A scalable method for integration and functional analysis of multiple microarray datasets

Bioinformatics

Volumn 22, Issue 23, 2006, Pages 2890-2897

  Huttenhower, Curtis ^a   Hibbs, Matt ^a   Myers, Chad ^a   Troyanskaya, Olga G ^a  

^a Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; AUTOMATION; BAYES THEOREM; BIOINFORMATICS; CONTROLLED STUDY; INFORMATION PROCESSING; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROTEIN MICROARRAY; RATING SCALE; SACCHAROMYCES CEREVISIAE; VALIDATION PROCESS;

SACCHAROMYCES CEREVISIAE;

EID: 33751359281     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btl492     Document Type: Article

References (60)

1. Allison,D.B. (2006) Microarray data analysis: From disarray to consolidation and consensus. Nat. Rev. Genet., 7, 55-65.
2. Angus-Hill,M.L. et al. (2001) A Rsc3/Rsc30 zinc cluster dimer reveals novel roles for the chromatin remodeler RSC in gene expression and cell cycle control. Mol. Cell, 7, 741-751.
3. Ashburner,M. et al. (2000) Gene Ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet., 25, 25-29.
4. Baggerly,K.A. et al. (2001) Identifying differentially expressed genes in cDNA microarray experiments. J. Comput. Biol., 8, 639-659.
5. Ball,C.A. et al. (2005) The stanford microarray database accommodates additional microarray platforms and data formats. Nucleic Acids Res., 33, D580-D582.
6. Barrett,T. et al. (2005) NCBI GEO: Mining millions of expression profiles - database and tools. Nucleic Acids Res., 33, D562-D566.
7. Bergmann,S. et al. (2003) Iterative signature algorithm for the analysis of large-scale gene expression data. Phys. Rev. E. Stat. Nonlin. Soft. Matter Phys., 67, 031902.
8. Bozdech,Z. et al. (2003) Expression profiling of the schizont and trophozoite stages of Plasmodium falciparum with a long-oligonucleotide microarray. Genome Biol., 4, R9.
9. Brem,R.B. and Kruglyak,L. (2005) The landscape of genetic complexity across 5700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA, 102, 1572-1577.
10. Brunner,S. et al. (2002) Su e of the yeast F1Fo-ATP synthase forms homodimers. J. Biol. Chem., 277, 48484-48489.
11. Butte,A.J. and Kohane,I.S. (2006) Creation and implications of a phenome-genome network. Nat. Biotechnol., 24, 55-62.
12. Butte,A.J. et al. (2000) Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl Acad. Sci. USA, 97, 12182-12186.
13. Cheng,Y. and Church,G.M. (2000) Biclustering of expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol., 8, 93-103.
14. Choi,J.K. et al. (2003) Combining multiple microarray studies and modeling interstudy variation. Bioinformatics, 19, i84-i90.
15. Clare,A. and King,R.D. (2003) Predicting gene function in Saccharomyces cerevisiae. Bioinformatics, 19, 1142-1149.
16. Cui,X. and Churchill,G.A. (2003) Statistical tests for differential expression in cDNA microarray experiments. Genome Biol., 4, 210.
17. David,F.N. (1949) The moments of the Z and F distributions. Biometrika, 36, 394-403.
18. Druzdzel,M. SMILE: Structural modeling, inference, and learning engine and genie: A development environment for graphical decision-theoretic models. Proceedings of the Sixteenth National Conference on Artificial Intelligence pp. 902-903.
19. Eisen,M.B. et al. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA, 95, 14863-14868.
20. Gasch,A.P. et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11, 4241-4257.
21. Griffith,O.L. et al. (2005) Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses. Genomics, 86, 476-488.
22. Griffith,J.S. et al. (1999) Rapamycin-modulated transcription defines the subset of nutrient-sensitive signaling pathways directly controlled by the Tor proteins. Proc. Natl Acad. Sci. USA, 96, 14866-14870.
23. Heyer,L.J. et al. (1999) Exploring expression data: Identification and analysis of coexpressed genes. Genome Res., 9, 1106-1115.
24. Hu,P. et al. (2005) Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models. BMC Bioinformatics, 6, 128.
25. Huh,W.K. et al. (2003) Global analysis of protein localization in budding yeast. Nature, 425, 686-691.
26. Huttenhower,C. and Troyanskaya,O. (2006) Bayesian data integration: A functional perspective. Comput. Syst. Bioinformatics, in press.
27. Ideker,T.E. et al. (2000) Discovery of regulatory interactions through perturbation: Inference and experimental design. Pac. Symp. Biocomput., 305-316.
28. Ihmels,J. et al. (2005) Comparative gene expression analysis by differential clustering approach: Application to the Candida albicans transcription program. PLoS Genet., 1, e39.
29. Ihmels,J. et al. (2002) Revealing modular organization in the yeast transcriptional network. Nat. Genet., 31, 370-377.
30. Jansen,R. et al. (2002) Relating whole-genome expression data with protein-protein interactions. Genome Res., 12, 37-46.
31. Jin,Y.S. et al. (2004) Saccharomyces cerevisiae engineered for xylose metabolism exhibits a respiratory response. Appl. Environ. Microbiol., 70, 6816-6825.
32. Karaoz,U. et al. (2004) Whole-genome annotation by using evidence integration in functional-linkage, networks. Proc. Natl. Acad. Sci. USA, 101, 2888-2893.
33. Kloster,M. et al. (2005) Finding regulatory modules through large-scale gene-expression data analysis. Bioinformatics, 21, 1172-1179.
34. Lanckriet,G.R. et al. (2004) Kemel-based data fusion and its application to protein function prediction in yeast. Pac. Symp. Biocomput., 300-311.
35. Lauritzen,S. and Spiegelhalter,D. (1988) Local computations with probabilities on graphical structures and their application to expert systems. J. R. Stat. Soc., 50.
36. Lee,I. et al. (2004) A probabilistic functional network of yeast genes. Science, 306, 1555-1558.
37. Madhani,H.D. and Fink,G.R. (1997) Combinatorial control required for the specificity of yeast MAPK signaling. Science, 275, 1314-1317.
38. Mah,A.S. et al. (2005) Substrate specificity analysis of protein kinase complex Dbf2-Mob1 by peptide library and proteome array screening. BMC Biochem., 6, 22.
39. Moreau, Y. et al. (2003) Comparison and meta-analysis of microarray data: From the bench to the computer desk. Trends Genet., 19, 570-577.
40. Mulder,N.J. et al. (2005) InterPro, progress and status in 2005. Nucleic Acids Res., 33, D201-D205.
41. Mulligan,M.K. et al. (2006) Toward understanding the genetics of alcohol drinking through transcriptome meta-analysis. Proc. Natl Acad. Sci. USA, 103, 6368-6373.
42. Neapolitan,R. (2004) Learning Bayesian Networks. Prentice Hall, Chicago, IL.
43. Pavlidis,P. et al. (2002) Learning gene functional classifications from multiple data types. J. Comput. Biol., 9, 401-411.
44. Primig,M. et al. (2000) The core meiotic transcriptome in budding yeasts. Nat. Genet., 26, 415-423.
45. Pringle,J. et al. (1997) Saccharomyces: Cell cycle and cell biology. Mol. Cell. Biol. Yeast, in press.
46. Ptashne,M. and Gann,A. (2003) Signal transduction. Imposing specificity on kinases, Science, 299, 1025-1027.
47. Rhodes,D.R. et al. (2004) Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc. Natl Acad. Sci. USA, 101, 9309-9314.
48. Rudra,D. et al. (2005) Central role of Ifh1p-Fh11p interaction in the synthesis of yeast ribosomal proteins. EMBO J., 24, 533-542.
49. Ruepp,A. et al. (2004) The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res., 32, 5539-5545.
50. Saeed,A.I. et al. (2003) TM4: A free, open-source system for microarray data management and analysis. Biotechniques, 34, 374-378.
51. Saldanha,A.J. et al. (2004) Nutritional homeostasis in batch and steady-state culture of yeast. Mol. Biol. Cell, 15, 4089-4104.
52. Sickmann,A. et al. (2003) The proteome of Saccharomyces cerevisiae mitochondria. Proc. Natl Acad. Sci. USA, 100, 13207-13212.
53. Steinmetz,L.M. et al. (2002) Systematic screen for human disease genes in yeast. Nat. Genet., 31, 400-404.
54. Titz,B. et al. (2006) Transcriptional activators in yeast. Nucleic Acids Res., 34, 955-967.
55. Troyanskaya,O. et al. (2001) Missing value estimation methods for DNA microarrays. Bioinformatics, 17, 520-525.
56. Troyanskaya,O.G. et al. (2003) A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl. Acad. Sci. USA, 100, 8348-8353.
57. Ubersax,J.A. et al. (2003) Targets of the cyclin-dependent kinase Cdk1. Nature, 425, 859-864.
58. Williams,R.M. et al. (2002) The Ume6 regulon coordinates metabolic and meiotic gene expression in yeast. Proc. Natl Acad. Sci. USA, 99, 13431-13436.
59. Xiao,W. et al. (1998) Mms4, a putative transcriptional (co)activator, protects Saccharomyces cerevisiae cells from endogenous and environmental DNA damage. Mol. Gen. Genet., 257, 614-623.
60. Yvert,G. et al. (2003) Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat. Genet., 35, 57-64.