E. Amir, K. L. Davis, M. D. Tadmor, E. F. Simonds, J. H. Levine et al., viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nat. Biotechnol, vol.31, issue.6, pp.545-552, 2013.

S. Anders and W. Huber, Differential expression analysis for sequence count data, Genome Biology, vol.11, issue.10, p.106, 2010.

A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh, Clustering with Bregman Divergences, Journal of Machine Learning Research, vol.6, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22

M. Baron, A. Veres, S. L. Wolock, A. L. Faust, R. Gaujoux et al., , 2016.

, A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Interand Intra-cell Population Structure, vol.3, pp.346-360

M. J. Beal and Z. Ghahramani, The variational Bayesian EM algorithm for incomplete data: With application to scoring graphical model structures, Bayesian statistics, vol.7, pp.453-464, 2003.

D. M. Blei, A. Kucukelbir, and J. D. Mcauliffe, Variational inference: A review for statisticians, Journal of the American Statistical Association, 2017.

J. D. Buenrostro, B. Wu, U. M. Litzenburger, D. Ruff, M. L. Gonzales et al., Single-cell chromatin accessibility reveals principles of regulatory variation, Nature, vol.523, issue.7561, pp.486-490, 2015.

F. Buettner, K. N. Natarajan, F. P. Casale, V. Proserpio, A. Scialdone et al., Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells, Nat. Biotechnol, vol.33, issue.2, pp.155-160, 2015.

A. T. Cemgil, Bayesian inference for nonnegative matrix factorisation models, Computational Intelligence and Neuroscience, 2009.
DOI : 10.1155/2009/785152
URL : https://doi.org/10.1155/2009/785152

H. H. Chen, Y. Jin, Y. Huang, C. , and Y. , Detection of high variability in gene expression from single-cell RNA-seq profiling, BMC Genomics, vol.17, issue.7, 2016.

P. Chen, Y. Chen, and M. Rao, Metrics defined by Bregman Divergences, Communications in Mathematical Sciences, vol.6, issue.4, pp.915-926, 2008.
DOI : 10.4310/cms.2008.v6.n4.a6
URL : http://www.intlpress.com/site/pub/files/_fulltext/journals/cms/2008/0006/0004/CMS-2008-0006-0004-a006.pdf

M. Collins, S. Dasgupta, and R. E. Schapire, A generalization of principal components analysis to the exponential family, Advances in Neural Information Processing Systems, pp.617-624, 2001.

Q. Deng, D. Ramsköld, B. Reinius, and R. Sandberg, Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells, Science, vol.343, issue.6167, pp.193-196, 2014.

O. Dikmen and C. Févotte, Maximum marginal likelihood estimation for nonnegative dictionary learning in the Gamma-Poisson model, IEEE Transactions on, vol.60, issue.10, pp.5163-5175, 2012.

C. Eckart and G. Young, The approximation of one matrix by another of lower rank, Psychometrika, vol.1, issue.3, pp.211-218, 1936.

J. Eggers, On Statistical Methods for Zero-Inflated Models, p.9, 2015.

B. E. Engelhardt and R. P. Adams, Bayesian Structured Sparsity from Gaussian Fields, 2014.

C. Févotte and A. T. Cemgil, Nonnegative matrix factorizations as probabilistic inference in composite models, Signal Processing Conference, pp.1913-1917, 2009.

C. Fraley and A. E. Raftery, Model-based clustering, discriminant analysis, and density estimation, Journal of the American statistical Association, vol.97, issue.458, pp.611-631, 2002.
DOI : 10.1198/016214502760047131

S. Freytag, L. Tian, I. Lönnstedt, M. Ng, and M. Bahlo, Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data, 1000.

C. Friguet, Impact de La Dépendance Dans Les Procédures de Tests Multiples En Grande Dimension, 2010.

R. Gaujoux and C. Seoighe, A flexible R package for nonnegative matrix factorization, BMC Bioinformatics, p.367, 2010.
DOI : 10.1186/1471-2105-11-367
URL : https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-11-367

T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, vol.286, issue.5439, pp.531-537, 1999.

D. Grün, A. Lyubimova, L. Kester, K. Wiebrands, O. Basak et al., Single-cell messenger RNA sequencing reveals rare intestinal cell types, Nature, vol.525, issue.7568, p.251, 2015.

M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley, Stochastic Variational Inference, J. Mach. Learn. Res, vol.14, issue.1, pp.1303-1347, 2013.

P. V. Kharchenko, L. Silberstein, and D. T. Scadden, Bayesian approach to single-cell differential expression analysis, Nature Methods, vol.11, issue.7, p.740, 2014.
DOI : 10.1038/nmeth.2967
URL : http://europepmc.org/articles/pmc4112276?pdf=render

J. H. Krijthe, Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation, 2015.

A. J. Landgraf and Y. Lee, Generalized principal component analysis: Projection of saturated model parameters, 2015.

D. D. Lee and H. S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature, vol.401, issue.6755, pp.788-791, 1999.

E. Llorens-bobadilla, S. Zhao, A. Baser, G. Saiz-castro, K. Zwadlo et al., Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury, Cell Stem Cell, vol.17, issue.3, pp.329-340, 2015.

A. Lun and D. Risso, SingleCellExperiment: S4 Classes for Single Cell Data, 2019.

G. Malsiner-walli and H. Wagner, Comparing spike and slab priors for Bayesian variable selection, Austrian Journal of Statistics, vol.40, issue.4, pp.241-264, 2011.
DOI : 10.17713/ajs.v40i4.215
URL : https://doi.org/10.17713/ajs.v40i4.215

T. Minka, Estimating a Dirichlet distribution, 2000.

T. J. Mitchell and J. J. Beauchamp, Bayesian variable selection in linear regression, Journal of the American Statistical Association, vol.83, issue.404, pp.1023-1032, 1988.

F. S. Nathoo, M. L. Lesperance, A. B. Lawson, and C. B. Dean, Comparing variational Bayes with Markov chain Monte Carlo for Bayesian computation in neuroimaging, Statistical methods in medical research, vol.22, issue.4, pp.398-423, 2013.

R. B. O'hara and D. J. Kotze, Do not log-transform count data, Methods in Ecology and Evolution, vol.1, issue.2, pp.118-122, 2010.

K. Pearson, LIII. On lines and planes of closest fit to systems of points in space, Philosophical Magazine Series, vol.6, issue.11, pp.559-572, 1901.

S. Picelli, A. K. Bjorklund, O. R. Faridani, S. Sagasser, G. Winberg et al., Smart-seq2 for sensitive full-length transcriptome profiling in single cells, Nat. Methods, vol.10, issue.11, pp.1096-1098, 2013.
DOI : 10.1038/nmeth.2639

E. Pierson and C. Yau, ZIFA: Dimensionality reduction for zero-inflated singlecell gene expression analysis, Genome Biology, vol.16, p.241, 2015.

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.66, issue.336, pp.846-850, 1971.

J. D. Riggs and T. L. Lalonde, Handbook for Applied Modeling: Non-Gaussian and Correlated Data, 2017.
DOI : 10.1017/9781316544778

D. Risso, F. Perraudeau, S. Gribkova, S. Dudoit, and J. Vert, ZINBWaVE: A general and flexible method for signal extraction from single-cell RNA-seq data. bioRxiv, p.125112, 2017.

A. Rotem, O. Ram, N. Shoresh, R. A. Sperling, A. Goren et al., Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state, Nat. Biotechnol, vol.33, issue.11, pp.1165-1172, 2015.

A. Saliba, A. J. Westermann, S. A. Gorski, and J. Vogel, Single-cell RNAseq: Advances and future challenges, Nucleic Acids Research, vol.42, issue.14, pp.8845-8860, 2014.

Å. Segerstolpe, A. Palasantza, P. Eliasson, E. Andersson, A. Andréasson et al., Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes, Cell Metabolism, vol.24, issue.4, pp.593-607, 2016.

M. Simchowitz, Zero-Inflated Poisson Factorization for Recommendation Systems. Junior Independent Work (advised by D. Blei), 2013.

C. Soneson and M. D. Robinson, Bias, robustness and scalability in single-cell differential expression analysis, Nature Methods, vol.15, issue.4, pp.255-261, 2018.

M. K. Titsias and M. Lázaro-gredilla, Spike and slab variational inference for multi-task and multiple kernel learning, Advances in Neural Information Processing Systems, pp.2339-2347, 2011.

C. Trapnell, D. Cacchiarelli, J. Grimsby, P. Pokharel, S. Li et al., The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells, Nat. Biotechnol, vol.32, issue.4, pp.381-386, 2014.