S. Anders and W. Huber, Differential expression analysis for sequence count data, Genome Biology, vol.11, issue.R106, pp.1-28, 2010.

P. L. Auer and R. W. Doerge, Statistical design and analysis of RNA-Seq data, Genetics, vol.185, pp.1-12, 2010.

P. L. Auer and R. W. Doerge, A Two-Stage Poisson Model for Testing RNA-Seq Data, Statistical Applications in Genetics and Molecular Biology, vol.10, issue.1, pp.1-26, 2011.
DOI : 10.2202/1544-6115.1627

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000.
DOI : 10.1109/34.865189

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.561-575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

P. G. Bryant, Large-sample results for optimization-based clustering methods, Journal of Classification, vol.4, issue.4, pp.31-44, 1991.
DOI : 10.1007/BF02616246

J. H. Bullard, E. A. Purdom, K. D. Hansen, and S. Dudoit, Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments, BMC Bioinformatics, vol.11, issue.1, 2010.
DOI : 10.1186/1471-2105-11-94

L. Cai, H. Huang, S. Blackshaw, J. S. Liu, C. Cepko et al., Clustering analysis of SAGE data using a Poisson approach, Genome Biology, vol.5, issue.7, p.51, 2004.
DOI : 10.1186/gb-2004-5-7-r51

T. Cali?ski and J. Harabasz, A dendrite method for cluster analysis, Communications in Statistics - Theory and Methods, vol.3, issue.1, pp.1-27, 1974.
DOI : 10.1080/03610927408827101

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992.
DOI : 10.1016/0167-9473(92)90042-E

URL : https://hal.archives-ouvertes.fr/inria-00075196

A. P. Dempster, N. M. Laird, R. , and D. B. , Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B (Methodological), vol.39, issue.1, pp.1-38, 1977.

M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Sciences, vol.95, issue.25, pp.95-14863, 1998.
DOI : 10.1073/pnas.95.25.14863

P. Engström, D. Tommei, S. Stricker, A. Smith, S. Pollard et al., Transcriptional characterization of glioblastoma stem cell lines using tag sequencing, 2010.

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/BF01908075

D. Jiang, C. Tang, and A. Zhang, Cluster analysis for gene expression data: a survey, IEEE Transactions on Knowledge and Data Engineering, vol.16, issue.11, pp.1370-1386, 2004.
DOI : 10.1109/TKDE.2004.68

D. Karlis, An EM algorithm for multivariate Poisson distribution and related models, Journal of Applied Statistics, vol.30, issue.1, pp.63-77, 2003.
DOI : 10.1080/0266476022000018510

K. Kim, S. Zhang, K. Jiang, L. Cai, I. Lee et al., Measuring similarities between gene expression profiles through new data transformations, BMC Bioinformatics, vol.8, issue.29, 2007.

J. B. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

G. Mclachlan, K. Do, and C. Ambroise, Analyzing Microarray Gene Expression Data, 2004.
DOI : 10.1002/047172842X

A. Mortazavi, B. Williams, K. Mccue, L. Schaeffer, and B. Wold, Mapping and quantifying mammalian transcriptomes by RNA-Seq, Nature Methods, vol.14, issue.7, pp.621-628, 2008.
DOI : 10.1038/nmeth.1226

A. Oshlack and M. J. Wakefield, Transcript length bias in RNA-seq data confounds systems biology, Biology Direct, vol.4, issue.1, 2009.
DOI : 10.1186/1745-6150-4-14

M. D. Robinson and A. Oshlack, A scaling normalization method for differential expression analysis of RNA-seq data, Genome Biology, vol.11, issue.3, p.11, 2010.
DOI : 10.1186/gb-2010-11-3-r25

M. D. Robinson and G. K. Smyth, Moderated statistical tests for assessing differences in tag abundance, Bioinformatics, vol.23, issue.21, pp.2881-2887, 2007.
DOI : 10.1093/bioinformatics/btm453

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

A. J. Severin, J. L. Woody, Y. Bolon, B. Joseph, B. W. Diers et al., RNA-Seq Atlas of Glycine max: A guide to the soybean transcriptome, BMC Plant Biology, vol.10, issue.1, p.10, 2010.
DOI : 10.1186/1471-2229-10-160

Y. Si, P. Liu, P. Li, and T. Brutnell, Model-based clustering for RNA-seq data, Bioinformatics, vol.30, issue.2, 2011.
DOI : 10.1093/bioinformatics/btt632

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.
DOI : 10.1111/1467-9868.00293

C. Trapnell, B. A. Williams, G. Pertea, A. Mortazavi, G. Kwan et al., Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation, Nature Biotechnology, vol.25, issue.5, pp.511-518, 2010.
DOI : 10.1038/nbt.1621

J. H. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963.
DOI : 10.1007/BF02289263

D. M. Witten, Classification and clustering of sequencing data using a Poisson model, The Annals of Applied Statistics, vol.5, issue.4, 2011.
DOI : 10.1214/11-AOAS493

K. Y. Yeung, C. Fraley, A. Murua, A. E. Raftery, R. et al., Model-based clustering and data transformations for gene expression data, Bioinformatics, vol.17, issue.10, pp.17-977, 2001.
DOI : 10.1093/bioinformatics/17.10.977