Ensemble-based network aggregation improves the accuracy of gene network reconstruction

PLoS ONE

Volumn 9, Issue 11, 2014, Pages

  Zhong, Rui ^a   Allen, Jeffrey D ^a,b   Xiao, Guanghua ^a   Xie, Yang ^a,b  

^a UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^b UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA;

ACCURACY; ARTICLE; BOOTSTRAPPING; CONTROLLED STUDY; DATA ANALYSIS; ENSEMBLE BASED NETWORK AGGEGATION APPROACH; EPITHELIAL MESENCHYMAL TRANSITION; ESCHERICHIA COLI; GENE EXPRESSION; GENE REGULATORY NETWORK; GENETIC ASSOCIATION; GENETIC PROCEDURES; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MICROARRAY ANALYSIS; NONHUMAN; PROCESS DEVELOPMENT; PROCESS MODEL; ALGORITHM; BIOLOGY; COMPUTER PROGRAM; COMPUTER SIMULATION; DNA MICROARRAY; GENE EXPRESSION REGULATION; GENETIC DATABASE; GENETICS; GENOME; HUMAN; LUNG TUMOR; METABOLISM; NON SMALL CELL LUNG CANCER; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; SEQUENCE ANALYSIS;

ALGORITHMS; CARCINOMA, NON-SMALL-CELL LUNG; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, GENETIC; EPITHELIAL-MESENCHYMAL TRANSITION; ESCHERICHIA COLI; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; GENOME; HUMANS; LUNG NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 84911409703     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0106319     Document Type: Article

References (52)

1. Sun X, Vilar S, Tatonetti NP (2013) High-throughput methods for combinatorial drug discovery. Sci Transl Med 5: 205rv201.
2. Rix U, Colinge J, Blatt K, Gridling M, Remsing Rix LL, et al. (2013) A Target- Disease Network Model of Second-Generation BCR-ABL Inhibitor Action in Ph+ ALL. PLoS One 8: e77155.
3. Zhao H, Jin G, Cui K, Ren D, Liu T, et al. (2013) Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases. Cancer Res 73: 6149-6163.
4. Wang XS, Simon R (2013) Identification of potential synthetic lethal genes to p53 using a computational biology approach. Bmc Medical Genomics 6.
5. Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303: 799-805.
6. Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, et al. (2002) Revealing modular organization in the yeast transcriptional network. Nature Genetics 31: 370-377.
7. Lee I, Date SV, Adai AT, Marcotte EM (2004) A probabilistic functional network of yeast genes. Science 306: 1555-1558.
8. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP (2005) Causal proteinsignaling networks derived from multiparameter single-cell data. Science 308: 523-529.
9. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, et al. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34: 166-176.
10. Stuart JM, Segal E, Koller D, Kim SK (2003) A gene-coexpression network for global discovery of conserved genetic modules. Science 302: 249-255.
11. Kendall SD, Linardic CM, Adam SJ, Counter CM (2005) A network of genetic events sufficient to convert normal human cells to a tumorigenic state. Cancer Research 65: 9824-9828.
12. Mani KM, Lefebvre C, Wang K, Lim WK, Basso K, et al. (2008) A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Molecular Systems Biology 4.
13. Tang H, Xiao G, Behrens C, Schiller J, Allen J, et al. (2013) A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients. Clin Cancer Res 19: 1577-1586.
14. Nibbe RK, Koyuturk M, Chance MR (2010) An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer. Plos Computational Biology 6.
15. Slavov N, Dawson KA (2009) Correlation signature of the macroscopic states of the gene regulatory network in cancer. Proceedings of the National Academy of Sciences of the United States of America 106: 4079-4084.
16. Friedman N, Linial M, Nachman I, Pe'er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7: 601-620.
17. Liang F (2009) Learning Bayesian Networks for Gene Expression Data. In: Dey D, Ghosh S, Mallick B, editors. Bayesian Modeling in Bioinformatics: Chapman & Hall/CRC Biostatistics Series.
18. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9: 559-559.
19. Peng J, Wang P, Zhou N, Zhu J (2009) Partial Correlation Estimation by Joint Sparse Regression Models. J Am Stat Assoc 104: 735-746.
20. Schä fer J, Strimmer K (2005) An empirical Bayes approach to inferring largescale gene association networks. Bioinformatics 21: 754-764.
21. Allen JD, Xie Y, Chen M, Girard L, Xiao G (2012) Comparing statistical methods for constructing large scale gene networks. PLoS One 7: e29348.
22. Allen JD, Wang S, Chen M, Girard L, Minna JD, et al. (2012) Probe mapping across multiple microarray platforms. Brief Bioinform 13: 547-554.
23. Liu J, Huang J, Ma S (2013) Incorporating network structure in integrative analysis of cancer prognosis data. Genet Epidemiol 37: 173-183.
24. Ma S, Huang J, Song X (2011) Integrative analysis and variable selection with multiple high-dimensional data sets. Biostatistics 12: 763-775.
25. Breitling R, Armengaud P, Amtmann A, Herzyk P (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 573: 83-92.
26. Breitling R, Herzyk P (2005) Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data. J Bioinform Comput Biol 3: 1171-1189.
27. Birmingham A, Selfors LM, Forster T, Wrobel D, Kennedy CJ, et al. (2009) Statistical methods for analysis of high-throughput RNA interference screens. Nat Methods 6: 569-575.
28. Wiederhold E, Gandhi T, Permentier HP, Breitling R, Poolman B, et al. (2009) The yeast vacuolar membrane proteome. Mol Cell Proteomics 8: 380-392.
29. Koziol JA (2010) Comments on the rank product method for analyzing replicated experiments. FEBS Lett 584: 941-944.
30. Faith JJ, Driscoll ME, Fusaro VA, Cosgrove EJ, Hayete B, et al. (2008) Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res 36: 866-870.
31. Fong SS, Joyce AR, Palsson BØ (2005) Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res 15: 1365-1372.
32. Sangurdekar DP, Srienc F, Khodursky AB (2006) A classification based framework for quantitative description of large-scale microarray data. Genome Biol 7.
33. Xiao G, Wang X, Khodursky AB (2011) Modeling Three-Dimensional Chromosome Structures Using Gene Expression Data. J Am Stat Assoc 106: 61-72.
34. Byers LA, Diao L, Wang J, Saintigny P, Girard L, et al. (2013) An epithelialmesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin Cancer Res 19: 279-290.
35. Lim J, Lee S, Choi H (2006) Information loss from censoring in rank-based procedures. Statistics & Probability Letters 76: 1705-1713.
36. Penrose R (1954) A Generalized Inverse for Matrices; 1954. pp. 406-413.
37. th European PVM/MPI Users' Group Meeting. Budapest, Hungary. pp. 97-104.
38. Xie Y (2013) knitr: A Comprehensive Tool for Reproducible Research in R. In: Stodden V, Leisch F, Peng D, editors. Implementing Reproducible Computational Research: Chapman and Hall/CRC.
39. Xie Y (2013) Dynamic Documents with R and knitr: Chapman and Hall/CRC.
40. Mishra GR, Suresh M, Kumaran K, Kannabiran N, Suresh S, et al. (2006) Human protein reference database-2006 update. Nucleic Acids Res 34: 411-414.
41. Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, et al. (2004) Human protein reference database as a discovery resource for proteomics. Nucleic Acids Research 32: D497-D501.
42. Pan W, Lin J, Le CT (2002) How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol 3.
43. Gama-Castro S, Salgado H, Peralta-Gil M, Santos-Zavaleta A, Muñiz-Rascado L, et al. (2011) RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units). Nucleic Acids Res 39: 98-9105.
44. Salgado H, Martinez-Flores I, Lopez-Fuentes A, Garcia-Sotelo JS, Porron- Sotelo L, et al. (2012) Extracting regulatory networks of Escherichia coli from RegulonDB. Methods Mol Biol 804: 179-195.
45. Wellner U, Schubert J, Burk UC, Schmalhofer O, Zhu F, et al. (2009) The EMT-activator ZEB1 promotes tumorigenicity by repressing stemness-inhibiting microRNAs. Nat Cell Biol 11: 1487-1495.
46. Schmalhofer O, Brabletz S, Brabletz T (2009) E-cadherin, beta-catenin, and ZEB1 in malignant progression of cancer. Cancer Metastasis Rev 28: 151-166.
47. Kojima T, Sawada N (2012) Regulation of tight junctions in human normal pancreatic duct epithelial cells and cancer cells. Ann N Y Acad Sci 1257: 85-92.
48. Peng L, Wang H, Dong Y, Ma J, Wen J, et al. (2013) Increased expression of EphA1 protein in prostate cancers correlates with high Gleason score. Int J Clin Exp Pathol 6: 1854-1860.
49. Maki-Nevala S, Kaur Sarhadi V, Tuononen K, Lagstrom S, Ellonen P, et al. (2013) Mutated Ephrin Receptor Genes in Non-Small Cell Lung Carcinoma and Their Occurrence with Driver Mutations-Targeted Resequencing Study on Formalin-Fixed, Paraffin-Embedded Tumor Material of 81 Patients. Genes Chromosomes Cancer.
50. Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, et al. (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9: 796-804.
51. Hase T, Ghosh S, Yamanaka R, Kitano H (2013) Harnessing diversity towards the reconstructing of large scale gene regulatory networks. PLoS Comput Biol 9: e1003361.
52. Breiman L (1996) Heuristics of Instability and Stabilization in Model Selection. The Annals of Statistics 24: 2350-2383.