W. Paxton, R. Coombs, M. Mcelrath, M. Keefer, J. Hughes et al., Longitudinal analysis of quantitative virologic measures in human immunodeficiency virus-infected subjects with > or = 400 CD4 lymphocytes: implications for applying measurements to individual patients. National Institute of Allergy and Infectious Diseases AIDS Vaccine Evaluation Group, J Infect Dis, vol.175, issue.2, pp.247-54, 1997.

D. R. Helsel, More than obvious: Better methods for interpreting nondetect data, Environ Sci Technol, vol.39, issue.20, pp.419-442, 2005.

M. Lee, L. Kong, and L. Weissfeld, Multiple imputation for left-censored biomarker data based on Gibbs sampling method, Stat Med, vol.31, pp.1838-1886, 2012.

D. Greco, M. Pattaro, C. Minelli, C. Thompson, and J. R. , Bayesian analysis of censored response data in family-based genetic association studies, Biom J, vol.58, issue.5, pp.1039-53, 2016.

I. Marschner, R. Betensky, V. Degruttola, S. Hammer, and D. Kuritzkes, Clinical trials using HIV-1 RNA-based primary endpoints: Statistical analysis and potential biase, J Acquir Immune Defic Syndr Hum Retrovirol, vol.20, issue.3, pp.220-227, 1999.

B. W. Gillespie, Q. Chen, H. Reichert, A. Franzblau, E. Hedgeman et al., Estimating population distributions when some data are below a limit of detection by using a reverse Kaplan-Meier estimator, Epidemiology, vol.21, pp.64-70, 2010.

G. Dinse, A. Jusko, L. Ho, K. Annam, B. Graubard et al., Accomodating measurements below a limit of detection: A novel application of Cox regression, Am J Epidemiol, vol.179, issue.8, pp.1018-1042, 2014.

H. J. Wang, Z. Zhu, and J. Zhou, Quantile regression in partially linear varying coefficient models, Ann Stat, vol.37, issue.6B, pp.3841-66, 2009.

P. H. Eilers, E. Röder, H. F. Savelkoul, and R. G. Van-wijk, Quantile regression for the statistical analysis of immunological data with many non-detects, BMC Immunol, vol.13, pp.13-37, 2012.

J. L. Powell, Least absolute deviations estimation for the censored regression model, J Econ, vol.25, pp.303-328, 1984.

J. L. Powell, Censored regression quantiles, J Econom, vol.32, pp.143-55, 1986.

J. Tobin, Estimation of relationships for limited dependent variables, Econometrica, vol.26, pp.24-36, 1958.

J. P. Hughes, Mixed effects models with censored data with application to HIV RNA levels, Biometrics, vol.55, pp.625-634, 1999.

H. Jacqmin-gadda, R. Thiébaut, G. Chêne, and D. Commenges, Analysis of left-censored longitudinal data with application to viral load in HIV infection, Biostatistics, vol.1, issue.4, pp.355-68, 2000.

H. S. Lynn, Maximum likelihood inference for left-censored HIV RNA data, Stat Med, vol.20, pp.33-45, 2001.

L. Nie, H. Chu, C. Liu, S. R. Cole, A. Vexler et al., Linear regression with an independent variable subject to a detection limit, Epidemiology, vol.21, pp.17-24, 2010.

P. Fu, J. Hughes, G. Zeng, S. Hanook, J. Orem et al., A comparative investigation of methods for longitudinal data with limits of detection through a case study, Stat Methods Med Res, vol.25, issue.1, pp.153-66, 2016.

R. E. Wiegand, C. E. Rose, and J. M. Karon, Comparison of models for analyzing two-group, cross-sectional data with a gaussian outcome subject to a detection limit, Stat Methods Med Res, vol.25, issue.6, pp.2733-2782, 2016.

. Bibliographie,

N. Acosta, F. J. Whelan, R. Somayaji, A. Poonja, M. G. Surette et al., The evolving cystic fibrosis microbiome : A comparative cohort study spanning sixteen years

J. Aitchison and J. Bacon-shone, Log contrast models for experiments with mixtures, Biometrika, vol.71, issue.2, p.82, 1984.

J. Aitchison, The statistical analysis of compositional data, vol.64, p.82, 1986.

J. Aitchison and J. W. Kay, Possible solution of some essential zero problems in compositional data analysis, p.67, 2003.

S. Anders, A. Reyes, and W. Huber, Detecting differential usage of exons from rna-seq data, Genome research, vol.22, issue.10, p.75, 2008.

M. J. Anderson, T. O. Crist, J. M. Chase, M. Vellend, B. D. Inouye et al., Navigating the multiple meanings of ? diversity : a roadmap for the practicing ecologist, Ecology letters, vol.14, issue.1, p.74, 2011.

C. Andréjak and L. Delhaes, Le microbiome pulmonaire en 2015-une fenêtre ouverte sur les pathologies pulmonaires chroniques. médecine/sciences, vol.31, p.54, 2015.

L. Assoumou, A. Houssaïna, D. Corstagliola, and P. Flandre, Standardization, and clinical relevance of HIV drug resistance testing project from the forum for collaborative HIV research. Relative contributions of baseline patient characteristics and the choice of statistical methods to the variability of genotypic resistance scores : the example of didanosine, Journal antimicrop chemother, vol.65, issue.4, p.16, 2010.

F. R. Bach, Bolasso : model consistent Lasso estimation through the bootstrap, Proceedings of the 25th International Conference on Machine Learning, ICML '08, vol.15, p.28, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00271289

J. Bacon-shone, Discrete and continuous compositions, p.68, 2008.

Y. Ban, L. An, and H. Jiang, Investigating microbial co-occurrence patterns based on metagenomic compositional data, Bioinformatics, vol.31, issue.20, p.70, 2015.

M. Beaume, T. Köhler, G. Greub, O. Manuel, J. Aubert et al., Rapid adaptation drives invasion of airway donor microbiota by pseudomonas after lung transplantation, Scientific reports, vol.7, p.18, 2017.

N. Beerenwinkel, H. Montazeri, H. Schuhmacher, P. Knupfer, V. Von-wyl et al., The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients, PLOS Computational Biology, vol.9, issue.8, pp.1-11, 2013.

J. M. Bhatt, Treatment of pulmonary exacerbations in cystic fibrosis, European Respiratory Review, vol.22, issue.129, p.19, 2013.

D. Bilton, G. Canny, S. Conway, S. Dumcius, L. Hjelte et al., Pulmonary exacerbation : towards a definition for use in clinical trials. report from the eurocarecf working group on outcome parameters in clinical trials, Journal of Cystic Fibrosis, vol.10, p.19, 2011.

B. M. Bolker, M. E. Brooks, C. J. Clark, S. W. Geange, J. R. Poulsen et al., Generalized linear mixed models : a practical guide for ecology and evolution, Trends in ecology & evolution, vol.24, issue.3, p.78, 2009.

F. Botterel, C. Angebault, O. Cabaret, F. A. Stressmann, J. Costa et al., Fungal and bacterial diversity of airway microbiota in adults with cystic fibrosis : Concordance between conventional methods and ultra-deep sequencing, and their practical use in the clinical laboratory, Mycopathologia, vol.20, p.131, 2017.

S. Boutin and A. H. Dalpke, Acquisition and adaptation of the airway microbiota in the early life of cystic fibrosis patients. Molecular and cellular pediatrics, vol.4, p.18, 2017.

J. R. Bray and C. J. , An ordination of the upland forest communities of southern wisconsin, Ecological monographs, vol.27, issue.4, p.62, 1957.

L. Breiman, Statistical modeling : The two cultures (with comments and a rejoinder by the author), Statistical science, vol.16, issue.3, p.14, 2001.

F. Brun-vezinet, D. Costagliola, M. A. Khaled, V. Calvez, F. Clavel et al., Clinically validated genotype analysis : guiding principles and statistical concerns, Antiviral therapy, vol.9, issue.4, p.35, 2004.

J. Buckley and I. James, Linear regression with censored data, Biometrika, vol.66, p.34, 1979.

T. Cai, J. Huang, and L. Tian, Regularized estimation for the accelerated failure time model, Biometrics, vol.65, p.36, 2009.

J. G. Caporaso, J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman et al., Qiime allows analysis of highthroughput community sequencing data, Nature methods, vol.7, issue.5, p.55, 2010.

J. G. Caporaso, J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman et al., Qiime allows analysis of highthroughput community sequencing data, Nature methods, vol.7, issue.5, p.91, 2010.

L. A. Carmody, J. Zhao, P. D. Schloss, J. F. Petrosino, S. Murray et al., Changes in cystic fibrosis airway microbiota at pulmonary exacerbation, Annals of the American Thoracic Society, vol.10, issue.3, p.57, 2013.

A. Chao, R. L. Chazdon, R. K. Colwell, and S. , A new statistical approach for assessing similarity of species composition with incidence and abundance data, Ecology letters, vol.8, issue.2, p.62, 2005.

E. S. Charlson, K. Bittinger, A. R. Haas, A. S. Fitzgerald, I. Frank et al., Topographical continuity of bacterial populations in the healthy human respiratory tract, American journal of respiratory and critical care medicine, vol.184, issue.8, p.54, 2011.

E. S. Charlson, J. M. Diamond, K. Bittinger, A. S. Fitzgerald, A. Yadav et al., Lung-enriched organisms and aberrant bacterial and fungal respiratory microbiota after lung transplant, American journal of respiratory and critical care medicine, vol.186, issue.6, p.20, 2012.

J. Chen, F. D. Bushman, J. D. Lewis, G. D. Wu, and H. Li, Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis, Biostatistics, vol.14, p.83, 2013.

J. Chen and H. Li, Kernel Methods for Regression Analysis of Microbiome Compositional Data, vol.72, p.74, 2013.

J. Chen and H. Li, Variable selection for sparse dirichlet-multinomial regression with an application to microbiome data analysis. The annals of applied statistics, vol.7, p.81, 2013.

J. Chen, K. Bittinger, E. S. Charlson, C. Hoffmann, J. Lewis et al., Associating microbiome composition with environmental covariates using generalized unifrac distances, Bioinformatics, vol.28, issue.16, p.63, 2012.

J. Chen, K. Bittinger, E. S. Charlson, C. Hoffmann, J. Lewis et al., Associating microbiome composition with environmental covariates using generalized unifrac distances, Bioinformatics, vol.28, issue.16, p.63, 2012.

L. Chen, H. Liu, J. A. Kocher, H. Li, and C. J. , glmgraph : an r package for variable selection and predictive modeling of structured genomic data, Bioinformatics, vol.31, issue.24, p.87, 2015.

J. Chiquet, M. Mariadassou, and R. S. , Variational inference for sparse network reconstruction from count data, Proceedings of the 36th International Conference on Machine Learning, vol.69, p.70, 2019.

M. Chung, Q. Long, and J. B. , A tutorial on rank-based coefficient estimation for censored data in small-and large-scale problems, Statistics and computing, vol.23, issue.5, p.17, 2013.

K. R. Clarke, Non-parametric multivariate analyses of changes in community structure, Austral Ecology, vol.18, issue.1, p.74, 1993.

D. Conrad, M. Haynes, P. Salamon, P. B. Rainey, M. Youle et al., Cystic fibrosis therapy : a community ecology perspective, American journal of respiratory cell and molecular biology, vol.48, issue.2, p.20, 2013.

M. J. Cox, E. M. Turek, C. Hennessy, G. K. Mirza, P. L. James et al., Longitudinal assessment of sputum microbiome by sequencing of the 16s rrna gene in non-cystic fibrosis bronchiectasis patients, PLOS ONE, vol.12, issue.2, p.18, 2017.

A. Cozzi-lepri, Initiatives for developing and comparing genotype interpretation systems : external validation of existing rule-based interpretation systems for abacavir against virological response, HIV medicine, vol.9, issue.1, p.36, 2008.

A. Cozzi-lepri, M. C. Prosperi, J. Kjaer, D. Dunn, R. Paredes et al., for the EuroSIDA, and the United Kingdom CHIC/United Kingdom HDRD Studies. Can linear regression modeling help clinicians in the interpretation of genotypic resistance data ? an application to derive a lopinavir-score, PLOS ONE, vol.6, issue.11, p.16, 2011.

S. K. Cribbs and J. Beck, Microbiome in the pathogenesis of cystic fibrosis and lung transplant-related disease, Translational Research, vol.179, p.18, 2017.

J. F. Cryan, O. 'mahony, and S. , The microbiome-gut-brain axis : from bowel to behavior, Neurogastroenterology & Motility, vol.23, issue.3, p.54, 2011.

S. Datta, J. Le-rademacher, and S. Datta, Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and lasso, Biometrics, vol.63, p.17, 2007.

E. M. De-koff, K. M. De-winter-de-groot, and D. Bogaert, Development of the respiratory tract microbiota in cystic fibrosis. Current opinion in pulmonary medicine, vol.22, p.19, 2016.

D. Greco, M. F. Pattaro, C. Minelli, C. , and T. J. , Bayesian analysis of censored response data in family-based genetic association studies, Biometrical Journal, vol.58, issue.5, p.33, 2016.

L. Delhaes, S. Monchy, E. Fréalle, C. Hubans, J. Salleron et al., The airway microbiota in cystic fibrosis : a complex fungal and bacterial community-implications for therapeutic management, PLOS ONE, vol.7, issue.4, p.20, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00806782

R. P. Dickson, J. R. Erb-downward, and H. G. , The role of the bacterial microbiome in lung disease. Expert review of respiratory medicine, vol.7, p.54, 2013.

G. Dinse, A. Jusko, L. Ho, K. Annam, B. Graubard et al., Accomodating measurements below a limit of detection : A novel application of Cox regression, American Journal of Epidemiology, vol.179, issue.8, p.33, 2014.

A. G. Dirienzo, Parsimonious covariate selection with censored outcomes, Biometrics, vol.72, p.17, 2016.

S. Dray and A. Dufour, The ade4 package : implementing the duality diagram for ecologists, Journal of statistical software, vol.22, issue.4, p.65, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00434575

J. Durack, S. V. Lynch, S. Nariya, N. R. Bhakta, A. Beigelman et al., Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment, Journal of Allergy and Clinical Immunology, vol.140, issue.1, p.18, 2017.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, The Annals of Statistics, vol.32, p.26, 2004.

J. J. Egozcue, V. Pawlowsky-glahn, G. Mateu-figueras, and C. Barcelo-vidal, Isometric logratio transformations for compositional data analysis, Mathematical Geology, vol.35, issue.3, p.66, 2003.

P. H. Eilers, E. Röder, H. F. Savelkoul, and R. G. Van-wijk, Quantile regression for the statistical analysis of immunological data with many non-detects, BMC Immunology, vol.13, p.33, 2012.

B. Falissard, Epistémologie de la statistique à l'heure du tout digital, 2018.

J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American statistical Association, vol.96, issue.456, p.26, 2001.

H. Fang, C. Huang, H. Zhao, and M. Deng, Cclasso : correlation inference for compositional data through lasso, Bioinformatics, vol.31, issue.19, p.70, 2015.

K. Faust, J. F. Sathirapongsasuti, J. Izard, N. Segata, D. Gevers et al., Microbial co-occurrence relationships in the human microbiome, PLOS Comput Biol, vol.8, issue.7, p.70, 2012.

R. Feigelman, C. R. Kahlert, F. Baty, F. Rassouli, R. L. Kleiner et al., Sputum dna sequencing in cystic fibrosis : non-invasive access to the lung microbiome and to pathogen details, vol.5, p.18, 2017.

A. D. Fernandes, J. M. Macklaim, T. G. Linn, G. Reid, and G. G. , Anova-like differential expression (aldex) analysis for mixed population rna-seq, PLOS ONE, vol.8, issue.7, p.76, 2013.

A. D. Fernandes, J. N. Reid, J. M. Macklaim, T. A. Mcmurrough, D. R. Edgell et al., Unifying the analysis of high-throughput sequencing datasets : characterizing rna-seq, 16s rrna gene sequencing and selective growth experiments by compositional data analysis. Microbiome, vol.2, p.78, 2014.

L. Filkins, T. Hampton, A. Gifford, M. Gross, D. Hogan et al., and Calvez V. Comparison of tests and procedures to build clinically relevant genotypic scores : application to the jaguar study, Journal of bacteriology, vol.194, issue.17, p.35, 2005.

A. A. Fodor, E. R. Klem, D. F. Gilpin, J. S. Elborn, R. C. Boucher et al., The adult cystic fibrosis airway microbiota is stable over time and infection type, and highly resilient to antibiotic treatment of exacerbations, PLOS ONE, vol.7, issue.9, p.19, 2012.

L. E. Frank and J. H. Friedman, A statistical view of some chemometrics regression tools, Technometrics, vol.35, issue.2, p.26, 1993.

K. B. Frayman, D. S. Armstrong, K. Grimwood, and S. C. Ranganathan, The airway microbiota in early cystic fibrosis lung disease, Pediatric Pulmonology, p.18, 2017.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, vol.33, issue.1, p.26, 2010.

J. Friedman, E. J. Alm, V. Mering, and C. , Inferring correlation networks from genomic survey data, PLOS Computational Biology, vol.8, p.70, 2012.

P. Fu, J. Hughes, G. Zeng, S. Hanook, J. Orem et al., A comparative investigation of methods for longitudinal data with limits of detection through a case study, Statistical Methods in Medical Research, vol.25, issue.1, p.34, 2016.

W. J. Fu, Penalized regressions : the bridge versus the lasso, Journal of computational and graphical statistics, vol.7, issue.3, p.26, 1998.

J. Fukuyama, P. J. Mcmurdie, L. A. Dethlefsen-d, and . Holmes-s, Comparisons of distance methods for combining covariates and abundances in microbiome studies, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, p.63, 2012.

T. P. Garcia, S. Müller, R. J. Carroll, and R. L. Walzem, Identification of important regressor groups, subgroups and individuals via regularization methods : application to gut microbiome data, Bioinformatics, vol.30, issue.6, p.87, 2014.

B. W. Gillespie, Q. Chen, H. Reichert, A. Franzblau, E. Hedgeman et al., Estimating population distributions when some data are below a limit of detection by using a reverse kaplan-meier estimator, Epidemiology, vol.21, p.33, 2010.

E. Goleva, L. P. Jackson, J. K. Harris, C. E. Robertson, E. R. Sutherland et al., The effects of airway microbiome on corticosteroid responsiveness in asthma, American journal of respiratory and critical care medicine, vol.188, issue.10, pp.1193-1201, 2013.

C. H. Goss and J. L. Burns, Exacerbations in cystic fibrosis· 1 : epidemiology and pathogenesis, Thorax, vol.62, issue.4, p.19, 2007.

P. Guo, Y. Hao, and . Sparselearner, Sparse Learning Algorithms Using a LASSO-Type Penalty for Coefficient Estimation and Model Prediction, 2015.

T. J. Hardcastle and K. A. Kelly, Empirical bayesian analysis of paired high-throughput sequencing data with a beta-binomial distribution, BMC bioinformatics, vol.14, issue.1, p.78, 2013.

B. C. Healy, V. G. Degruttola, and C. Hu, Accommodating uncertainty in a tree set for function estimation, Statistical applications in genetics and molecular biology, vol.7, p.36, 2008.

G. Heinze and D. Dunkler, Five myths about variable selection, Transplant International, vol.30, issue.1, p.135, 2017.

A. A. Heirali, M. L. Workentine, N. Acosta, A. Poonja, D. G. Storey et al., The effects of inhaled aztreonam on the cystic fibrosis lung microbiome, vol.5, p.18, 2017.

D. P. Helmbold and P. Long, On the necessity of irrelevant variables, Journal of Machine Learning Research, vol.13, p.14, 2012.

D. R. Helsel, More than obvious : Better methods for interpreting nondetect data, Environmental Science & Technology, vol.39, issue.20, p.33, 2005.

M. Hilty, C. Burke, H. Pedro, P. Cardenas, A. Bush et al., Disordered microbial communities in asthmatic airways, PLOS ONE, vol.5, issue.1, p.18, 2010.

M. S. Hirsch, H. F. Günthard, J. M. Schapiro, F. B. Vézinet, B. Clotet et al., Antiretroviral drug resistance testing in adult HIV-1 infection : 2008 recommendations of an international aids society-USA panel, Clinical Infectious Diseases, vol.47, issue.2, p.16, 2008.

A. E. Hoerl and R. W. Kennard, Ridge regression : applications to nonorthogonal problems, Technometrics, vol.12, issue.1, p.25, 1970.

L. M. Hofstra, N. Sauvageot, J. Albert, I. Alexiev, F. Garcia et al., Transmission of HIV drug resistance and the predicted effect on current first-line regimens in europe, Clinical infectious diseases, vol.62, issue.5, p.16, 2016.

K. B. Hooks and M. A. O'malley, Dysbiosis and its discontents, mBio, vol.8, issue.5
URL : https://hal.archives-ouvertes.fr/hal-01677295

H. D. Hosgood, A. R. Sapkota, N. Rothman, T. Rohan, W. Hu et al., The potential role of lung microbiota in lung cancer attributed to household coal burning exposures, Environmental and molecular mutagenesis, vol.55, issue.8, p.19, 2014.

J. Huang, S. Ma, and H. Xie, Regularized estimation in the accelerated failure time model with high-dimensional covariates, Biometrics, vol.62, p.17, 2006.

X. Huang, W. Pan, S. Park, X. Han, L. W. Miller et al., Modeling the relationship between lvad support time and gene expression changes in the human heart by penalized partial least squares, Bioinformatics, vol.20, issue.6, p.36, 2004.

Y. J. Huang, S. Sethi, T. Murphy, S. Nariya, H. A. Boushey et al., Airway microbiome dynamics in exacerbations of chronic obstructive pulmonary disease, Journal of clinical microbiology, vol.52, issue.8, p.18, 2014.

J. P. Hughes, Mixed effects models with censored data with application to HIV RNA levels, Biometrics, vol.55, p.34, 1999.

C. Huttenhower, D. Gevers, R. Knight, S. Abubucker, J. H. Badger et al., Structure, function and diversity of the healthy human microbiome, Nature, vol.486, issue.7402, p.59, 2012.

H. Ishwaran, U. B. Kogalur, E. H. Blackstone, and M. Lauer, Random survival forests, The Annals of Applied Statistics, vol.2, p.17, 2008.

H. Jacqmin-gadda, R. Thiébaut, G. Chêne, and D. Commenges, Analysis of left-censored longitudinal data with application to viral load in HIV infection, Biostatistics, vol.1, issue.4, p.34, 2000.

B. A. Johnson, Rank-based estimation in the 1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data, Biostatistics, vol.10, p.17, 2009.

B. A. Johnson, Variable selection in semiparametric linear regression with censored data, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.70, p.36, 2008.

B. A. Johnson, On lasso for censored data, Electronic Journal of Statistics, vol.3, p.36, 2009.

V. A. Johnson, F. Brun-vézinet, B. Clotet, H. Gunthard, D. R. Kuritzkes et al., Update of the drug resistance mutations in HIV-1, Top HIV Med, vol.17, issue.5, p.37, 2009.

R. J. Keizer, R. S. Jansen, H. Rosing, B. Thijssen, J. H. Beijnen et al., Incorporation of concentration data below the limit of quantification in population pharmacokinetic analyses, Pharmacology research & perspectives, vol.3, issue.2, p.134, 2015.

R. A. Koeth, Z. Wang, B. S. Levison, J. A. Buffa, E. Org et al., Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis, Nature medicine, vol.19, issue.5, p.54, 2013.

H. Koh, M. J. Blaser, and H. Li, A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping, vol.5, p.74, 2017.

H. H. Kong, J. Oh, C. Deming, S. Conlan, E. A. Grice et al., Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis, Genome research, vol.22, issue.5, p.54, 2012.

N. Kraemer, J. Schaefer, and B. , Regularized estimation of large-scale gene regulatory networks using gaussian graphical models, BMC Bioinformatics, vol.10, issue.384, p.28, 2009.

R. Kramer, A. Sauer-heilborn, T. Welte, C. A. Guzman, W. Abraham et al., Cohort study of airway mycobiome in adult cystic fibrosis patients : differences in community structure between fungi and bacteria reveal predominance of transient fungal elements, Journal of clinical microbiology, vol.53, issue.9, p.20, 2015.

R. Krause, C. Moissl-eichinger, B. Halwachs, G. Gorkiewicz, G. Berg et al., Mycobiome in the lower respiratory tract -a clinical perspective, Frontiers in Microbiology, vol.7, p.20, 2017.

Z. D. Kurtz, C. L. Müller, E. R. Miraldi, D. R. Littman, M. J. Blaser et al., Sparse and compositionally robust inference of microbial ecological networks, PLOS computational biology, vol.11, issue.5, p.70, 2015.

P. Larossa, J. P. Brooks, E. Deych, E. L. Boone, D. J. Edwards et al., Hypothesis testing and power calculations for taxonomicbased human microbiome data, PLOS ONE, vol.73, p.74, 2012.

S. L. Lauritzen, Graphical models, vol.17, p.69, 1996.

L. Cao, K. Costello, M. Lakis, V. A. Bartolo, F. Chua et al., Mixmc : a multivariate statistical framework to gain insight into microbial communities, PLOS ONE, vol.11, issue.8, p.87, 2016.

M. Lee, L. Kong, and L. Weissfeld, Multiple imputation for left-censored biomarker data based on Gibbs sampling method, Statistics in Medicine, vol.31, p.33, 2012.

S. H. Lee, J. Y. Sung, D. Yong, J. Chun, S. Y. Kim et al., Characterization of microbiome in bronchoalveolar lavage fluid of patients with lung cancer comparing with benign mass like lesions, Lung Cancer, vol.102, p.19, 2016.

C. D. Leite, T. W. Folescu, M. De-cássia-firmida, R. W. Cohen, R. S. Leão et al., Monitoring clinical and microbiological evolution of a cystic fibrosis patient over 26 years : experience of a brazilian cf centre, BMC pulmonary medicine, vol.17, issue.1, p.18, 2017.

H. Li, Microbiome, metagenomics, and high-dimensional compositional data analysis. Annual review of statistics and its application, vol.2, p.56, 2015.

Y. W. Lim, R. Schmieder, M. Haynes, D. Willner, M. Furlan et al., Metagenomics and metatranscriptomics : windows on cfassociated viral and microbial communities, Journal of Cystic Fibrosis, vol.12, issue.2, p.19, 2013.

W. Lin, P. Shi, R. Feng, and H. Li, Variable selection in regression with compositional covariates, Biometrika, vol.101, issue.4, p.87, 2014.

D. Liu, D. Ghosh, and L. X. , Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models, BMC bioinformatics, vol.9, issue.1, p.73, 2008.

H. Liu, X. Xu, and L. J. , A bootstrap lasso+ partial ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models, p.28, 2017.

M. I. Love, W. Huber, and A. S. , Moderated estimation of fold change and dispersion for rna-seq data with deseq2, Genome biology, vol.15, issue.12, p.78, 2014.

C. Lozupone and R. Knight, Unifrac : a new phylogenetic method for comparing microbial communities, APPL. ENVIRON, p.63, 2005.

C. A. Lozupone, M. Hamady, S. T. Kelley, and R. Knight, Quantitative and qualitative ? diversity measures lead to different insights into factors that structure microbial communities, Applied and environmental microbiology, vol.73, issue.5, p.63, 2007.

H. S. Lynn, Maximum likelihood inference for left-censored HIV RNA data, Statistics in Medicine, vol.20, p.34, 2001.

L. K. Macdougall, G. Broukhanski, A. Simor, J. Johnstone, S. Mubareka et al., Comparison of qpcr versus culture for the detection and quantification of clostridium difficile environmental contamination, PLOS ONE, vol.13, issue.8, p.201569, 2018.

S. Mandal, W. Van-treuren, R. A. White, M. Eggesbø, R. Knight et al., Analysis of composition of microbiomes : a novel method for studying microbial composition. Microbial ecology in health and disease, vol.26, p.78, 2015.

E. Marcon and . Mesures-de-la-biodiversité, Lecture, p.60, 2015.

J. Mariette and N. Villa-vialaneix, Unsupervised multiple kernel learning for heterogeneous data integration, Bioinformatics, vol.34, issue.6, p.64, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01738461

G. Marks, L. I. Gardner, J. Craw, T. P. Giordano, M. J. Mugavero et al., The spectrum of engagement in hiv care : do more than 19% of hiv-infected persons in the us have undetectable viral load ? Clinical infectious diseases, vol.53, p.67, 2011.

P. R. Marri, D. A. Stern, A. L. Wright, D. Billheimer, and F. D. Martinez, Asthma-associated differences in microbial composition of induced sputum, Journal of Allergy and Clinical Immunology, vol.131, issue.2, pp.346-352, 2013.

I. Marschner, R. Betensky, V. Degruttola, S. Hammer, and D. Kuritzkes, Clinical trials using HIV-1 RNA-based primary endpoints : Statistical analysis and potential biase, Journal of acquired immune deficiency syndromes and human retrovirology, vol.20, issue.3, p.33, 1999.

B. J. Marsland and E. S. Gollwitzer, Host-microorganism interactions in lung diseases, Nature reviews. Immunology, vol.14, issue.12, p.54, 2014.

J. Martín-fernández and S. Thió-henestrosa, Rounded zeros : some practical aspects for compositional data, Geological Society, vol.264, issue.1, p.67, 2006.

J. Martín-fernández, C. Barceló-vidal, and V. Pawlowsky-glahn, Dealing with zeros and missing values in compositional data sets using nonparametric imputation, Mathematical Geology, vol.35, issue.3, p.67, 2003.

J. A. Mart?n-fernandez, J. Palarea-albaladejo, and R. A. Olea, Dealing with zeros. Compositional data analysis : Theory and applications, p.67, 2011.

M. V. Biology, The big challenges of big data, Nature, vol.498, issue.7453, pp.255-260, 2013.

D. Mcdonald, J. C. Clemente, J. Kuczynski, J. R. Rideout, J. Stombaugh et al., The biological observation matrix (biom) format or : how i learned to stop worrying and love the ome-ome, vol.1, p.132, 2012.

P. J. Mcmurdie and S. Holmes, Waste not, want not : why rarefying microbiome data is inadmissible, PLOS computational biology, vol.10, issue.4, p.56, 2014.

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.72, issue.4, p.82, 2010.

P. L. Molyneaux, P. Mallia, M. J. Cox, J. Footitt, S. A. Willis-owen et al., Outgrowth of the bacterial airway microbiome after rhinovirus exacerbation of chronic obstructive pulmonary disease, American journal of respiratory and critical care medicine, vol.188, issue.10, pp.1224-1231, 2013.

S. J. Mooney and V. Pejaver, Big data in public health : terminology, machine learning, and privacy. Annual review of public health, vol.39, pp.95-112, 2018.

A. Morris, J. M. Beck, P. D. Schloss, T. B. Campbell, K. Crothers et al., Comparison of the respiratory microbiome in healthy nonsmokers and smokers, American journal of respiratory and critical care medicine, vol.187, issue.10, p.54, 2013.

P. Müller and S. Van-de-geer, Censored linear model in high dimensions, TEST, p.16, 2015.

L. D. Nguyen, P. Deschaght, S. Merlin, A. Loywick, C. Audebert et al., Effects of propidium monoazide (pma) treatment on mycobiome and bacteriome analysis of cystic fibrosis airways during exacerbation, PLOS ONE, vol.11, issue.12, p.131, 2016.

L. Nie, H. Chu, C. Liu, S. R. Cole, A. Vexler et al., Linear regression with an independent variable subject to a detection limit, Epidemiology, vol.21, p.34, 2010.

S. O'brien and J. L. Fothergill, The role of multispecies social interactions in shaping pseudomonas aeruginosa pathogenicity in the cystic fibrosis lung, FEMS Microbiology Letters, vol.364, issue.15, p.131, 1920.

J. Oksanen, F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre et al., Community Ecology Package, vol.61, p.62, 2017.

, Guidelines for the use of antiretroviral agents in adults and adolescents living with hiv, on Antiretroviral Guidelines for Adults P. and Adolescents, p.34, 2018.

J. Palarea-albaladejo, J. A. Martín-fernández, and J. Gómez-garcía, A parametric approach for dealing with compositional rounded zeros, Mathematical Geology, vol.39, issue.7, p.67, 2007.

O. Paliy and V. Shankar, Application of multivariate statistical techniques in microbial ecology, Molecular ecology, vol.25, issue.5, p.64, 2016.

W. Pan, J. Kim, Y. Zhang, X. Shen, and W. P. , A powerful and adaptive association test for rare variants, Genetics, vol.197, issue.4, p.74, 2014.

E. Paradis, J. Claude, and K. Strimmer, APE : analyses of phylogenetics and evolution in R language, Bioinformatics, vol.20, p.64, 2004.
URL : https://hal.archives-ouvertes.fr/ird-01887318

D. H. Parks, G. W. Tyson, P. Hugenholtz, and R. G. Beiko, Stamp : statistical analysis of taxonomic and functional profiles, Bioinformatics, vol.30, issue.21, p.78, 2014.

J. Paulson, O. Stine, C. Bravo, H. Pop, and M. , Robust methods for differential abundance analysis in marker gene surveys, Nat Methods, vol.10, p.132, 2013.

W. Paxton, R. Coombs, M. Mcelrath, M. Keefer, J. Hughes et al., Longitudinal analysis of quantitative virologic measures in human immunodeficiency virus-infected subjects with > or = 400 cd4 lymphocytes : implications for applying measurements to individual patients. national institute of allergy and infectious diseases aids vaccine evaluation group, Journal of Infectious Disease, vol.175, issue.2, p.33, 1997.

M. E. Pierotti, J. A. Martín-fernández, and O. Seehausen, Mapping individual variation in male mating preference space : multiple choice in a color polymorphic cichlid fish, Evolution, vol.63, issue.9, p.67, 2009.

J. E. Pittman, K. M. Wylie, K. Akers, G. A. Storch, J. Hatch et al., Association of antibiotics, airway microbiome, and inflammation in infants with cystic fibrosis, Annals of the American Thoracic Society, vol.14, issue.10, p.18, 2017.

J. L. Powell, Least absolute deviations estimation for the censored regression model, Journal of Econometrics, vol.25, p.33, 1984.

J. L. Powell, Censored regression quantiles, Journal of Econometrics, vol.32, p.33, 1986.

J. Qin, Y. Li, Z. Cai, S. Li, J. Zhu et al., A metagenome-wide association study of gut microbiota in type 2 diabetes, Nature, vol.490, issue.7418, p.54, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01204262

R. A. Quinn, Y. W. Lim, H. Maughan, D. Conrad, F. Rohwer et al., Biogeochemical forces shape the composition and physiology of polymicrobial communities in the cystic fibrosis lung, MBio, vol.5, issue.2, p.20, 2014.

R. A. Quinn, K. Whiteson, Y. Lim, P. Salamon, B. Bailey et al., A winogradsky-based culture system shows an association between microbial fermentation and cystic fibrosis exacerbation, The ISME journal, vol.9, issue.4, p.19, 2015.

R. A. Quinn, Y. W. Lim, T. D. Mak, K. Whiteson, M. Furlan et al., Metabolomics of pulmonary exacerbations reveals the personalized nature of cystic fibrosis disease. PeerJ, 4 : e2174, vol.20, p.131, 2016.

R. A. Quinn, K. Whiteson, Y. W. Lim, J. Zhao, D. Conrad et al., Ecological networking of cystic fibrosis lung infections, NPJ Biofilms and Microbiomes, vol.2, issue.1, p.20, 2016.

M. Rabinowitz, L. Myers, M. Banjevic, A. Chan, J. Sweetkind-singer et al., Accurate prediction of HIV-1 drug response from the reverse transcriptase and protease amino acid sequences using sparse models created by convex optimization, Bioinformatics, vol.22, issue.5, p.16, 2006.

A. Ramette, Multivariate analyses in microbial ecology, FEMS microbiology ecology, vol.62, issue.2, p.64, 2007.

S. Rhee, J. Taylor, G. Wadhera, A. Ben-hur, D. Brutlag et al., Genotypic predictos of human immunodeficiency cirus type 1 drug resistance, Proc Natl Acad Sci U S A, vol.103, issue.46, p.16, 2006.

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.78, 2010.

M. D. Robinson, D. J. Mccarthy, and G. K. Smyth, edger : a bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, vol.26, issue.1, p.76, 2010.

R. Romero, S. S. Hassan, P. Gajer, A. L. Tarca, D. W. Fadrosh et al., The vaginal microbiota of pregnant women who subsequently have spontaneous preterm labor and delivery and those with a normal delivery at term. Microbiome, vol.2, p.54, 2014.

S. T. Rush, C. H. Lee, W. Mio, and K. P. , The phylogenetic lasso and the microbiome, vol.85, p.87, 2016.

P. D. Schloss, S. L. Westcott, T. Ryabin, J. R. Hall, M. Hartmann et al., Introducing mothur : open-source, platform-independent, community-supported software for describing and comparing microbial communities, Applied and environmental microbiology, vol.75, issue.23, p.55, 2009.

J. Schumi and V. Degruttola, Resampling-based analyses of the effects of combinations of hiv genetic mutations on drug susceptibility, Statistics in medicine, vol.27, issue.23, p.36, 2008.

N. Segata, J. Izard, L. Waldron, D. Gevers, L. Miropolsky et al., Metagenomic biomarker discovery and explanation, Genome biology, vol.12, issue.6, p.78, 2011.

R. W. Shafer and J. Schapiro, HIV-1 drug resistance mutations : an updated framework for the second decade of haart, AIDS reviews, vol.10, issue.2, p.37, 2008.

J. Shankar, S. Szpakowski, N. V. Solis, S. Mounaud, H. Liu et al., A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses, BMC bioinformatics, vol.16, issue.1, p.87, 2015.

G. Shmueli, To explain or to predict ? Statistical science, vol.25, p.14, 2010.

J. H. Shows, W. Lu, and H. H. Zhang, Sparse estimation and inference for censored median regression, Journal of Statistical Planning and Inference, vol.140, p.16, 2010.

N. Simon, J. Friedman, T. Hastie, and T. R. , SGL : Fit a GLM (or cox model) with a combination of lasso and group lasso regularization, 2013.

N. Simon, J. Friedman, T. Hastie, and T. R. , A sparse-group lasso, Journal of Computational and Graphical Statistics, vol.22, issue.2, p.30, 2013.

J. L. Simpson, J. Daly, K. J. Baines, I. A. Yang, J. W. Upham et al., Airway dysbiosis : Haemophilus influenzae and tropheryma in poorly controlled asthma, European Respiratory Journal, vol.47, issue.3, p.18, 2016.

E. C. Society and . European, Cystic Fibrosis Society Patient Registry Annual Report, p.89, 2014.

A. E. Stenbit and P. Flume, Pulmonary exacerbations in cystic fibrosis. Current opinion in pulmonary medicine, vol.17, p.19, 2011.

A. Sverrild, P. Kiilerich, A. Brejnrod, R. Pedersen, C. Porsbjerg et al., Eosinophilic airway inflammation in asthmatic patients is associated with an altered airway microbiome, Journal of Allergy and Clinical Immunology, vol.140, issue.2, p.18, 2017.

R. Thiébaut, B. P. Hejblum, and R. L. , L'analyse des «big data» en recherche clinique. Epidemiology and Public Health/Revue d'Epidémiologie et de Santé Publique, vol.62, pp.1-4, 2014.

J. Thorsen, A. Brejnrod, M. Mortensen, M. A. Rasmussen, J. Stokholm et al., Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16s rrna gene amplicon data analysis methods used in microbiome studies, vol.4, p.75, 2016.

R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.15, p.25, 1996.

R. Tibshirani, The lasso method for variable selection in the cox model, Statistics in Medicine, vol.16, p.16, 1997.

J. Tobin, Estimation of relationships for limited dependent variables, Econometrica, vol.26, p.34, 1958.

K. Touati and L. Delhaes, The airway colonization by opportunistic filamentous fungi in patients with cystic fibrosis : recent updates, Current Fungal Infection Reports, vol.8, issue.4, p.89, 2014.

S. G. Tringe, E. M. Rubin, and . Metagenomics, Dna sequencing of environmental samples, Nature reviews genetics, vol.6, issue.11, p.54, 2005.

M. C. Tsilimigras and A. A. Fodor, Compositional data analysis of the microbiome : fundamentals, tools, and challenges, Annals of epidemiology, vol.26, issue.5, p.69, 2016.

M. M. Tunney, G. G. Einarsson, L. Wei, M. Drain, E. R. Klem et al., Lung microbiota and bacterial abundance in patients with bronchiectasis when clinically stable and during exacerbation, American journal of respiratory and critical care medicine, vol.187, issue.10, p.19, 2013.

M. Ueki, A note on automatic variable selection using smooth-threshold estimating equations, Biometrika, p.17, 2009.

K. G. Van-den-boogaart, R. Tolosana, and B. M. , Compositions : compositional data analysis. R package version, p.66, 2010.

H. K. Van-der-burgh, R. Schmidt, H. Westeneng, M. A. De-reus, L. H. Van-den-berg et al., Deep learning predictions of survival based on mri in amyotrophic lateral sclerosis, NeuroImage : Clinical, vol.13, p.17, 2017.

M. J. Vavrek, fossil : palaeoecological and palaeogeographical analysis tools, Palaeontologia Electronica, vol.14, issue.1, p.1, 2011.

W. D. Wadsworth, R. Argiento, M. Guindani, J. Galloway-pena, S. A. Shelburne et al., An integrative bayesian dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data, BMC bioinformatics, vol.18, issue.1, p.81, 2017.

H. J. Wang, Z. Zhu, and J. Zhou, Quantile regression in partially linear varying coefficient models, The Annals of Statistics, vol.37, issue.6B, p.33, 2009.

H. J. Wang, J. Zhou, and Y. Li, Variable selection for censored quantile regression, Statistica Sinica, vol.23, issue.1, pp.145-167, 2013.

J. Wang, M. Lesko, M. H. Badri, B. C. Kapoor, B. G. Wu et al., Lung microbiome and host immune tone in subjects with idiopathic pulmonary fibrosis treated with inhaled interferon-?, ERJ Open Research, vol.3, issue.3, p.19, 2017.

S. Wang, B. Nan, J. Zhu, and D. G. Beer, Doubly penalized Buckley -James method for survival data with high-dimensional covariates, Biometrics, vol.64, issue.1, p.17, 2008.

Y. Wang, T. Chen, and D. Zeng, Support vector hazards machine : A counting process framework for learning risk scores for censored outcomes, Journal of Machine Learning Research, vol.17, issue.167, p.17, 2016.

Z. Wang, Y. Wu, and L. Zhao, A LASSO-type approach to variable selection and estimation for censored regression model, Chinese Journal of Applied Probability and Statistics, vol.26, issue.1, p.16, 2010.

Z. Wang and C. Wang, Buckley-James boosting for survival analysis with high-dimensional biomarker data, Statistical Applications in Genetics and Molecular Biology, vol.9, issue.1, p.36, 2010.

R. Wei, J. Wang, E. Jia, T. Chen, Y. Ni et al., A gibbs sampler based left-censored missing value imputation approach for metabolomics studies, PLoS computational biology, vol.14, issue.1, p.134, 2018.

U. Weinreich and J. Korsgaard, Bacterial colonisation of lower airways in health and chronic lung disease. The clinical respiratory journal, vol.2, p.18, 2008.

S. Weiss, Z. Z. Xu, S. Peddada, A. Amir, K. Bittinger et al., Normalization and microbial differential abundance strategies depend upon data characteristics, vol.5, p.75, 2017.

A. M. Wensing, V. Calvez, H. F. Günthard, V. A. Johnson, R. Paredes et al., update of the drug resistance mutations in HIV-1, Topics in antiviral medicine, vol.24, issue.4, p.16, 2017.

J. R. White, N. Nagarajan, and M. Pop, Statistical methods for detecting differentially abundant features in clinical metagenomic samples, PLOS computational biology, vol.5, issue.4, p.78, 2009.

K. L. Whiteson, B. Bailey, M. Bergkessel, D. Conrad, L. Delhaes et al., The upper respiratory tract as a microbial source for pulmonary infections in cystic fibrosis. parallels from island biogeography, American journal of respiratory and critical care medicine, vol.189, issue.11, p.20, 2014.

R. H. Whittaker, Vegetation of the siskiyou mountains, oregon and california. Ecological Monographs, vol.30, p.61, 1960.

R. E. Wiegand, C. E. Rose, and K. J. , Comparison of models for analyzing two-group, cross-sectional data with a gaussian outcome subject to a detection limit, Statistical Methods in Medical Research, vol.17, p.36, 2016.

S. D. Willger, S. L. Grim, E. L. Dolben, A. Shipunova, T. H. Hampton et al., Characterization and quantification of the fungal microbiome in serial samples from individuals with cystic fibrosis, vol.2, p.20, 2014.

D. Willner, M. R. Haynes, M. Furlan, N. Hanson, B. Kirby et al., Case studies of the spatial heterogeneity of dna viruses in the cystic fibrosis lung, American journal of respiratory cell and molecular biology, vol.46, issue.2, p.19, 2012.

L. Wittkop, H. Günthard, F. De-wolf, D. Dunn, A. Cozzi-lepri et al., Effects of transmitted drug resistance on virological and immunological response to initial combination antiretroviral therapy for HIV (euro-coord-chain joint project) : a european multicohort study. The lancet infectious diseases, vol.11, p.16, 2011.

L. Wittkop, D. Commenges, I. Pellegrin, D. Breilh, D. Neau et al., Alternative methods to analyse the impact of HIV mutations on virological response to antiviral therapy, BMC Medical Research Methodology, vol.8, issue.1, p.35, 2008.
URL : https://hal.archives-ouvertes.fr/inserm-00333577

S. Wold, H. Martens, and H. Wold, The multivariate calibration problem in chemistry solved by the pls method, Matrix pencils, p.15, 1983.

F. Xia, J. Chen, W. K. Fung, and L. H. , A logistic normal multinomial regression model for microbiome compositional data analysis, Biometrics, vol.69, issue.4, p.81, 2013.

Y. Xia and J. Sun, Hypothesis testing and statistical analysis of microbiome, Genes & Diseases, vol.4, issue.3, p.75, 2017.

X. Xue, X. Xie, and H. D. Strickler, A censored quantile regression approach for the analysis of time to event data, Statistical Methods in Medical Research, vol.0, issue.0

Y. Yang and H. Zou, A fast unified algorithm for computing group-lasso penalized learning problems, Statistics and Computing, p.30, 2013.

Y. Yang and V. Degruttola, Resampling-based multiple testing methods with covariate adjustment : Application to investigation of antiretroviral drug susceptibility, Biometrics, vol.64, issue.2, p.36, 2008.

G. Yu, M. H. Gail, D. Consonni, M. Carugno, M. Humphrys et al., Characterizing human lung tissue microbiota and its relationship to epidemiological and clinical features, Genome biology, vol.17, issue.1, p.19, 2016.

L. Yuan, J. Liu, and Y. J. , Efficient methods for overlapping group lasso, Advances in Neural Information Processing Systems, p.83, 2011.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.68, p.30, 2006.

T. Zakharkina, E. Heinzel, R. A. Koczulla, T. Greulich, K. Rentz et al., Analysis of the airway microbiota of healthy individuals and patients with chronic obstructive pulmonary disease by t-rflp and clone sequencing, PLOS ONE, vol.8, issue.7, p.68302, 2013.

E. T. Zemanick, J. K. Harris, B. D. Wagner, C. E. Robertson, S. D. Sagel et al., Inflammation and airway microbiota during cystic fibrosis pulmonary exacerbations, PLOS ONE, vol.8, issue.4, p.19, 2013.

X. Zhan, X. Tong, N. Zhao, A. Maity, M. Wu et al., A small-sample multivariate kernel machine test for microbiome association studies, Genetic Epidemiology, vol.41, issue.5, p.73, 2017.

Q. Zhang, M. Cox, Z. Liang, F. Brinkmann, P. A. Cardenas et al., Airway microbiota in severe asthma and relationship to asthma severity and phenotypes, PLOS ONE, vol.11, issue.4, p.18, 2016.

Q. Zhang, H. Abel, A. Wells, P. Lenzini, F. Gomez et al., Selection of models for the analysis of risk-factor trees : leveraging biological knowledge to mine large sets of risk factors with application to microbiome data, Bioinformatics, vol.31, issue.10, p.87, 2015.

J. Zhao, P. D. Schloss, L. M. Kalikin, L. A. Carmody, B. K. Foster et al., Decade-long bacterial community dynamics in cystic fibrosis airways, Proceedings of the National Academy of Sciences, vol.109, issue.15, p.19, 2012.

S. D. Zhao, D. Lee, and L. Y. , The Dantzig selector for censored linear regression models, Statistica Sinica, vol.24, issue.1, p.17, 2014.

N. Zhou and J. Zhu, Group variable selection via a hierarchical lasso and its oracle property, p.85, 2010.

H. Zou, The adaptive lasso and its oracle properties, Journal of the American statistical association, vol.101, issue.476, p.27, 2006.

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.67, p.27, 2005.

A. De-recherche, Communications écrites (poster) à des conférences internationales avec comité de lecture

P. Soret, M. Avalos, ?. , L. Delhaes, and R. Thiébaut, A simulation framework of highdimensional phylogenetic microbiota data, 29 th International Biometric Conference, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01856324

L. Vandenborght, ?. , P. Soret, N. Coron, R. Enaud et al., Caractérisation du mycobiote et du microbiote respiratoire dans la mucoviscidose : importance du dialogue interrègnes pendant un phénomène d'exacerbation, 2018.

, Logiciels Implémentation des méthodes pour données censurées par limite de quantification

, Évaluation des différentes implémentations liées au GroupLasso Encadrement d'un stage de M1 Santé publique, option biostatistique, 2015.

, Begin R" : séquence pédagogique autour de la prise en main de R, de la manipulation de données et de la mise en oeuvre des principales approches de statistiques descriptives et inférentielles, Ce projet a été financé par l'Idex, pp.2014-2015

, Master1 Santé Publique : enseignement sur la régression linéaire (14h)

, Master1 Santé Publique : supervision d'un projet tutoré Master1 Santé Publique : Référente de trois étudiants à distance Intervention au sein du Workshop

, Présidente de l'association des doctorants de l'EDSP2 2014-2017 : Membre du comité des doctorants de l'EDSP2 et Membre de l'association des doctorants de l'EDSP2 26 Mai 2016 : Organisation des journées de l'EDSP2 11 Octobre 2016 : Participation à la fête de la science, Responsabilités administratives et implications dans l'organisation d'événements, pp.2015-2016, 2015.