Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods
详细信息    查看全文
  • 作者:Frank Emmert-Streib (1)
    Shailesh Tripathi (1)
    Ricardo de Matos Simoes (1)
  • 关键词:Gene expression data ; Cancer data ; Statistical analysis methods ; Pathway methods ; Correlation structure ; Cancer genomics
  • 刊名:Biology Direct
  • 出版年:2012
  • 出版时间:December 2012
  • 年:2012
  • 卷:7
  • 期:1
  • 全文大小:524KB
  • 参考文献:1. Bock G, Goode J: / Novartis Foundation Symposium. John Wiley & Sons; 1998.
    2. Van Regenmortel M: Reductionism and complexity in molecular biology. / EMBO reports 2004,5(9):1016鈥?020. CrossRef
    3. Mazzocchi F: Complexity in biology. / EMBO Rep 2008, 9:10鈥?4. CrossRef
    4. von Bertalanffy L: An outline of general systems theory. / Br J Philosophy Sci 1950,1(2):134鈥?65. CrossRef
    5. Beadle GW, Tatum EL: Genetic control of biochemical reactions in neurospora. / Proc Natl Acad Sci USA 1941,27(11):499鈥?06. CrossRef
    6. Hanahan D, Weinberg RA: The hallmarks of cancer. / Cell 2000, 100:57鈥?0. CrossRef
    7. Noble D: Genes and causation. / Phil Trans R Soc A 2008, 366:3001鈥?3015. CrossRef
    8. Kitano H: Systems biology: a brief overview. / Science 2002,295(5560):1662鈥?664. CrossRef
    9. Han JDJ: Understanding biological functions through molecular networks. / Cell Res 2008,18(2):224鈥?37. CrossRef
    10. MacDougall-Shackleton SA: The levels of analysis revisited. / Phil Trans R Soc B: Biol Sci 2011,366(1574):2076鈥?085. CrossRef
    11. Barabasi AL, Oltvai ZN: Network biology: understanding the cell鈥檚 functional organization. / Nat Rev 2004, 5:101鈥?13. CrossRef
    12. Brazhnik P, de la Fuente A, Mendes P: Gene networks: how to put the function in genomics. / Trends Biotechnol 2002,20(11):467鈥?72. CrossRef
    13. Emmert-Streib F, Glazko G: Network biology: a direct approach to study biological function. / Wiley Interdiscip Rev Syst Biol Med 2011,3(4):379鈥?91. CrossRef
    14. Davidson E, Levin M: Gene regulatory networks. / Proc Natl Acad Sci USA 2005,102(14):4935. CrossRef
    15. de Matos Simoes R, Tripathi S, Emmert-Streib F: Organizational structure of the peripheral gene regulatory network in B-cell lymphoma. / BMC Syst Biol 2012, 6:38. CrossRef
    16. Jones S, Thornton JM: Principles of protein-protein interactions. / Proc Nat Acad Sci 1996, 93:13鈥?0. CrossRef
    17. Maslov S, Sneppen K: Specificity and stability in topology of protein networks. / Science 2002,296(5569):910鈥?13. CrossRef
    18. Jeong H, Tombor B, Albert R, Olivai Z, Barabasi A: The large-scale organization of metabolic networks. / Nature 2000, 407:651鈥?54. CrossRef
    19. Babu MM, Luscombe NM, Aravind L, Gerstein M, Teichmann SA: Structure and evolution of transcriptional regulatory networks. / Curr Opin Struct Biol 2004, 14:283鈥?91. CrossRef
    20. Lee TI, / et al.: Transcriptional regulatory networks in saccharomyces cerevisiae. / Science 2002,298(5594):799鈥?04. CrossRef
    21. Allison DB: Microarray data analysis: from disarray to consolidation and consensus. / Nat Rev Genet 2006, 7:55鈥?5. CrossRef
    22. Dehmer M, Emmert-Streib F, Graber A, Salvador A(Eds): / Applied Statistics for Network Biology: Methods for Systems Biology. Weinheim: Wiley-Blackwell; 2011. CrossRef
    23. Quackenbush J: Computational analysis of microarray data. / Nat Rev Genet 2001,2(6):418鈥?27. CrossRef
    24. Metzker ML: Sequencing technologies - the next generation (With NOTES). / Nat Rev Genet 2010, 11:31鈥?6. CrossRef
    25. Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. / Nat Rev Genet 2009, 10:57鈥?3. CrossRef
    26. Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. / Cell 2011,144(5):646鈥?74. CrossRef
    27. Subramanian A, Tamayo P, Mootha V, Mukherjee S, Ebert B, Gillette M, Paulovich A, Pomeroy S, Golub T, Lander E, Mesirov J: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. / Proc Natl Acad Sci USA 2005,102(43):15545鈥?0. CrossRef
    28. Chuang HY, Lee E, Liu YT, Ideker T: Network-based classification of breast cancer metastasis. / Mol Syst Biol 2007, 3:140. CrossRef
    29. Compagno M, Lim WK, Grunn A, Nandula SV, Brahmachary M, Shen Q, Bertoni F, Ponzoni M, Scandurra M, Califano A, / et al.: Mutations of multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. / Nature 2009,459(7247):717鈥?21. CrossRef
    30. Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, / et al.: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. / Proc Natl Acad Sci USA 2006,103(46):17402鈥?7407. CrossRef
    31. Krivtsov AV, Twomey D, Feng Z, Stubbs MC, Wang Y, Faber J, Levine JE, Wang J, Hahn WC, Gilliland DG, / et al.: Transformation from committed progenitor to leukaemia stem cell initiated by MLL-AF9. / Nature 2006,442(7104):818鈥?22. CrossRef
    32. Oskarsson T, Acharyya S, Zhang XHF, Vanharanta S, Tavazoie SF, Morris PG, Downey RJ, Manova-Todorova K, Brogi E, Massague J: Breast cancer cells produce tenascin C as a metastatic niche component to colonize the lungs. / Nat Med 2011,17(7):867鈥?74. CrossRef
    33. Mavrakis KJ, Wolfe AL, Oricchio E, Palomero T, De Keersmaecker K, McJunkin K, Zuber J, James T, Khan AA, Leslie CS, / et al.: Genome-wide RNA-mediated interference screen identifies miR-19 targets in Notch-induced T-cell acute lymphoblastic leukaemia. / Nat Cell Biol 2010,12(4):372鈥?79. CrossRef
    34. Nam S, Park T: Pathway-based evaluation in early onset colorectal cancer suggests focal adhesion and immunosuppression along with Epithelial-Mesenchymal transition. / PLoS ONE 2012,7(4):e31685. CrossRef
    35. Guedj M, Marisa L, De Reynies A, Orsetti B, Schiappa R, Bibeau F, Macgrogan G, Lerebours F, Finetti P, Longy M, / et al.: A refined molecular taxonomy of breast cancer. / Oncogene 2011,31(July 2011):1196鈥?206.
    36. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA: Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. / J Clin Invest 2011,121(7):2750鈥?767. CrossRef
    37. Fabbri G, Rasi S, Rossi D, Trifonov V, Khiabanian H, Ma J, Grunn A, Fangazio M, Capello D, Monti S, / et al.: Analysis of the chronic lymphocytic leukemia coding genome: role of NOTCH1 mutational activation. / J Exp Med 2011,208(7):1389鈥?401. CrossRef
    38. Ooi CH, Ivanova T, Wu J, Lee M, Tan IB, Tao J, Ward L, Koo JH, Gopalakrishnan V, Zhu Y, Cheng LL, Lee J, Rha SY, Chung HC, Ganesan K, So J, Soo KC, Lim D, Chan WH, Wong WK, Bowtell D, Yeoh KG, Grabsch H, Boussioutas A, Tan P: Oncogenic pathway combinations predict clinical prognosis in gastric cancer. / PLoS Genet 2009,5(10):e1000676. CrossRef
    39. Setlur SR, Royce TE, Sboner A, Mosquera JM, Demichelis F, Hofer MD, Mertz KD, Gerstein M, Rubin MA: Integrative microarray analysis of pathways dysregulated in metastatic prostate cancer. / Cancer Res 2007,67(21):10296鈥?0303. CrossRef
    40. Nucera C, Porrello A, Antonello ZA, Mekel M, Nehs MA, Giordano TJ, Gerald D, Benjamin LE, Priolo C, Puxeddu E, / et al.: B-Raf(V600E) and thrombospondin-1 promote thyroid cancer progression. / Proc Natl Acad Sci USA 2010,107(23):10649鈥?0654. CrossRef
    41. Shah MA, Khanin R, Tang L, Janjigian YY, Klimstra DS, Gerdes H, Kelsen DP: Molecular classification of gastric cancer: a new paradigm. / Clin Cancer Res 2011,17(9):2693鈥?701. CrossRef
    42. Perroud B, Lee J, Valkova N, Dhirapong A, Lin PY, Fiehn O, Kultz D, Weiss R: Pathway analysis of kidney cancer using proteomics and metabolic profiling. / Mol Cancer 2006, 5:64. CrossRef
    43. Trewavas A: A Brief History of Systems Biology: 鈥淓very object that biology studies is a system of systems.鈥?Francois Jacob (1974). / Plant Cell 2006,18(10):2420鈥?430. CrossRef
    44. Emmert-Streib F, Dehmer M: Networks for systems biology: conceptual connection of data and function. / IET Syst Biol 2011,5(3):185. CrossRef
    45. Macneil LT, Walhout AJM: Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. / Genome Res 2011,21(5):645鈥?7. CrossRef
    46. Lehman E: / Testing Statistical Hypotheses. New York: Springer; 2005.
    47. DasGupta A: / Probability for Statistics and Machine Learning. New York: Springer; 2011. CrossRef
    48. Chen Y, Dougherty ER, Bittner ML: Ratio-based decisions and the quantitative analysis of cDNA microarray smages. / J Biomed Optics 1997,2(4):364鈥?74. CrossRef
    49. Zhang L, Zhou W, Velculescu VE, Kern SE, Hruban RH, Hamilton SR, Vogelstein B, Kinzler KW: Gene expression profiles in normal and cancer cells. / Science 1997,276(5316):1268鈥?272. CrossRef
    50. Tusher V, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. / Proc Natl Acad Sci USA 2001,98(18):5116鈥?121. CrossRef
    51. Chu G, Narasimhan B, Tibshirani R, Tusher V: Significance analysis of microarrays (SAM) software. / Nature 2002, 5:436鈥?42.
    52. Smyth GK: Limma: linear models for microarray data. In / Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. New York: Springer; 2005:397鈥?20. CrossRef
    53. Efron B, Tibshirani R, JD S, Tusher V: Empirical Bayes analysis of a microarray experiment. / J Am Stat Assoc 2001,96(456):1151鈥?160. CrossRef
    54. Ho JWK, Stefani M, Dos Remedios CG, Charleston MA: Differential variability analysis of gene expression and its application to human diseases. / Bioinformatics 2008,24(13):i390-i398. CrossRef
    55. Hu R, Qiu X, Glazko G, Klebanov L, Yakovlev A: Detecting intergene correlation changes in microarray analysis: a new approach to gene selection. / BMC Bioinformatics 2009, 10:20. CrossRef
    56. Dettling M, Gabrielson E, Parmigiani G: Searching for differentially expressed gene combinations. / Genome Biol 2005,6(10):R88. CrossRef
    57. Lai Y, Wu B, Chen L, Zhao H: A statistical method for identifying differential gene-gene co-expression patterns. / Bioinformatics 2004,20(17):3146鈥?155. CrossRef
    58. Dawson JA, Ye S, Kendziorski C: R/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression. / Bioinformatics 2012,28(14):1939鈥?940. CrossRef
    59. Li KC: Genome-wide coexpression dynamics: theory and application. / Proc Natl Acad Sci USA 2002, 99:16875鈥?6880. CrossRef
    60. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The gene ontology consortium. / Nature Genet 2000, 25:25鈥?9. CrossRef
    61. Ackermann M, Strimmer K: A general modular framework for gene set enrichment analysis. / BMC Bioinformatics 2009, 10:47. CrossRef
    62. Dinu I, Potter JD, Mueller T, Liu Q, Adewale AJ, Jhangri GS, Einecke G, Famulski KS, Halloran P, Yasui Y: Gene-set analysis and reduction. / Brief Bioinform 2009, 10:24鈥?4. CrossRef
    63. Emmert-Streib F, Glazko G: Pathway analysis of expression data: deciphering functional building blocks of complex diseases. / PLoS Comput Biology 2011,7(5):e1002053. CrossRef
    64. Khatri P, Sirota M, Butte A J: Ten years of pathway analysis: current approaches and outstanding challenges. / PLoS Comput Biol 2012,8(2):e1002375. CrossRef
    65. Liu Q, Dinu I, Adewale A, Potter J, Yasui Y: Comparative evaluation of gene-set analysis methods. / BMC Bioinformatics 2007, 8:431. CrossRef
    66. Goeman J, Buhlmann P: Analyzing gene expression data in terms of gene sets: methodological issues. / Bioinformatics 2007,23(8):980鈥?. CrossRef
    67. Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. / Nucl Acids Res 2009, 37:1鈥?3. CrossRef
    68. Mootha V, Lindgren C, Eriksson KFea: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. / Nature Genet 2003, 34:267鈥?73. CrossRef
    69. Efron B, Tibshiran R: On testing the significance of sets of genes. / Ann Appl Stat 2007, 1:107鈥?29. CrossRef
    70. D酶rum G, Snipen L, Solheim M, S忙b酶 S: Rotation testing in gene set enrichment analysis for small direct comparison experiments. / Stat App Genet Mol Biol 2009, 8:34.
    71. Luo W, Friedman M, Shedden K, Hankenson K, Woolf P: GAGE: generally applicable gene set enrichment for pathway analysis. / BMC Bioinformatics 2009, 10:161. CrossRef
    72. Kim SY, Volsky D: PAGE: Parametric Analysis of Gene Set Enrichment. / BMC Bioinformatics 2005, 6:144. CrossRef
    73. Newton M, Quintana F, den Boon Jea: Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. / Ann Appl Stat 2007, 1:85鈥?06. CrossRef
    74. Freudenberg JM, Sivaganesan S, Phatak M, Shinde K, Medvedovic M: Generalized random set framework for functional enrichment analysis using primary genomics datasets. / Bioinformatics 2011 Jan 1,27(1):70鈥?. CrossRef
    75. Rafael IA, Chi W, Yun Z, Terence SP: Gene set enrichment analysis made simple. / Stat Methods Med Res 2009,18(6):565鈥?75. CrossRef
    76. Lange K: / Numerical Analysis for Statisticians. Statistics and Computing. Springer; 2010. CrossRef
    77. Pyeon D, Newton MA, Lambert PF, den Boon JA, Sengupta S, Marsit CJ, Woodworth CD, Connor JP, Haugen TH, Smith EM, Kelsey KT, Turek LP, Ahlquist P: Fundamental differences in cell cycle deregulation in human Papillomavirus-positive and human Papillomavirus-negative head/neck and cervical cancers. / Cancer Res 2007,67(10):4605鈥?619. CrossRef
    78. Sheskin DJ: / Handbook of Parametric and Nonparametric Statistical Procedures. 3rd edition. Boca Raton: RC Press; 2004.
    79. Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ: Discovering statistically significant pathways in expression profiling studies. / Proc Natl Acad Sci USA 2005,102(38):13544鈥?3549. CrossRef
    80. Jiang Z, Gentleman R: Extensions to gene set enrichment. / Bioinformatics 2007,23(3):306鈥?13. CrossRef
    81. Dinu I, Potter JD, Mueller T, Liu Q, Adewale AJ, Jhangri GS, Einecke G, Famulski KS, Halloran P, Yasui Y: Improving gene set analysis of microarray data by SAM-GS. / BMC Bioinformatics 2007, 8:242. CrossRef
    82. Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC: A global test for groups of genes: testing association with a clinical outcome. / Bioinformatics 2004, 20:93鈥?9. CrossRef
    83. Hummel M, Meister R, Mansmann U: GlobalANCOVA: exploration and assessment of gene group effects. / Bioinformatics 2008, 24:78鈥?5. CrossRef
    84. Lu Y, Liu P, Xiao P, Deng H: Hotelling鈥檚 T2 multivariate profiling for detecting differential expression in microarrays. / Bioinformatics 2005,21(14):3105鈥?113. CrossRef
    85. Kong S, Pu W, Park P: A multivariate approach for integrating genome-wide expression data and biological knowledge. / Bioinformatics 2006,22(19):2373鈥?380. CrossRef
    86. Tsai C, Chen J: Multivariate analysis of variance test for gene set analysis. / Bioinformatics 2009,25(7):897鈥?03. CrossRef
    87. Xiong H: Non-linear tests for identifying differentially expressed genes or genetic networks. / Bioinformatics 2006,22(8):919鈥?23. CrossRef
    88. Klebanov L, Glazko G, Salzman P, Yakovlev A, Xiao Y: A multivariate extension of the gene set enrichment analysis. / J Bioinform Comput Biol 2007,5(5):1139鈥?153. CrossRef
    89. Yates P, Reimers M: RCMAT: a regularized covariance matrix approach to testing gene sets. / BMC Bioinformatics 2009, 10:300. CrossRef
    90. 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鈥?545. CrossRef
    91. 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, 25:75鈥?2. CrossRef
    92. Thomas R, Gohlke JM, Stopper GF, Parham FM, Portier CJ: Choosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structure. / Genome Biol 2009,10(4):R44. CrossRef
    93. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, Stuart JM: Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. / Bioinformatics 2010,26(12):i237鈥攊245. CrossRef
    94. Massa M, Chiogna M, Romualdi C: Gene set analysis exploiting the topology of a pathway. / BMC Syst Biol 2010, 4:121.
    95. Glazko G, Emmert-Streib F: Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. / Bioinformatics 2009,25(18):2348鈥?354. CrossRef
    96. Ledoit O, Wolf M: Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. / J Empir Finance 2003, 10:603鈥?21. CrossRef
    97. Ledoit O, Wolf M: A well conditioned estimator for largedimensional covariance matrices. / J Multiv Anal 2004, 88:365鈥?11. CrossRef
    98. Ledoit O, Wolf M: Honey, I shrunk the sample covariance matrix. / J Portfolio Manage 2004, 30:110鈥?19. CrossRef
    99. Sch盲fer J, Strimmer K: A shrinkage approach to large-scale covariance matrix estimation and implications for functional Genomics. / Stat Appl Genet Mol Biol 2005, 4:32.
    100. Kanehisa M, Goto S: KEGG: kyoto encyclopia of genes and genomes. / Nuclei Acids Res 2000, 28:27鈥?0. CrossRef
    101. Lauritzen S: / Graphical Models. New York: Oxford Science Publications, Clarendon Press; 1996.
    102. Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, Kanapin A, Lewis S, Mahajan S, May B, Schmidt E, Vastrik I, Wu G, Birney E, Stein L, D鈥橢ustachio P: Reactome knowledgebase of human biological pathways and processes. / Nucleic Acids Res 2009,37(suppl 1):D619鈥擠622.
    103. Klebanov L, Jordan C, Yakovlev A: A new type of stochastic dependence revealed in gene expression data. / Stat Appl Genet Mol Biol 2006,5(05/11):Article7.
    104. Lim J, Kim J, Kim B: An alternative model of type A dependence in a gene set of correlated genes. / Stat Appl in Genet Mol Biol 2010, 9:Article 12.
    105. Tripathi S, Emmert-Streib F: Assessment method for a power analysis to identify differentially expressed pathways. / PLoS ONE 2012,7(5):e37510. CrossRef
    106. Emmert-Streib F: The chronic fatigue syndrome: a comparative pathway analysis. / J Comput Biol 2007,14(7):961鈥?72. CrossRef
    107. Choi Y, Kendziorski C: Statistical methods for gene set co-expression analysis. / Bioinformatics 2009,25(21):2780鈥?786. CrossRef
    108. Cho SB, Kim J, Kim JH: Identifying set-wise differential co-expression in gene expression microarray data. / BMC Bioinformatics 2009, 10:109. CrossRef
    109. Tesson BM, Breitling R, Jansen RC: DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. / BMC Bioinformatics 2010, 11:497. CrossRef
    110. Altay G, Asim M, Markowetz F, Neal DE: Differential C3NET reveals disease networks of direct physical interactions. / BMC Bioinformatics 2011, 12:296. CrossRef
    111. Watkinson J, Wang X, Zheng T, Anastassiou D: Identification of gene interactions associated with disease from gene expression data using synergy networks. / BMC Syst Biol 2008, 2:10. CrossRef
    112. Bunke H: What is the distance between graphs? / Bull EATCS 1983, 20:35鈥?9.
    113. Fuite J, Vernon S, Broderick G: Neuroendocrine and immune network re-modeling in chronic fatigue syndrome: an exploratory analysis. / Genomics 2008, 92:393鈥?99. CrossRef
    114. Wang YC, Lan CY, Hsieh WP, Murillo L, Agabian N, Chen BS: Global screening of potential Candida albicans biofilm-related transcription factors via network comparison. / BMC Bioinformatics 2010, 11:53. CrossRef
    115. Gill R, Datta S, Datta S: A statistical framework for differential network analysis from microarray data. / BMC Bioinformatics 2010, 11:95. CrossRef
    116. Altay G, Emmert-Streib F: Inferring the conservative causal core of gene regulatory networks. / BMC Syst Biol 2010, 4:132. CrossRef
    117. Altay G, Emmert-Streib F: Structural influence of gene networks on their inference: analysis of C3NET. / Biol Direct 2011, 6:31. CrossRef
    118. Dempster A: Covariance selection. / Biometrics 1972, 28:157鈥?75. CrossRef
    119. Koller D, Friedman N: / Probabilistic Graphical Models: Principles and Techniques. Cambridge: The MIT Press; 2009.
    120. Whittaker J: / Graphical Models in Applied Multivariate Statistics. Chichester: Wiley; 1990.
    121. Li H, Gui J: Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks. / Biostatistics 2006,7(2):302鈥?17. CrossRef
    122. Sch盲fer J, Strimmer K: An empirical Bayes approach to inferring large-scale gene association networks. / Bioinformatics 2005, 21:754鈥?64. CrossRef
    123. Wille A, Zimmermann P, Vranova E, Furholz A, Laule O, Bleuler S, Hennig L, Prelic A, von Rohr P, Thiele L, Zitzler E, Gruissem W, Buhlmann P: Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana. / Genome Biol 2004,5(11):R92. CrossRef
    124. Fan J, Feng Y, Wu Y: Network exploration via the adaptive lasso and SCAD penalties. / Ann Appl Stat 2009,3(2):521鈥?41. CrossRef
    125. Friedman J, Hastie T, Tibshirani R: Sparse inverse covariance estimation with the graphical lasso. / Biostatistics Oxford England 2008,9(3):432鈥?41. CrossRef
    126. Kiiveri H, de Hoog F: Fitting very large sparse Gaussian graphical models. / Comput Stat & Data Anal 2012,56(9):2626鈥?636. CrossRef
    127. Witten DM, Friedman JH, Simon N: New insights and faster computations for the graphical Lasso. / J Comput Graphical Stat 2011,20(4):892鈥?00. CrossRef
    128. Zou H: The adaptive Lasso and its oracle properties. / J Am Stat Assoc 2006,101(476):1418鈥?429. CrossRef
    129. Dudoit S, van der Laan M: / Multiple Testing Procedures with Applications to Genomics. New York: Springer; 2007.
    130. Dudoit S, van der Laan M, Pollard K: Multiple testing. part I. single-step procedures for control of general type I error rates. / Stat App Genet Mol Biol 2004, 3:13.
    131. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. / J R Stat Soc, Ser B (Methodological) 1995, 57:125鈥?33.
    132. Storey J: A direct approach to false discovery rates. / J R Stat Soc, Ser B 2002, 64:479鈥?98. CrossRef
    133. Aubert J, Bar-Hen A, Daudin J, Robin S: Determination of the differentially expressed genes in microarray experiments using local FDR. / BMC Bioinformatics 2004, 5:125. CrossRef
    134. Efron B: Correlation and large-scale simultaneous signifance testing. / J Am Stat Assoc 2007,102(477):93鈥?03. CrossRef
    135. Pounds S, Morris SW: Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. / Bioinformatics 2003,19(10):1236鈥?242. CrossRef
    136. Benjamini Y, Yekutieli D: The control of the false discovery rate in multiple testing under dependency. / Ann Stat 2001,29(4):1165鈥?188. CrossRef
    137. Efron B: Size, power and false discovery rates. / Ann Stat 2007,35(4):1351鈥?377. CrossRef
    138. Storey J, Tibshirani R: Statistical significance for genomewide studies. / Proc Natl Acad Sci USA 2003,100(16):9440鈥?445. CrossRef
    139. Hung JH, Yang TH, Hu Z, Weng Z, DeLisi C: Gene set enrichment analysis: performance evaluation and usage guidelines. / Briefings in Bioinformatics 2012,13(3):281鈥?91. CrossRef
    140. Nam D, Kim S: Gene-set approach for expression pattern analysis. / Brief Bioinform 2008,9(3):189鈥?97. CrossRef
    141. Weinberg RA: / The Biology of Cancer. New York: Garland Science; 2007.
    142. Leek JT, Storey JD: Capturing heterogeneity in gene expression studies by surrogate variable analysis. / PLoS Genet 2007,3(9):e161. CrossRef
    143. McClintick JN, Edenberg HJ: Effects of filtering by Present call on analysis of microarray experiments. / BMC Bioinformatics 2006, 7:49. CrossRef
    144. Bourgon R, Gentleman R, Huber W: Independent filtering increases detection power for high-throughput experiments. / Proc Natl Acad Sci USA 2010,107(21):9546鈥?551. CrossRef
    145. Carter GW: Inferring network interactions within a cell. / Briefings in Bioinformatics 2005,6(4):380鈥?89. CrossRef
    146. Perou CM, Sorlie T, Eisen MB, Van De Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, / et al.: Molecular portraits of human breast tumours. / Nature 2000,406(6797):747鈥?52. CrossRef
    147. Gerstung M, Eriksson N, Lin J, Vogelstein B, Beerenwinkel N: The temporal order of genetic and pathway alterations in Tumorigenesis. / PLoS ONE 2011,6(11):e27136. CrossRef
    148. Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A, / et al.: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. / Science 2008,321(5897):1801鈥?806. CrossRef
    149. Wood LD, Parsons DW, Jones S, Lin J, Sj枚blom T, Leary RJ, Shen D, Boca SM, Barber T, Ptak J, / et al.: The genomic landscapes of human breast and colorectal cancers. / Science 2007,318(5853):1108鈥?113. CrossRef
    150. Vogelstein B, Kinzler KW: Cancer genes and the pathways they control. / Nature Med 2004,10(8):789鈥?99. CrossRef
    151. Lazebnik Y: What are the hallmarks of cancer? / Nature Rev Cancer 2010,10(4):232鈥?33. CrossRef
    152. Mukherjee S: / The Emperor of All Maladies: A Biography of Cancer. London: Fourth Estate; 2011.
  • 作者单位:Frank Emmert-Streib (1)
    Shailesh Tripathi (1)
    Ricardo de Matos Simoes (1)

    1. Computational Biology and Machine Learning Laboratory, Queen鈥檚 University Belfast, Belfast, UK
文摘

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700