A mixture model-based cluster analysis of DNA microarray gene expression data on Brahman and Brahman composite steers fed high-, medium-, and low-quality diets

Journal of Animal Science

Volumn 81, Issue 8, 2003, Pages 1900-1910

  Reverter, A ^a   Byrne, K A ^a   Bruce, H L ^a   Wang, Y H ^a   Dalrymple, B P ^a   Lehnert, S A ^a  

^a CSIRO   (Australia)

Author keywords

Beef; Complementary DNA; Gene Expression; Maximum Likelihood; Statistical Analysis

Indexed keywords

ANIMALIA; BOS TAURUS;

ANIMAL; ANIMAL FOOD; ARTICLE; BIOLOGICAL MODEL; CALORIC INTAKE; CATTLE; CLUSTER ANALYSIS; DNA MICROARRAY; GENE EXPRESSION REGULATION; GENE LIBRARY; GENETICS; GROWTH, DEVELOPMENT AND AGING; MALE; PROTEIN INTAKE; RANDOMIZATION; SKELETAL MUSCLE; STANDARD; STATISTICAL MODEL; STATISTICS;

ANIMAL FEED; ANIMAL NUTRITION PHYSIOLOGY; ANIMALS; CATTLE; CLUSTER ANALYSIS; DIETARY PROTEINS; ENERGY INTAKE; GENE EXPRESSION REGULATION; GENE LIBRARY; LIKELIHOOD FUNCTIONS; MALE; MODELS, GENETIC; MUSCLE, SKELETAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; RANDOM ALLOCATION;

EID: 0642279219     PISSN: 00218812     EISSN: None     Source Type: Journal    
DOI: 10.2527/2003.8181900x     Document Type: Article

References (31)

1. Akaike, H. 1969. Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 21:243-247.
2. Box, G. E. P., and D. R. Cox. 1964. An analysis of transformations (with discussion). J. Royal Stat. Soc. 26:211-252.
3. Chu, T.-M., B. Weir, and R. Wolfinger. 2002. A systematic statistical linear modeling approach to oligonucleotide array experiments. Math. Biosci. 176:35-51.
4. Durbin, B. P., J. S. Hardin, D. M. Hawkins, and D. M. Rocke. 2002. A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics (Oxf.) 18(Suppl. 1):S105-S110.
5. Efron, B., R. Tibshirani, J. D. Storey, and V. Tusher. 2001. Empirical Bayes analysis of a microarray experiment. J. Am. Stat. Assoc. 96:1151-1160.
6. Eisen E., P. Spellman, P. Brown, and D. Botstein. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95:14863-14868.
7. Fraley, C., and A. E. Raftery. 1998. How many clusters? Which clustering methods? Answers via model-based cluster analysis. Computer J. 41:578-588.
8. Groeneveld, E., and L. A. Garcia-Cortes. 1998. VCE 4.0, a (co)variance components package for frequentists and Bayesians. Proc. 6th World Cong. Genet. Appl. Livest. Prod., Armidale, NSW, Australia 27:455-458.
9. Johnson, R. A., and D. W. Wichern. 1992. Pages 164-166 in Applied Multivariate Statistical Analysis. 3rd ed. Prentice Hall, Englewood Cliffs, NJ.
10. Kerr, M. K., and G. A. Churchill. 2001. Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments. Proc. Nat. Acad. Sci. 98:8961-8965.
11. Lee, S. H., and K. L. Hossner. 2002. Coordinate regulation of ovine adipose tissue gene expression by propionate. J. Anim. Sci. 80:2840-2849.
12. Lönnstedt, I., and T. Speed. 2002. Replicated microarray data. Statistica Sinica 12:31-46.
13. McLachlan, G. J. 1987. On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Appl. Stat. 36:318-324.
14. McLachlan, G. J., D. Peel, K. E. Basford, and P. Adams. 1999. The EMMIX software for the fitting of mixtures of normal and t-components. J. Stat. Software 4:2. Available: http://www.jstat-soft.org/v04/i02. Accessed Dec. 18, 2002.
15. Moody, D. E. 2001. Genomic techniques: An overview of methods for the study of gene expression. J. Anim. Sci. 79(E. Suppl.):128-135.
16. Pan, W., L. Jizhen, and C. T. Le. 2001. A Mixture Model Approach to Detecting Differentially Expressed Genes with Microarray Data. Tech. Report 2001-001. Div. Biostatistics, Univ. Minnesota, St. Paul. Available: http://www.biostat.umn.edu/~weip. Accessed Dec. 18, 2002.
17. Pan, W. 2002. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics (Oxf.) 18:546-554.
18. Pan, W., J. Lin, and C. T. Le. 2002a. Model-based cluster analysis of microarray gene-expression data. Genome Biol. 3:research0009.1-0009.8.
19. Pan, W., J. Lin, and C. T. Le. 2002b. How many replicates of array are required to detect gene expression changes in microarray experiments? A mixture model approach. Genome Biol. 3:research0022.1-0022.10.
20. Rekaya, R. 2002. Bayesian mixture models with unknown number of components: Application to power calculation is microarray experiments. Proc. 7th World Cong. Genet. Appl. Livest. Prod., Montpellier, France, CD-ROM Communication No. 16-12.
21. Schwartz, G. 1978. Estimating the dimensions of a model. Ann. Stat. 6:461-464.
22. Tamayo, P., D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E. S. Lander, and T. R. Golub. 1999. Interpreting patterns of gene expression with self-organizing maps: Methods and applications to hematopietic differentiation. Proc. Natl. Acad. Sci. 96:2907-2912.
23. Tavazoie, S., J. D. Hughes, M. J. Campbell, R. J. Cho, and G. M. Church. 1999. Systematic determination of genetic network architecture. Nat. Genet. 22:281-285.
24. Thomas, J. G., J. M. Olson, S. J. Tapscott, and L. P. Zhao. 2001. An efficient and robust statistical modelling approach to discover differentially expressed genes using genomic expression profiles. Genome Res. 11:1227-1236.
25. Tibshirani, R., T. Hastie, M. Eisen, G. Ross, D. Botstein, and P. Brown. 1999. Clustering methods for the analysis of DNA microarray data. Tech. Report, Stanford Univ., Stanford, CA. Available: http://www-stat.stanford.edu/~tibs/ research.html. Accessed Dec. 18, 2002.
26. Tran, P. H., D. A. Peiffer, Y. Shin, L. M. Meek, J. P. Brody, and K. W. Y. Cho. 2002. Microarray optimizations: Increasing spot accuracy and automated identification of true microarray signals. Nucleic Acids Res. 30:12e54.
27. Tusher, V. G., R. Tibshirani, and G. Chu. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. 98:5116-5121.
28. Welch, B. L. 1951. On the comparison of several mean values: An alternative approach. Biometrika 38:330-336.
29. Wolfinger, R. D., G. Gibson, E. D. Wolfinger, L. Bennett, H. Hamadeh, P. Bushel, C. Afshari, and R. S. Paules. 2001. Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8:625-637.
30. Workman, C., J. J. Jensen, H. Jarmer, R. Berka, L. Gautier, H. B. Nielsen, H.-H. Saxild, C. Nielsen, S. Brunak, and S. Knudsen. 2002. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol. 3:research0048.1-0048.16.
31. Yeung, K. Y., C. Fraley, A. Murua, A. E. Raftery, and W. L. Ruzzo. 2001. Model-based clustering and data transformations for gene expression data. Bioinformatics (Oxf.) 17:977-987.