Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments

Statistical Modelling

Volumn 5, Issue 3, 2005, Pages 243-267

  Celeux, Gilles ^a   Martin, Olivier ^b   Lavergne, Christian ^c  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b CEMAGREF   (France)

^c UNIVERSITÉ DE MONTPELLIER   (France)

Author keywords

Cluster analysis; Gene expression profile; Linear model; Mixture model; Penalized likelihood criteria; Random effect

Indexed keywords

EID: 26844458204     PISSN: 1471082X     EISSN: None     Source Type: Journal    
DOI: 10.1191/1471082X05st096oa     Document Type: Article

References (29)

1. Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716-23.
2. Berchtold A (2004) Optimisation of mixture models: Comparison of different strategies. Computational Statistics 19, 385-406.
3. Biernacki C, Celeux G and Govaert G (2000) Assessing a mixture model for clustering with the integrated completed likelihood. IEEE, Trans. on PAMI 22, 719-25.
4. Biernacki C, Celeux G and Govaert G (2003) Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate gaussian mixture models. Computational Statistics & Data Analysis 41, 561-75.
5. Birgé L and Massart P (2001) Gaussian model selection. Journal of European Mathematical Society 3, 203-68.
6. Celeux G, Lavergne C and Martin O (2002) Mixture of linear mixed models - application to repeated data clustering. Inria Research Report 4566.
7. Dempster AP, Laird NM and Rubin DB (1977) Maximum likelihood for incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society B. 39, 1-38.
8. Duggan D, Bitnner M, Chen Y, Meltzer P and Trent J (1999) Expression profiling using cDNA microarrays. Nature Genetics Supplement 21, 10-14.
9. Efron B, Tibshirani R, Goss V and Chu G (2000) Microarrays and their use in a comparative experiment. Dept Statistics, Stanford Univ, Nov 2000.
10. Eisen M, Spellman P, Brown P and Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences 95, 14863-68.
11. Fraley C and Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Computer Journal 41, 578-88.
12. Ghosh D and Chinnaiyan A (2002) Mixture modelling of gene expression data from microarray experiments. Bioinformatics 18, 275-86.
13. Hertzberg M, Aspeborg H, Schrader J, Andersson A, Erlandsson R, Blomqvist K, Bhalerao R, Uhlen M, Teeri T, Lundeberg J, Sundberg B, Nilsson P and Sandberg G (2001) A transcriptional roadmap to wood formation. Proceedings of the National Academy of Sciences 98(25), 14732-37.
14. Kass RE and Raftery AE (1995) Bayes factors. Journal of the American Statistical Association 90, 733-95.
15. Lee M, Kuo F, Whitmore G and Sklar J (2000) Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations. Proceedings of the National Academy of Sciences 97, 9834-39.
16. McLachlan G and Krishnan T (1997) The EM algorithm and extensions. New York: Wiley.
17. McLachlan G and Peel D (2000) Finite mixture models. New York: Wiley.
18. Roeder K and Wasserman L (1997) Practical Bayesian density estimation using mixtures of normals. Journal of the American Statistical Association 92, 894-902.
19. Schwarz G (1978) Estimating the dimension of a model. Annals of Statistics 6, 461-64.
20. Searle S, Casella G and McCulloch C (1992) Variance components. New York: Wiley.
21. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E and Golub T (1999) Interpreting patterns of gene expresion with self-organizing maps: methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences 96, 2907-12.
22. Tavazoie S, Hughes J, Campbell M, Cho R and Church G (1999) Systematic determination of genetic network architecture. Nature Genetics 22, 281-85.
23. Trottier C (1998) Estimation dans les modèles linéaires généralisés à effets aléatoires, PhD thesis, Institut National de Polytechnique de Grenoble.
24. Vaida F and Blanchard S (2003) Conditional Akaike information for mixed Effects models. Dept of Biostatistics, Harvard School of Public Health, 2003.
25. Ward J (1963) Hierarchical groupings to optimize an objective function. Journal of the American Statistical Association 58, 234-44.
26. Wolfinger R, Gibson G, Wolfinger E, Bennett L, Hamadeh H, Bushel P, Afshari C and Paules R (2001) Assessing gene signifiance from cDNA microarray expression data via mixed models. Journal of Computational Biology 8, 625-37.
27. Yang Y, Dudoit S, Luu P and Speed T (2001) Normalization for cDNA microarray data. In Bittner M, Chen Y, Dorsel A and Dougherty E eds. Microarrays: Optical technologies and informatics, Proceedings of SPIE, vol. 4266, 141-52.
28. Yeung K, Fraley C, Murua A, Raftery A and Ruzzo W (2001) Model-based clustering and data transformations for gene expression data. Bioinformatics 17, 977-87.
29. Yeung K, Medvedovic M and Bumgarner R (2003) Clustering gene-expression data with repeated measurements. Genome Biology 4, R34.