Direct functional assessment of the composite phenotype through multivariate projection strategies

Genomics

Volumn 92, Issue 6, 2008, Pages 373-383

  Conesa, Ana ^a   Bro, Rasmus ^b   García García, Francisco ^a   Prats, José Manuel ^c   Götz, Stefan ^a,d   Kjeldahl, Karin ^b   Montaner, David ^a   Dopazo, Joaquín ^a,d,e  

^a CENTRO DE INVESTIGACIÓN PRÍNCIPE FELIPE   (Spain)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

^c UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^d CENTRO DE INVESTIGACIÓN BIOMÉDICA EN RED DE ENFERMEDADES RARAS CIBERER   (Spain)

^e Functional Genomics Node (National Institute for Bioinformatics INB)   (Spain)

Author keywords

Data integration; Functional genomics; Gene annotation; Gene ontology; Multivariate regression; Partial least squares; Phenotype; Principal component analysis

Indexed keywords

ARTICLE; BREAST CANCER; CONTROLLED STUDY; FUNCTIONAL ASSESSMENT; GENE EXPRESSION; GENE FUNCTION; GENETIC ANALYSIS; PHENOTYPE; PRIORITY JOURNAL; TRANSCRIPTOMICS;

BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; FEMALE; GENE EXPRESSION PROFILING; HUMANS; MATHEMATICAL COMPUTING; MULTIVARIATE ANALYSIS; PHENOTYPE;

EID: 55949133386     PISSN: 08887543     EISSN: 10898646     Source Type: Journal    
DOI: 10.1016/j.ygeno.2008.05.015     Document Type: Article

References (39)

1. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98 (2001) 5116-5121
2. Kerr M.K., Martin M., and Churchill G.A. Analysis of variance for gene expression microarray data. J. Comput. Biol. 7 (2000) 819-837
3. Smyth G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3 (2004) 3
4. Speed T. Statistical Analysis of Gene Expression Microarray Data (2003), Chapman and Hall/CRC, London
5. Al-Shahrour F., Diaz-Uriarte R., and Dopazo J. Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics 21 (2005) 2988-2993
6. Dopazo J. Functional interpretation of microarray experiments. Omics 10 (2006) 398-410
7. Rivals I., Personnaz L., Taing L., and Potier M.C. Enrichment or depletion of a GO category within a class of genes: which test?. Bioinformatics 23 (2007) 401-407
8. Pounds S.B. Estimation and control of multiple testing error rates for microarray studies. Brief Bioinform. 7 (2006) 25-36
9. Jackson J.E. A User's Guide to Principal Components (1991), Wiley, New York
10. Smilde A., Bro R., and Geladi P. Multiway Analysis: Applications to the Chemical Sciences (2004), Wiley, Chichester, England
11. Kustra R., Shioda R., and Zhu M. A factor analysis model for functional genomics. BMC Bioinform. 7 (2006) 216
12. Carmona-Saez P., Chagoyen M., Tirado F F., Carazo J.M., and Pascual-Montano A. GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol. 8 (2007) R3
13. Lu Y., Liu P.Y., Xiao P., and Deng H.W. Hotelling's T2 multivariate profiling for detecting differential expression in microarrays. Bioinformatics 21 (2005) 3105-3113
14. Landgrebe J., Wolfgang W., and Welzl G. Permutation-validated principal components analysis of microarray data. Genome Biol. 3 (2002) 19.1-19.1
15. Nueda M.J., Conesa A., Westerhuis J.A., Hoefsloot H.C., Smilde A.K., Talón M., and Ferrer A. Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA. Bioinformatics 23 (2007) 1792-1800
16. Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A., Paulovich A., Pomeroy S.L., Golub T.R., Lander E.S., and Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102 (2005) 15545-15550
17. Al-Shahrour F., Arbiza L., Dopazo H., Huerta-Cepas J., Minguez P., Montaner D., and Dopazo J. From genes to functional classes in the study of biological systems. BMC Bioinform. 8 (2007) 114
18. Kong S.W., Pu W.T., and Park P.J. A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics 22 (2006) 2373-2380
19. Nettleton D., and Recknor J. J.M. Reecy. Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis. Bioinformatics 24 (2008) 192-201
20. Mielke Jr. P., and Berry K. Permutation methods: a distance function approach (2001), Springer-Verlag, New York
21. Lee H.K., Hsu A.K., Sajdak J., Qin J., and Pavlidis P. Coexpression analysis of human genes across many microarray data sets. BMC Bioinformatics 25 5 (2004) 18
22. Allocco D.J., Kohane I.S., and Butte A.J. Quantifying the relationship between co-expression, co-regulation and gene function. Genome Res. 14 (2004) 1085-1094
23. Luo F., Yang Y., Zhong J., Gao H., Khan L., Thompson D.K., and Zhou J. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinform 8 (2007) 299
24. Murali T.M. C.J. Wu. S. Kasif. The art of gene function prediction. Nat. Biotechnol. 24 (2006) 1474-1475
25. Khor T.O. Toxicogenomics in drug discovery and drug development: potential applications and future challenges. Pharm. Res. 23 (2006) 1659-1664
26. Clarke R., Ressom H.W., Wang A., Xuan J., Liu M.C., Gehan E.A., and Wang Y. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat. Rev. Cancer 8 (2008) 37-49
27. Heijne W.H., Stierum R.H., Slijper M., van Bladeren P.J., and van Ommen B. Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach. Biochem. Pharmacol. 65 (2003) 857-875
28. Miller L.D., Smeds J., George J., Vega V.B., Vergara L., Ploner A., Pawitan Y., Hall P., Klaar S., Liu E.T., and Bergh J. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc. Natl. Acad. Sci. USA 102 (2005) 13550-13555
29. Cattoretti G., Rilke F., Andreola S., D'Amato L., and Delia D. P53 expression in breast cancer. Int. J. Cancer 41 (1988) 178-183
30. Blüthgen N., Brand K., Cajavec B., Swat M., Herzel H., and Beule D. Biological profiling of gene groups utilizing Gene Ontology. Genome Inform. 16 (2005) 106-115
31. Troyanskaya O., Cantor M., Sherlock G., Brown P., Hastie T., Tibshirani R., Botstein D., and Altman R.B. Missing Value estimation methods for DNA microarrays. Bioinformatics 17 (2001) 520-525
32. Fagan A., Culhane A.C., and Higgins D.G. A multivariate analysis approach to the integration of proteomic and gene expression data. Proteomics 7 (2007) 2162-2171
33. Wei P., and Pan W. Incorporating gene networks into statistical tests for genomics data via spatially correlated mixture model. Bioinformatics 24 (2008) 404-411
34. Matsui S., Ito M., Nishiyama H., Uno H., Kotani H., Watanabe J., Guilford P., Reeve A., Fukushima M., and Ogawa O. Genomic characterization of multiple clinical phenotypes of cancer using multivariate linear regression models. Bioinformatics 15 (2007) 732-738
35. L. Eriksson, E. Johansson, N. Kettaneh-Wold, S. Wold, Multi- and Megavariate Data Analysis, UMETRICS AB, Umea, 2001.
36. Fellenberg K., Busold C.H., Witt O., Bauer A., Beckmann B., Hauser N.C., Frohme M., Winter S., and Dippon J. J.D. Hoheisel. Systematic interpretation of microarray data using experiment annotations. BMC Genomics 7 (2006) 319
37. Conesa A., Nueda M.J., Ferrer A., and Talon M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22 (2006) 1096-1102
38. Conesa A., Gotz S., Garcia-Gomez J.M., Terol J., Talon M., and Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21 (2005) 3674-3676
39. Mevik B.H., and Wehrens R. The pls Package: Principal Component and Partial Least Squares Regression in R. J. Stat. Software 18 (2007) 1-24