C. Carvalho, J. Chang, J. Lucas, J. Nevins, Q. Wang et al., High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics, Journal of the American Statistical Association, vol.103, issue.484, pp.1438-1456, 2008.
DOI : 10.1198/016214508000000869

J. Paisley and C. L. , Nonparametric factor analysis with beta process priors, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.777-784, 2009.
DOI : 10.1145/1553374.1553474

B. Chen, M. Chen, J. Paisley, A. Zaas, C. Woods et al., Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies, BMC Bioinformatics, vol.11, issue.1, p.552, 2010.
DOI : 10.1186/1471-2105-11-552

M. West, Bayesian Factor regression models in the " Large p, Small n " paradigm, In Bayesian Statistics, vol.7, pp.723-732, 2003.

K. Yeung and W. Ruzzo, Principal component analysis for clustering gene expression data, Bioinformatics, vol.17, issue.9, pp.763-774, 2001.
DOI : 10.1093/bioinformatics/17.9.763

J. Nascimento and J. Bioucas-dias, Vertex component analysis: a fast algorithm to unmix hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.4, pp.898-910, 2005.
DOI : 10.1109/TGRS.2005.844293

D. Lee and H. Seung, Algorithms for non-negative matrix factorization, Proc Neural Info Process Syst, pp.556-562, 2000.

P. Fogel, S. Young, D. Hawkins, and N. Ledirac, Inferential, robust non-negative matrix factorization analysis of microarray data, Bioinformatics, vol.23, issue.1, pp.44-49, 2007.
DOI : 10.1093/bioinformatics/btl550

G. Mclachlan, R. Bean, and D. Peel, A mixture model-based approach to the clustering of microarray expression data, Bioinformatics, vol.18, issue.3, pp.413-422, 2002.
DOI : 10.1093/bioinformatics/18.3.413

J. Baek and G. Mclachlan, Mixtures of common t-factor analyzers for clustering high-dimensional microarray data, Bioinformatics, vol.27, issue.9, pp.1269-1276, 2011.
DOI : 10.1093/bioinformatics/btr112

T. Moloshok, R. Klevecz, J. Grant, F. Manion, W. Speier et al., Application of Bayesian Decomposition for analysing microarray data, Bioinformatics, vol.18, issue.4, pp.566-575, 2002.
DOI : 10.1093/bioinformatics/18.4.566

E. Fertig, J. Ding, A. Favorov, G. Parmigiani, and M. Ochs, CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data, Bioinformatics, vol.26, issue.21, pp.2792-2793, 2010.
DOI : 10.1093/bioinformatics/btq503

N. Dobigeon, S. Moussaoui, M. Coulon, J. Tourneret, and A. Hero, Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery, IEEE Transactions on Signal Processing, vol.57, issue.11, pp.4355-4368, 2009.
DOI : 10.1109/TSP.2009.2025797

URL : https://hal.archives-ouvertes.fr/hal-00548758

Y. Huang, A. Zaas, A. Rao, N. Dobigeon, P. Woolf et al., Temporal Dynamics of Host Molecular Responses Differentiate Symptomatic and Asymptomatic Influenza A Infection, PLoS Genetics, vol.7, issue.8, p.1002234, 2011.
DOI : 10.1371/journal.pgen.1002234.s025

A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis, 2001.

D. Dueck, Q. Morris, and B. Frey, Multi-way clustering of microarray data using probabilistic sparse matrix factorization, Bioinformatics, vol.21, issue.Suppl 1, pp.144-151, 2005.
DOI : 10.1093/bioinformatics/bti1041

V. Nikulin, T. Huang, S. Ng, S. Rathnayake, and G. Mclachlan, A very fast algorithm for matrix factorization, Statistics & Probability Letters, vol.81, issue.7, pp.773-782, 2011.
DOI : 10.1016/j.spl.2011.02.001

A. Zaas, M. Chen, J. Varkey, T. Veldman, A. Hero et al., Gene Expression Signatures Diagnose Influenza and Other Symptomatic Respiratory Viral Infections in Humans, Cell Host & Microbe, vol.6, issue.3, pp.207-217, 2009.
DOI : 10.1016/j.chom.2009.07.006

M. Winter, <title>N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data</title>, Imaging Spectrometry V, pp.266-275, 1999.
DOI : 10.1117/12.366289

W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. London: Chapman and Hall, 1996.

P. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

R. Duda, P. Hart, and D. Stork, Pattern Classification, 2000.

T. Cox and M. Cox, Multidimensional Scaling. London: Chapman and Hall, 1994.

C. Robert and G. Casella, Monte Carlo Statistical Methods. 1edition, 1999.

N. Dobigeon, J. Tourneret, and C. Chang, Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery, IEEE Transactions on Signal Processing, vol.56, issue.7, pp.2684-2695, 2008.
DOI : 10.1109/TSP.2008.917851

URL : https://hal.archives-ouvertes.fr/hal-00474880