A. Azzalini and A. D. Valle, The multivariate skew-normal distribution, Biometrika, vol.83, pp.715-726, 1996.
DOI : 10.1017/cbo9781139248891.006

A. Azzalini, R. P. Browne, M. G. Genton, and P. D. Mcnicholas, On nomenclature for, and the relative merits of, two formulations of skew distributions, Statistics and Probability Letters, vol.110, pp.201-206, 2016.

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, pp.719-725, 2000.

D. A. Binder, Bayesian Cluster Analysis, Biometrika, vol.65, pp.31-38, 1978.

D. A. Binder, Approximations to Bayesian Clustering Rules, Biometrika, vol.68, pp.275-285, 1981.
DOI : 10.2307/2335828

R. R. Brinkman, M. Gasparetto, S. J. Lee, A. J. Ribickas, J. Perkins et al., High-content flow cytometry and temporal data analysis for defining a cellular signature of graft-versus-host disease. Biology of blood and marrow transplantation : journal of the American Society for, Blood and Marrow Transplantation, vol.13, pp.691-700, 2007.

F. Caron, Y. W. Teh, and T. B. Murphy, Bayesian nonparametric PlackettLuce models for the analysis of preferences for college degree programmes, The Annals of Applied Statistics, vol.8, pp.1145-1181, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00755478

F. Caron, M. Davy, A. Doucet, E. Duflos, and P. Vanheeghe, Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures, IEEE Transactions on Signal Processing, vol.56, pp.71-84, 2008.
DOI : 10.1109/tsp.2007.900167
URL : https://hal.archives-ouvertes.fr/inria-00129646

F. Caron, W. Neiswanger, F. Wood, A. Doucet, and M. Davy, Generalized Pólya Urn for Time-Varying Pitman-Yor Processes, Journal of Machine Learning Research, vol.18, pp.1-32, 2017.

C. Chan, F. Feng, J. Ottinger, D. Foster, M. West et al., Statistical mixture modeling for cell subtype identification in flow cytometry, Cytometry. Part A : the journal of the International Society for Analytical Cytology, vol.73, pp.693-701, 2008.

A. Cron, C. Gouttefangeas, J. Frelinger, L. Lin, S. K. Singh et al., Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples, PLoS computational biology, vol.9, p.1003130, 2013.
DOI : 10.1371/journal.pcbi.1003130
URL : https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003130&type=printable

D. B. Dahl, Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model, vol.10, pp.201-218, 2006.
DOI : 10.1017/cbo9780511584589.011

M. Dundar, F. Akova, H. Z. Yerebakan, and B. Rajwa, A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects, BMC Bioinformatics, vol.15, p.314, 2014.

M. D. Escobar and M. West, Bayesian Density Estimation and Inference Using Mixtures, Journal of the American Statistical Association, vol.90, pp.577-588, 1995.
DOI : 10.1080/01621459.1995.10476550

T. S. Ferguson, A Bayesian analysis of some nonparametric problems. The Annals of Statistics, vol.1, pp.209-230, 1973.

G. Finak, A. Bashashati, R. Brinkman, and R. Gottardo, Merging mixture components for cell population identification in flow cytometry, Advances in bioinformatics, 2009.
DOI : 10.1155/2009/247646
URL : http://downloads.hindawi.com/journals/abi/2009/247646.pdf

G. Finak, J. Perez, A. Weng, and R. Gottardo, Optimizing transformations for automated, high throughput analysis of flow cytometry data, BMC Bioinformatics, vol.11, p.546, 2010.

A. Fritsch and K. Ickstadt, Improved criteria for clustering based on the B.P. HEJBLUM ET AL. posterior similarity matrix, Bayesian Analysis, vol.4, pp.367-392, 2009.

S. Frühwirth-schnatter and S. Pyne, Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions, Biostatistics, vol.11, pp.317-353, 2010.

Y. Ge and S. C. Sealfon, flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding, Bioinformatics, vol.28, pp.2052-2058, 2012.
DOI : 10.1093/bioinformatics/bts300
URL : https://academic.oup.com/bioinformatics/article-pdf/28/15/2052/646633/bts300.pdf

F. Gondois-rey, S. Granjeaud, P. Rouillier, C. Rioualen, G. Bidaut et al., Multi-parametric cytometry from a complex cellular sample: Improvements and limits of manual versus computational-based interactive analyses. Cytometry Part A, vol.89, pp.480-490, 2016.

B. P. Hejblum, C. Alkhassim, R. Gottardo, F. Caron, and R. Thiébaut, Supplement to "Sequential Dirichlet process mixtures of multivariate skew tdistributions for model-based clustering of flow cytometry data, 2018.

Z. Huang and A. Gelman, Sampling for Bayesian Computation with Large Datasets, SSRN Electronic Journal, pp.1-21, 2005.
DOI : 10.2139/ssrn.1010107
URL : http://www.stat.columbia.edu/~gelman/research/unpublished/comp7.pdf

A. Huang and M. P. Wand, Simple Marginally Noninformative Prior Distributions for Covariance Matrices, Bayesian Analysis, vol.8, pp.439-452, 2013.
DOI : 10.1214/13-ba815
URL : https://doi.org/10.1214/13-ba815

A. Jasra, C. C. Holmes, and D. A. Stephens, Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling, Statistical Science, vol.20, pp.50-67, 2005.

K. Johnsson, J. Wallin, and M. Fontes, BayesFlow: latent modeling of flow cytometry cell populations, BMC Bioinformatics, vol.17, p.25, 2016.

M. A. Juárez and M. F. Steel, Model-Based Clustering of Non-Gaussian Panel Data Based on Skew-t Distributions, Journal of Business & Economic Statistics, vol.28, pp.52-66, 2010.

M. Kalli, J. E. Griffin, and S. G. Walker, Slice sampling mixture models, Statistics and Computing, vol.21, pp.93-105, 2011.

D. C. Kessler, P. D. Hoff, and D. B. Dunson, Marginally specified priors for non-parametric bayesian estimation, Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol.77, pp.35-58, 2015.

A. Larbi and T. Fulop, From "truly na¨?vena¨?ve" to "exhausted senescent" T cells: When markers predict functionality, Cytometry Part A, vol.85, pp.25-35, 2014.

J. W. Lau and P. J. Green, Bayesian Model-Based Clustering Procedures, Journal of Computational and Graphical Statistics, vol.16, pp.526-558, 2007.

S. X. Lee and G. J. Mclachlan, On mixtures of skew normal and skew tdistributions, Advances in Data Analysis and Classification, vol.7, pp.241-266, 2013.

S. X. Lee and G. J. Mclachlan, Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models, Statistics and Computing, vol.26, pp.573-589, 2016.

Y. Lévy, R. Thiébaut, M. Gougeon, J. Molina, L. Weiss et al., Effect of intermittent interleukin-2 therapy on CD4+ T-cell counts following antiretroviral cessation in patients with HIV, AIDS, vol.26, pp.711-720, 2012.

Y. Lévy, R. Thiébaut, M. Montes, C. Lacabaratz, L. Sloan et al., Dendritic cell-based therapeutic vaccine elicits polyfunctional HIV-specific T-cell immunity associated with control of viral load, European journal of immunology, vol.44, pp.2802-2810, 2014.

L. Lin, C. Chan, S. R. Hadrup, T. M. Froesig, Q. Wang et al., , 2013.

, Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies, Statistical Applications in Genetics and Molecular Biology, vol.12, pp.309-331

A. Y. Lo, On a class of Bayesian nonparametric estimates: I. Density estimates, The Annals of Statistics, vol.12, pp.351-357, 1984.

K. Lo, R. R. Brinkman, and R. Gottardo, Automated gating of flow cytometry data via robust model-based clustering, Cytometry. Part A : the journal of the International Society for Analytical Cytology, vol.73, pp.321-332, 2008.

K. Lo and R. Gottardo, Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: An alternative to the skew-t distribution, Statistics and Computing, vol.22, pp.33-52, 2012.

G. J. Mclachlan, S. X. Lee, R. Azzalini, M. Browne, P. Genton et al., Comment on On nomenclature, and the relative merits of two formulations of skew distributions by A, Statistics & Probability Letters, vol.116, pp.1-5, 2016.

M. Medvedovic and S. Sivaganesan, Bayesian infinite mixture model based clustering of gene expression profiles, Bioinformatics, vol.18, pp.1194-1206, 2002.

R. Melchiotti, F. Gracio, S. Kordasti, A. K. Todd, and E. De-rinaldis, Cluster stability in the analysis of mass cytometry data, Cytometry Part A, vol.91, pp.73-84, 2017.

T. R. Mosmann, I. Naim, J. Rebhahn, S. Datta, J. S. Cavenaugh et al., SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, Part 2: Biological evaluation, Cytometry Part A, vol.85, pp.422-433, 2014.

P. M. Murray, R. P. Browne, and P. D. Mcnicholas, Mixtures of skew-t factor analyzer, Computational Statistics & Data Analysis, vol.77, pp.326-335, 2014.

I. Naim, S. Datta, J. Rebhahn, J. S. Cavenaugh, T. R. Mosmann et al., SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, Part 1: Algorithm design, Cytometry Part A, vol.85, pp.408-421, 2014.

R. M. Neal, Slice sampling, The Annals of Statistics, vol.31, pp.705-767, 2003.

J. Pitman, Combinatorial Stochastic Processes. Lecture Notes in Mathematics 1875, 2006.

S. Pyne, X. Hu, K. Wang, E. Rossin, T. Lin et al., Automated high-dimensional flow cytometric data analysis, Proceedings of the National Academy of Sciences of the United States of America, vol.106, pp.8519-8524, 2009.

Y. Qian, C. Wei, E. Lee, F. Campbell, J. Halliley et al., Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data, Cytometry. Part B, Clinical cytometry, vol.78, pp.69-82, 2010.

J. Sethuraman, A constructive definition of Dirichlet priors, Statistica Sinica, vol.4, pp.639-650, 1994.

I. P. Sugár and S. C. Sealfon, Misty Mountain clustering: application to fast unsupervised flow cytometry gating, BMC Bioinformatics, vol.11, p.502, 2010.

Y. W. Teh, Dirichlet Process, Encyclopedia of Machine Learning 280-287, 2010.

R. Thiébaut, I. Pellegrin, G. Chêne, J. F. Viallard, H. Fleury et al., Immunological markers after long-term treatment interruption in chronically HIV-1 infected patients with CD4 cell count above 400 x 10(6) cells/l, AIDS, vol.19, pp.53-61, 2005.

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, pp.411-423, 2001.

D. A. Van-dyk and X. X. Jiao, Metropolis-Hastings within Partially Collapsed Gibbs Samplers, Journal of Computational and Graphical Statistics, vol.24, pp.301-327, 2015.

D. A. Van-dyk and T. Park, Partially Collapsed Gibbs Samplers, Journal of the American Statistical Association, vol.103, pp.790-796, 2008.

M. J. Welters, C. Gouttefangeas, T. H. Ramwadhdoebe, A. Letsch, C. H. Ottensmeier et al., Harmonization of the intracellular cytokine staining assay, Cancer Immunology, Immunotherapy, vol.61, pp.967-978, 2012.

H. Zare, P. Shooshtari, A. Gupta, and R. R. Brinkman, Data reduction for spectral clustering to analyze high throughput flow cytometry data, BMC Bioinformatics, vol.11, p.403, 2010.

B. France and E. , boris.hejblum@u-bordeaux.fr chariff.alkhassim@u-bordeaux.fr rodolphe.thiebaut@u-bordeaux.fr R. Gottardo Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N., Mail Stop, pp.1-514