Systems Analysis of High-Throughput Data

Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 844)

Abstract

Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g.,  sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.

Keywords

Statistical analysis High-throughput data Microarrays Sequencing NGS Genomics Machine learning Network models 

References

  1. 1.
    van den Akker-van Marle ME, Gurwitz D, Detmar SB, Enzing CM, Hopkins MM, de Mesa EG, Ibarreta D. Cost-effectiveness of pharmacogenomics in clinical practice: a case study of thiopurine methyltransferase genotyping in acute lymphoblastic leukemia in Europe. Pharmacogenomics. 2006;7(5):783–92.Google Scholar
  2. 2.
    Karajannis M, Vincent L, Direnzo R, Shmelkov S, Zhang F, Feldman E, Bohlen P, Zhu Z, Sun H, Kussie P, Rafii S. Activation of fgfr1beta signaling pathway promotes survival, migration and resistance to chemotherapy in acute myeloid leukemia cells. Leukemia. 2006.Google Scholar
  3. 3.
    Savageau MA, Rosen R. Biochemical systems analysis: a study of function and design in molecular biology, vol. 725. Reading: Addison-Wesley; 1976.Google Scholar
  4. 4.
    Von Bertalanffy L: Modern theories of development: an introduction to theoretical biology. In: Woodger JH, transl. Oxford University Press; 1933 (originally published 1928).Google Scholar
  5. 5.
    Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249–64.Google Scholar
  6. 6.
    Bolstad BM, Irizarry RA, Åstrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19(2):185–93.Google Scholar
  7. 7.
    Parmigiani G. The analysis of gene expression data: methods and software. Springer; 2003.Google Scholar
  8. 8.
    Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and snp calling from next-generation sequencing data. Nat Rev Genet. 2011;12(6):443–51.Google Scholar
  9. 9.
    Metzker ML. Sequencing technologies\mdashthe next generation. Nat Rev Genet. 200911(1):31–46.Google Scholar
  10. 10.
    Vazquez M, de la Torre V, Valencia A. Cancer genome analysis. PLoS Comput Biol. 20128(12):e1002824.Google Scholar
  11. 11.
    Smyth GK. Limma: linear models for microarray data. In: Bioinformatics and computational biology solutions using R and Bioconductor. Springer; 2005. pp. 397–420Google Scholar
  12. 12.
    R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. 2012. http://www.R-project.org/. ISBN 3-900051-07-0.
  13. 13.
    Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5(10):R80.Google Scholar
  14. 14.
    Gentleman, R., Carey, V., Huber, W., Irizarry, R., Dudoit, S.: Bioinformatics and computational biology solutions using R and Bioconductor, vol. 746718470. Springer; 2005.Google Scholar
  15. 15.
    Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst. 2007;99(2):147–57.Google Scholar
  16. 16.
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995; pp. 289–300.Google Scholar
  17. 17.
    Benjamini Y, Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001; pp. 1165–88.Google Scholar
  18. 18.
    Han, B., Kang, H.M., Eskin, E.: Rapid and accurate multiple testing correction and power estimation for millions of correlated markers. PLoS Genet. 2009;5(4):e1000,456.Google Scholar
  19. 19.
    Csete ME, Doyle JC. Reverse engineering of biological complexity. Science 2002;295(5560), 1664–9.Google Scholar
  20. 20.
    Edelman GM, Gally JA. Degeneracy and complexity in biological systems. Proc Natl Acad Sci. 2001;98(24):13763–8.Google Scholar
  21. 21.
    D’haeseleer P. How does gene expression clustering work? Nat Biotechnol. 2005;23(12):1499–501.Google Scholar
  22. 22.
    Datta S, Datta, S. Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics. 2003;19(4):459–66.Google Scholar
  23. 23.
    Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci. 1998;95(25):14863–8.Google Scholar
  24. 24.
    Hartigan, J, Wong M. Algorithm AS 136: A k-means clustering algorithm. J R Stat Soc C Appl Stat. 1979;28:100–8.Google Scholar
  25. 25.
    Ng A, Jordan M, Weiss Y. On spectral clustering: analysis and an algorithm. Adv Neur Inf Process Syst. 2002;2, 849–56.Google Scholar
  26. 26.
    Leibon G, Pauls S, Rockmore D, Savell R. Topological structures in the equities market network. Proc Natl Acad Sci. 2008;105(52):20589–594.Google Scholar
  27. 27.
    Chung F. Spectral graph theory. American Mathematical Society; 1997.Google Scholar
  28. 28.
    von Luxburg U. A tutorial on spectral clustering. Stat Comput. 2007;17(4):395–416.Google Scholar
  29. 29.
    Qiu P, Plevritis SK. Simultaneous class discovery and classification of microarray data using spectral analysis. J Comput Biol. 2009;16:935–44.Google Scholar
  30. 30.
    Braun R, Leibon G, Pauls S, Rockmore D. Partition decoupling for multi-gene analysis of gene expression profiling data. BMC Bioinformatics. 2011;12(497).Google Scholar
  31. 31.
    Kim D, Lee K, Lee D. Detecting clusters of different geometrical shapes in microarray gene expression data. Bioinformatics 2005;21(9):1927–34.Google Scholar
  32. 32.
    Baker S. Simple and flexible classification of gene expression microarrays via swirls and ripples. BMC Bioinformat. 2010;11(1):452Google Scholar
  33. 33.
    Fraley C, Raftery A. MCLUST: Software for model-based cluster analysis. J. Classification 1999;16(2):297–306.Google Scholar
  34. 34.
    Still S, Bialek W. How many clusters? An information-theoretic perspective. Neural Comput. 2004;16(12):2483–506.Google Scholar
  35. 35.
    Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc B. 2002;63(2):411–23.Google Scholar
  36. 36.
    Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52(1–2):91–118.Google Scholar
  37. 37.
    Monti S, Savage KJ, Kutok JL, Feuerhake F, Kurtin P, Mihm, M, Wu B, Pasqualucci L, Neuberg D, Aguiar RC, et al. Molecular profiling of diffuse large b-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood. 2005;105(5):1851–61.Google Scholar
  38. 38.
    Jolliffe I. Principal component analysis. Wiley Online Library; 2005.Google Scholar
  39. 39.
    Wilson NK, Foster SD, Wang X, Knezevic K, Schütte J, Kaimakis P, Chilarska PM, Kinston S, Ouwehand WH, Dzierzak E, et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell. 2010;7(4):532–44.Google Scholar
  40. 40.
    Chambers SM, Boles NC, Lin KYK, Tierney MP, Bowman TV, Bradfute SB, Chen AJ, Merchant AA, Sirin O, Weksberg DC, et al. Hematopoietic fingerprints: an expression database of stem cells and their progeny. Cell Stem Cell. 2007;1(5):578–91.Google Scholar
  41. 41.
    Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci. 2000;97(18):10101–6.Google Scholar
  42. 42.
    McIsaac RS, Petti AA, Bussemaker HJ, Botstein D. Perturbation-based analysis and modeling of combinatorial regulation in the yeast sulfur assimilation pathway. Mol Biol Cell 2012;23(15):2993–3007.Google Scholar
  43. 43.
    Narula J, Smith AM, Gottgens B, Igoshin OA. Modeling reveals bistability and low-pass filtering in the network module determining blood stem cell fate. PLoS Comput Biol. 2010;6(5):e1000771.Google Scholar
  44. 44.
    Bengio Y, Paiement J, Vincent P, Delalleau O, Le Roux N, Ouimet M. Out-of-sample extensions for LLE, IsoMap, MDS, eigenmaps, and spect ral clustering. Adv Neural Inf Process Syst. 2004;16:177–84.Google Scholar
  45. 45.
    Bengio Y, Delalleau O, Roux N, Paiement J, Vincent P, Ouimet M. Learning eigenfunctions links spectral embedding and kernel PCA. Neural Comput. 2004;16(10):2197–219.Google Scholar
  46. 46.
    Törönen P, Kolehmainen M, Wong G, Castrén E. Analysis of gene expression data using self-organizing maps. FEBS Lett. 1999;451(2):142–6.Google Scholar
  47. 47.
    Tamayo P, Slonim D, Mesirov J, Zhu Q, E Dmitrovsky SK, Lander ES, Golub TR. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci. 1999;96(6):2907–12.Google Scholar
  48. 48.
    Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: data mining, inference and prediction. Springer; 2009.Google Scholar
  49. 49.
    Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. {P}{I}{D}: the Pathway Interaction Database. Nucleic Acids Res. 2009;37:D674–9.Google Scholar
  50. 50.
    Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36(Database issue):D480–4.Google Scholar
  51. 51.
    Vastrik I, D’Eustachio P, Schmidt E, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, Wu G, Birney E, Stein L. Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 2007;8(3):R39.Google Scholar
  52. 52.
    Lynn DJ, Winsor GL, Chan C, Richard N, Laird MR, Barsky A, Gardy JL, Roche FM, Chan TH, Shah N, et al. Innatedb: facilitating systems-level analyses of the mammalian innate immune response. Mol Syst Biol. 2008;4(1).Google Scholar
  53. 53.
    Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, Kasprzyk A. BioMart–biological queries made easy. BMC Genomics. 2009;10:22.Google Scholar
  54. 54.
    Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):e1002375.Google Scholar
  55. 55.
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545–50.Google Scholar
  56. 56.
    Jiang Z, Gentleman R. Extensions to gene set enrichment. Bioinformatics. 2007;23(3):306–13.Google Scholar
  57. 57.
    Goeman JJ, Bühlmann P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics. 2007;23(8):980–7.Google Scholar
  58. 58.
    Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ. Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci U S A. 2005;102(38):13544–9.Google Scholar
  59. 59.
    Chiaretti S, Li X, Gentleman R, Vitale A, Vignetti M, Mandelli F, Ritz J, Foa R. Gene expression profile of adult t-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood. 2004;103(7):2771–8.Google Scholar
  60. 60.
    Grigoryev YA, Kurian SM, Avnur Z, Borie D, Deng J, Campbell D, Sung J, Nikolcheva T, Quinn A, Schulman H, et al. Deconvoluting post-transplant immunity: cell subset-specific mapping reveals pathways for activation and expansion of memory t, monocytes and b cells. PloS One. 2010;5(10):e13,358.Google Scholar
  61. 61.
    Ma S, Kosorok MR. Identification of differential gene pathways with principal component analysis. Bioinformatics. 2009;25(7):882–9.Google Scholar
  62. 62.
    Braun R, Cope L, Parmigiani G. Identifying differential correlation in gene/pathway combinations. BMC Bioinformatics. 2008;9:488.Google Scholar
  63. 63.
    Tibshirani R, Hastie T, Narasimhan B, Chu G. Class prediction by nearest shrunken centroids, with applications to dna microarrays. Stat Sci. 2003;104–17.Google Scholar
  64. 64.
    Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999; 286(5439):531–7.Google Scholar
  65. 65.
    Hastie T, Tibshirani R, Narasimhan B, Chu G. pamr: Pam: prediction analysis for microarrays. 2011. http://CRAN.R-project.org/package=pamr. R package version 1.54.
  66. 66.
    Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005;6(2):95–108.Google Scholar
  67. 67.
    McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, Hirschhorn JN. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9(5):356–69.Google Scholar
  68. 68.
    Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci. 2009;106(23):9362–7.Google Scholar
  69. 69.
    Schork N, Murray S, Frazer K, Topol E. Common vs. rare allele hypotheses for complex diseases. Current Opin Genet Dev. 2009;19(3):212–9.Google Scholar
  70. 70.
    Moore J, Asselbergs F, Williams S. Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010;26(4):445.Google Scholar
  71. 71.
    Greene C, Penrod N, Williams S, Moore J. Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS One. 2009;4(6):e5639.Google Scholar
  72. 72.
    Moore J. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered. 2003;56(1–3):73–82.Google Scholar
  73. 73.
    Tyler A, Asselbergs F, Williams S, Moore J. Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. BioEssays. 2009;31(2):220–7.Google Scholar
  74. 74.
    Holmans P. Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits. Adv Genet. 2010;72:141.Google Scholar
  75. 75.
    Wang K, Li M, Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet. 2010;11(12):843–54.Google Scholar
  76. 76.
    Wang K, Li M, Bucan M. Pathway-based approaches for analysis of genomewide association studies. Am J Hum Genet. 2007;81(6):1278.Google Scholar
  77. 77.
    Holden M, Deng S, Wojnowski L, Kulle B. GS{E}{A}-S{N}{P}: applying gene set enrichment analysis to SNP data from genome-wide association studies. Bioinformatics. 2008;24(23):2784–5.Google Scholar
  78. 78.
    Motsinger A, Ritchie M. Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene–gene interactions in human genetics and pharmacogenomics studies. Hum Genomics. 2006;2(5):318–28.Google Scholar
  79. 79.
    Moore J, Gilbert J, Tsai C, Chiang F, Holden T, Barney N, White B. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol. 2006;241(2):252–61.Google Scholar
  80. 80.
    Cordell H. Detecting gene–gene interactions that underlie human diseases. Nat Rev Genet. 2009;10(6):392–404.Google Scholar
  81. 81.
    Greene C, Sinnott-Armstrong N, Himmelstein D, Park P, Moore J, Harris B. Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic als. Bioinformatics. 2010;26(5):694.Google Scholar
  82. 82.
    Kira K, Rendell L. A practical approach to feature selection. Proceedings of the Ninth International Workshop on Machine learning; 1992. pp. 249–56.Google Scholar
  83. 83.
    Robnik-Šikonja M, Kononenko I. An adaptation of relief for attribute estimation in regression. Proceedings of the International Conference on Machine Learning ICML-97; 1997. pp. 296–304.Google Scholar
  84. 84.
    Moore J. Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data; 2007. pp. 17–30.Google Scholar
  85. 85.
    Greene C, Penrod N, Kiralis J, Moore J. Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions. BioData Mining. 2009;2:5.Google Scholar
  86. 86.
    Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, Pearson JV, Stephan DA, Nelson SF, Craig DW. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 2008;4(8):e1000167.Google Scholar
  87. 87.
    Braun R, Rowe W, Schaefer C, Zhang J, Buetow K. Needles in the haystack: Identifying individuals present in pooled genomic data. PLoS Genet. 2009;5(10):e1000668.Google Scholar
  88. 88.
    Visscher PM, Hill WG. The limits of individual identification from sample allele frequencies: theory and statistical analysis. PLoS Genet. 2009;5(10):e1000628.Google Scholar
  89. 89.
    Braun R, Buetow K. Pathways of Distinction Analysis: a new technique for multi-SNP ana lysis of GWAS data. PLoS Genet. 2011;7(6):e1002101.Google Scholar
  90. 90.
    Breiman L. Random forests. Machine Learn. 2001;45(1):5–32.Google Scholar
  91. 91.
    Pang H, Lin A, Holford M, Enerson BE, Lu B, Lawton MP, Floyd E, Zhao H. Pathway analysis using random forests classification and regression. Bioinformatics. 2006;22(16):2028–36.Google Scholar
  92. 92.
    D\'ıaz-Uriarte R, De Andres SA. Gene selection and classification of microarray data using random forest. BMC Bioinformatics. 2006;7(1):3.Google Scholar
  93. 93.
    Dettling M. Bagboosting for tumor classification with gene expression data. Bioinformatics. 2004;20(18):3583–93.Google Scholar
  94. 94.
    Lee JW, Lee JB, Park M, Song SH. An extensive comparison of recent classification tools applied to microarray data. Comput Stat Data Anal. 2005;48(4):869–85.Google Scholar
  95. 95.
    Hassane DC, Guzman ML, Corbett C, Li X, Abboud R, Young F, Liesveld JL, Carroll M, Jordan CT. Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data. Blood. 2008;111(12):5654–62.Google Scholar
  96. 96.
    Van Ness B, Ramos C, Haznadar M, Hoering A, Haessler J, Crowley J, Jacobus S, Oken M, Rajkumar V, Greipp P, et al. Genomic variation in myeloma: design, content, and initial application of the bank on a cure snp panel to detect associations with progression-free survival. BMC Med. 2008;6(1):26.Google Scholar
  97. 97.
    Ackermann M, Sikora-Wohlfeld W, Beyer A. Elucidating the regulatory mechanisms of transcription factor activity in hematopoietic stem cell differentiation. In: Saxon Biotechnology Symposium; 2011. p. 79.Google Scholar
  98. 98.
    De Souto M, Costa I, De Araujo D, Ludermir T, Schliep A. Clustering cancer gene expression data: a comparative study. BMC Bioinformatics. 2008;9(1):497.Google Scholar
  99. 99.
    Kolaczyk ED. Statistical analysis of network data. Springer; 2009.Google Scholar
  100. 100.
    Barabasi AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5(2):101–13.Google Scholar
  101. 101.
    Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL. The large-scale organization of metabolic networks. Nature. 2000;407(6804):651–4.Google Scholar
  102. 102.
    Jeong H, Mason SP, Barabasi AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411(6833):41–2.Google Scholar
  103. 103.
    Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 2001;292(5518):929–34.Google Scholar
  104. 104.
    Nacu S, Critchley-Thorne R, Lee P, Holmes S. Gene expression network analysis and applications to immunology. Bioinformatics. 2007;23(7):850–8.Google Scholar
  105. 105.
    Dittrich MT, Klau GW, Rosenwald A, Dandekar T, Müller T. Identifying functional modules in protein–protein interaction networks: an integrated exact approach. Bioinformatics. 2008;24(13):i223–31.Google Scholar
  106. 106.
    Beisser D, Klau GW, Dandekar T, Müller T, Dittrich MT. Bionet: an r-package for the functional analysis of biological networks. Bioinformatics. 2010;26(8):1129–30.Google Scholar
  107. 107.
    Efroni S, Schaefer CF, Buetow KH. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS One. 2007;2(5):e425.Google Scholar
  108. 108.
    Jörg R, Jochen M, Thomas L, et al. Calculating the statistical significance of changes in pathway activity from gene expression data. Stat Appl Genet Mol Biol. 2004;3(1):1–31.Google Scholar
  109. 109.
    Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C, Romero R. A systems biology approach for pathway level analysis. Genome Res. 2007;17(10):1537–45.Google Scholar
  110. 110.
    Tarca AL, Draghici S, Khatri P, Hassan SS, Mittal P, Kim Js, Kim CJ, Kusanovic JP, Romero R. A novel signaling pathway impact analysis. Bioinformatics. 2009;25(1):75–82.Google Scholar
  111. 111.
    Shojaie A, Michailidis G. Penalized principal component regression on graphs for analysis of subnetworks. In: Advances in neural information processing systems; 2010. pp. 2155–63.Google Scholar
  112. 112.
    Bansal M, Belcastro V, Ambesi-Impiombato A, Di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol. 2007;3(1).Google Scholar
  113. 113.
    Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera R, Califano A. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006;7(Suppl 1):S7.Google Scholar
  114. 114.
    Gardner T, Faith J. Reverse-engineering transcription control networks. Phys Life Rev. 2005;2(1):65–88.Google Scholar
  115. 115.
    Meyer P, Lafitte F, Bontempi G. {minet}: An R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics. 2008;9(1):461.Google Scholar
  116. 116.
    de la Fuente A, Brazhnik P, Mendes P. Linking the genes: inferring quantitative gene networks from microarray data. TRENDS Genet. 2002;18(8);395–8.Google Scholar
  117. 117.
    Gardner T, di Bernardo, D, Lorenz D, Collins J: Inferring genetic networks and identifying compound mode of action via expression profiling. Sci Signal. 2003;301(5629):102.Google Scholar
  118. 118.
    Rice J, Tu Y, Stolovitzky G. Reconstructing biological networks using conditional correlation analysis. Bioinformatics. 21(6):765–73.Google Scholar
  119. 119.
    Marbach D, Prill R, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G: Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci. 2010;107(14):6286–91.Google Scholar
  120. 120.
    Altay G, Emmert-Streib F: Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics. 2010;26(14):1738–44.Google Scholar
  121. 121.
    Dodd IB, Micheelsen MA, Sneppen K, Thon G. Theoretical analysis of epigenetic cell memory by nucleosome modification. Cell. 2007;129(4):813–22.Google Scholar
  122. 122.
    Sedighi M, Sengupta AM. Epigenetic chromatin silencing: bistability and front propagation. Phys Biol. 2007;4(4):246–55.Google Scholar
  123. 123.
    Graf T, Enver T. Forcing cells to change lineages. Nature. 2009;462:(7273):587–94.Google Scholar
  124. 124.
    Choi JK, Yu U, Yoo OJ, Kim S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics. 2005;21(24):4348–55.Google Scholar
  125. 125.
    Ho YY, Cope L, Dettling M, Parmigiani G. Statistical methods for identifying differentially expressed gene combinations. In: Gene function analysis. Springer; 2007. pp. 171–91.Google Scholar
  126. 126.
    Dettling M, Gabrielson E, Parmigiani G. Searching for differentially expressed gene combinations. Genome Biol. 2005;6:R88.Google Scholar
  127. 127.
    Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human b cells. Nat Genet. 2005;37(4):382–90.Google Scholar
  128. 128.
    Vallat L, Kemper CA, Jung N, Maumy-Bertrand M, Bertrand F, Meyer N, Pocheville A, Fisher JW, Gribben JG, Bahram S. Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proc Natl Acad Sci. 2013;110(2):459–64.Google Scholar
  129. 129.
    Volinia S, Galasso M, Costinean S, Tagliavini L, Gamberoni G, Drusco A, Marchesini J, Mascellani N, Sana ME, Jarour RA, et al. Reprogramming of miRNA networks in cancer and leukemia. Genome Res. 2010;20(5):589–99.Google Scholar
  130. 130.
    Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, Canese K, Chetvernin V, Church DM, DiCuccio M, Federhen S, et al. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2011;39(Suppl 1):D38–51.Google Scholar
  131. 131.
    Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009;37(Suppl 1):D885–90.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Biostatistics Division, Department of Preventive Medicine and Northwestern Institute on Complex SystemsNorthwestern UniversityChicagoUSA

Personalised recommendations