMedCrossRefGoogle Scholar
  76. Vidal M (2009) A unifying view of 21st century systems biology. FEBS Lett 583(24):3891–3894PubMedCrossRefGoogle Scholar
  77. von Bertalanffy L (1950) The theory of open systems in physics and biology. Science 11:23–29CrossRefGoogle Scholar
  78. Waddington CH (1957) The strategy of the genes. Geo, Allen & Unwin LondonGoogle Scholar
  79. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘Small-world’ networks. Nature 393:440–442PubMedCrossRefGoogle Scholar
  80. Wu MC, Lin X (2009) Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways. Stat Methods Med Res 18(6):577PubMedCrossRefGoogle Scholar
  81. Xia K, Fu Z, Hou L, Han JDJ (2008) Impacts of protein-protein interaction domains on organism and network complexity. Genome Res 18(9):1500PubMedCrossRefGoogle Scholar
  82. Zinovyev A, Viara E, Calzone L, Barillot E (2008) BiNoM: a cytoscape plugin for manipulating and analyzing biological networks. Bioinformatics 24(6):876PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2012

Authors and Affiliations

  • Laurin A. J. Mueller
    • 1
  • Matthias Dehmer
    • 2
  • Frank Emmert-Streib
    • 3
  1. 1.UMIT, Institute for Bioinformatics and Translational ResearchHall in TirolAustria
  2. 2.UMIT, Institute for Bioinformatics and Translational ResearchHall in TyrolAustria
  3. 3.Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical SciencesQueen’s University BelfastBelfastUK

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