A. Chambaz and P. , tmle.npvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation, Bioinformatics, vol.31, pp.3054-3060, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01490380

A. Chambaz, P. Neuvial, and M. J. Van-der-laan, Estimation of a Non-Parametric Variable Importance Measure of a Continuous Exposure, Electron. J. Statist, vol.6, pp.1059-1099, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00629899

A. Chambaz and P. , Targeted Learning of a Non-Parametric Variable Importance Measure of a Continuous Exposure, 2015.

, Contents 7.1 Defining the parameter of interest

.. .. Inference,

.. .. Simulations,

, An application to TCGA data

M. Pierre-jean, G. J. Rigaill, and P. Neuvial, Performance evaluation of DNA copy number segmentation methods, Briefings in Bioinformatics, vol.4, pp.600-615, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00952896

M. Ortiz-estevez, A. Aramburu, H. Bengtsson, P. Neuvial, and A. Rubio, CalMaTe: A Method and Software to Improve Allele-Specific Copy Number of SNP Arrays for Downstream Segmentation, Bioinformatics, vol.28, pp.1793-1794, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01200761

A. B. Olshen, H. Bengtsson, P. Neuvial, P. T. Spellman, R. A. Olshen et al., Parent-specific copy number in paired tumor-normal studies using circular binary segmentation, Bioinformatics, vol.27, pp.2038-2046, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01200750

H. Bengtsson, P. Neuvial, and T. P. Speed, TumorBoost: Normalization of allelespecific tumor copy numbers from a single pair of tumor-normal genotyping microarrays, BMC Bioinformatics, vol.11, issue.1, p.245, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01199777

P. Neuvial, H. Bengtsson, and T. P. Speed, Statistical analysis of Single Nucleotide Polymorphism microarrays in cancer studies, Springer Handbooks of Computational Statistics, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00497273

M. Pierre-jean, G. Rigaill, and P. Neuvial, jointseg: Joint segmentation of multivariate (copy number) signals, 2019.

N. Randriamihamison, N. Vialaneix, and P. Neuvial, Applicability and Interpretability of Ward Hierarchical Agglomerative Clustering With or Without Contiguity Constraints, Journal of Classification, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02294847

C. Ambroise, A. Dehman, P. Neuvial, G. Rigaill, and N. Vialaneix, Adjacencyconstrained hierarchical clustering of a band similarity matrix with application to Genomics, Algorithms for molecular biology, vol.14, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02790995

A. Dehman, C. Ambroise, and P. Neuvial, Performance of a Blockwise Approach in Variable Selection using Linkage Disequilibrium Information, BMC Bioinformatics, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01193074

C. Ambroise, S. Chaturvedi, A. Dehman, P. Neuvial, G. Rigaill et al., adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02791370

. Hac and . .. Hac, 90 10.2 Extensions to possibly non-Euclidean settings, Contents 10.1, p.91

, Fast segmentation of a band similarity matrix, p.92

, Application to Genome-Wide Association Studies, p.93

G. Blanchard, P. Neuvial, and E. Roquain, On agnostic post hoc approaches to false positive control. Book chapter in revision for Handbook of Multiple Comparisons, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02320543

G. Blanchard, P. Neuvial, and E. Roquain, Post Hoc Confidence Bounds on False Positives Using Reference Families, Annals of Statistics, vol.48, pp.45-47, 2020.
URL : https://hal.archives-ouvertes.fr/hal-01483585

G. Durand, G. Blanchard, P. Neuvial, and E. Roquain, Post hoc false positive control for structured hypotheses, Scandinavian Journal of Statistics, vol.98, pp.52-54, 2020.
URL : https://hal.archives-ouvertes.fr/hal-01829037

F. Pont, M. Tosolini, Q. Gao, M. Perrier, T. S. Madrid-mencía et al., Single-Cell Virtual Cytometer allows userfriendly and versatile analysis and visualization of multimodal single cell RNAseq datasets, NAR Genomics and Bioinformatics, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02861405

C. Ambroise, A. Dehman, P. Neuvial, G. Rigaill, and N. Vialaneix, Adjacencyconstrained hierarchical clustering of a band similarity matrix with application to Genomics, Algorithms for molecular biology, vol.14, pp.91-93, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02790995

F. Bachoc, G. Blanchard, and P. Neuvial, On the post selection inference constant under restricted isometry properties, Electron. J. Statist, vol.12, p.25, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01772538

B. Sadacca, A. Hamy-petit, C. Laurent, P. Gestraud, H. Bonsang-kitzis et al., New insight for pharmacogenetics studies from the transcriptional analysis of two large-scale cancer cell line panels, Scientific Reports, vol.7, p.15126, 2017.

T. Picchetti, J. Chiquet, M. Elati, P. Neuvial, R. Nicolle et al., A model for gene deregulation detection using expression data, BMC Systems Biology, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01154154

M. Pierre-jean, G. J. Rigaill, and P. Neuvial, Performance evaluation of DNA copy number segmentation methods, Briefings in Bioinformatics, vol.4, p.77, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00952896

I. Brito, P. Hupé, P. Neuvial, and E. Barillot, Stability-based comparison of class discovery methods for array-CGH profiles, PLoS One, vol.8, p.59, 2013.

P. , Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators, Journal of Machine Learning Research, vol.14, pp.30-34, 2013.

A. Chambaz, P. Neuvial, and M. J. Van-der-laan, Estimation of a Non-Parametric Variable Importance Measure of a Continuous Exposure, Electron. J. Statist, vol.6, pp.61-63, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00629899

L. Heiser, Subtype and pathway specific responses to anticancer compounds in breast cancer, Proceedings of the National Academy of Sciences, vol.109, issue.8, p.60, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01201568

L. Jacob, P. Neuvial, and S. Dudoit, More Power via Graph-Structured Tests for Differential Expression of Gene Networks, Annals of Applied Statistics, vol.6, pp.561-600, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00521097

P. Neuvial and E. Roquain, On false discovery rate thresholding for classification under sparsity, Annals of Statistics, vol.40, pp.37-39, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00604427

A. B. Olshen, H. Bengtsson, P. Neuvial, P. T. Spellman, R. A. Olshen et al., Parent-specific copy number in paired tumor-normal studies using circular binary segmentation, Bioinformatics, vol.27, p.82, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01200750

, Integrated Genomic Analyses of Ovarian Carcinoma, Nature, vol.474, p.60, 2011.

H. Noushmehr, Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma, Cancer Cell, vol.17, p.60, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01201573

M. A. Bollet, High-resolution mapping of DNA breakpoints to define true recurrences among ipsilateral breast cancers, J Natl Cancer Inst, vol.100, p.59, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00204833

P. , Asymptotic properties of false discovery rate controlling procedures under independence, Electron. J. Statist, vol.2, pp.27-32, 2008.

M. Elati, P. Neuvial, M. Bolotin-fukuhara, E. Barillot, F. Radvanyi et al., LICORN: LearnIng COoperative Regulation Networks, Bioinformatics, vol.23, p.58, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00192537

P. and L. Rosa, VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles, Bioinformatics, vol.22, p.59, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01199867

S. Liva, P. Hupé, P. Neuvial, I. Brito, E. Viara et al., CAPweb: a bioinformatics CGH array Analysis Platform, Nucleic Acids Res, vol.34, p.59, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01199870

P. Neuvial, P. Hupé, I. Brito, S. Liva, E. Manié et al., Spatial normalization of array-CGH data, BMC Bioinformatics, vol.7, issue.1, p.59, 2006.
URL : https://hal.archives-ouvertes.fr/inserm-00089910

M. Champion, J. Chiquet, P. Neuvial, M. Elati, and E. Birmelé, Identification of deregulated transcription factors involved in subtypes of cancers, Bioinformatics and Computational Biology, vol.59, p.58, 2020.
URL : https://hal.archives-ouvertes.fr/hal-01516892

P. , Vers une médecine personnalisée grâceà la recherche en génomique, Variances, vol.48, pp.31-33, 2013.

P. , Tests multiples en génomique, La gazette des mathématiciens 130, pp.71-76, 2011.

P. Neuvial and P. Bourguignon, Problématiques statistiquesà l'heure de la postgénomique, Variances, vol.35, pp.56-60, 2009.

P. , Contributionsà l'analyse statistique des données de pucesà ADN, p.28, 2008.

E. Hauvuy, B. Lebrave, and P. Neuvial, Analyse statistique du lien entre les plages homogènes de séquences d'ADN de différentes bactéries, 2003.

R. Elie, A. Frachot, P. Georges, and P. Neuvial, A Model of Prepayment for the French Residential Loan Market, 2002.

G. Blanchard, G. Durand, P. Neuvial, and E. Roquain, sansSouci: Post Hoc Multiple Testing Inference

M. Pierre-jean, G. Rigaill, and P. Neuvial, jointseg: Joint segmentation of multivariate (copy number) signals, vol.83, p.77

C. Ambroise, S. Chaturvedi, A. Dehman, P. Neuvial, G. Rigaill et al., adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix. R package version 0.5, vol.8, p.89, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02791370

M. Pierre-jean and P. , acnr: Annotated Copy-Number Regions

A. Chambaz and P. , Targeted Learning of a Non-Parametric Variable Importance Measure of a Continuous Exposure, vol.58, p.61

L. Jacob and P. , DEGraph: Two-sample tests on a graph

P. Neuvial and P. Hupé, MANOR: Micro-Array data NORmalization. Bioconductor R package version 1.58.0. 2006 (cit, p.59

A. Olshen, Analysis of Parent-Specific DNA Copy Numbers. R package, CRAN, vol.82, p.59, 2011.

M. Ortiz-estevez, CalMaTe: A post-calibration process to improve allele-specific copy number estimates from SNP microarrays. R package, CRAN, vol.80, p.59, 2011.

H. Bengtsson and P. Neuvial, cn: Analysis of copy-number estimates obtained from various platforms. R package, aroma-project, vol.79, p.59, 2010.

, Bengtsson and P. Neuvial. aroma.cn.eval: Evaluating copy-number estimates. R package, aroma-project, 2010.

P. and L. Rosa, Visualisation and Analysis of Molecular Profiles, p.59, 2006.

S. Liva, Copy Number Microarray Analysis Platform, p.59, 2006.

F. Bachoc, H. Leeb, and B. M. Pötscher, Valid confidence intervals for post-modelselection predictors, Annals of Statistics, p.24

F. Bachoc, D. Preinerstorfer, and L. Steinberger, Uniformly valid confidence intervals post-model-selection, Annals of Statistics

E. Katsevich and A. Ramdas, Simultaneous high-probability bounds on the false discovery proportion in structured, regression, and online settings, Annals of Statistics

A. K. Kuchibhotla, L. D. Brown, A. Buja, J. Cai, E. I. George et al., Valid Post-selection Inference in Model-free Linear Regression, the Annals to Statistics

A. Rinaldo, L. Wasserman, M. Sell, J. Lei, and R. Tibshirani, Bootstrapping and sample splitting for high-dimensional, assumption-free inference, Annals of Statistics, p.23

X. Tang, K. Li, and M. Ghosh, Properties of multiple testing procedures for Student's t distributions, Statistica Sinica, p.39, 2015.

J. Hemerik, A. Solari, and J. J. Goeman, Permutation-based simultaneous confidence bounds for the false discovery proportion, Biometrika, vol.106, issue.3, p.50, 2019.

T. P. Morris, I. R. White, and M. J. Crowther, Using simulation studies to evaluate statistical methods, Statistics in medicine, vol.38, p.101, 2019.

M. D. Robinson and O. Vitek, Benchmarking comes of age, p.101, 2019.

J. Tian and A. Ramdas, ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls, Advances in Neural Information Processing Systems, vol.32, p.98, 2019.

L. M. Weber, W. Saelens, R. Cannoodt, C. Soneson, A. Hapfelmeier et al., Essential guidelines for computational method benchmarking, Genome biology, vol.20, p.100, 2019.

H. Wickham, J. Hester, and W. Chang, devtools: Tools to Make Developing R Packages Easier, p.101

D. J. Wilson, The harmonic mean p-value for combining dependent tests, Proceedings of the National Academy of Sciences, vol.116, p.98, 2019.

J. Azaïs, Y. Castro, and S. Mourareau, Power of the spacing test for leastangle regression, Bernoulli, vol.24, p.23, 2018.

A. Boulesteix, H. Binder, M. Abrahamowicz, W. Sauerbrei, and S. P. Initiative, On the necessity and design of studies comparing statistical methods, Biometrical Journal, vol.60, issue.1, p.101, 2018.

A. Celisse, G. Marot, M. Pierre-jean, and G. Rigaill, New efficient algorithms for multiple change-point detection with reproducing kernels, Computational Statistics & Data Analysis, vol.128, p.60, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02621669

M. Chavent, V. Kuentz-simonet, A. Labenne, and J. Saracco, ClustGeo2: an R package for hierarchical clustering with spatial constraints, Computational Statistics, vol.33, p.91, 2018.

J. Hemerik and J. J. Goeman, Exact testing with random permutations, Test, vol.811, issue.825, p.17, 2018.

A. Javanmard and A. Montanari, Online rules for control of false discovery rate and false discovery exceedance, Ann. Statist, vol.46, issue.2, p.98, 2018.

S. Juggins and . Rioja, Analysis of Quaternary Science Data. R package version 0.9-15, vol.1, p.90, 2018.

D. Kivaranovic and H. Leeb, Expected length of post-model-selection confidence intervals conditional on polyhedral constraints, p.23, 2018.

R. J. Meijer, T. J. Krebs, and J. J. Goeman, Hommel's procedure in linear time, Biometrical Journal, p.43, 2018.

C. Truong, L. Oudre, and N. Vayatis, A review of change point detection methods, 2018.

M. J. Van-der-laan and S. Rose, Targeted learning in data science: causal inference for complex longitudinal studies, p.62, 2018.

H. Wickham, P. Danenberg, and M. Eugster, , vol.2, p.101, 2018.

A. Boulesteix, R. Wilson, and A. Hapfelmeier, Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies, BMC medical research methodology, vol.17, p.101, 2017.

A. Chatterjee, E. J. Rodger, I. M. Morison, M. R. Eccles, and P. A. Stockwell, Tools and strategies for analysis of genome-wide and gene-specific DNA methylation patterns, Methods in Molecular Biology, pp.249-277, 2017.

T. Strauss and M. J. Von-maltitz, Generalising Ward's method for use with Manhattan distances, PLoS ONE, vol.12, p.91, 2017.

S. Arlot, A. Celisse, and Z. Harchaoui, A kernel multiple change-point algorithm via model selection, p.60, 2016.
URL : https://hal.archives-ouvertes.fr/hal-00671174

S. Delattre and E. Roquain, On empirical distribution function of high-dimensional Gaussian vector components with an application to multiple testing, Bernoulli, vol.22, p.34, 2016.
URL : https://hal.archives-ouvertes.fr/hal-00739749

G. Durand, Multiple testing and post hoc bounds for heterogeneous data, p.25
URL : https://hal.archives-ouvertes.fr/tel-02374758

J. Gallier, Spectral theory of unsigned and signed graphs. applications to graph clustering: a survey, p.72, 2016.

C. Gawad, W. Koh, and S. R. Quake, Single-cell genome sequencing: current state of the science", Nature Reviews Genetics, vol.17, p.4, 2016.

P. Ghosh, X. Tang, M. Ghosh, and A. Chakrabarti, Asymptotic Properties of Bayes Risk of a General Class of Shrinkage Priors in Multiple Hypothesis Testing Under Sparsity, Bayesian Anal, vol.11, issue.3, p.39, 2016.

S. Goodwin, J. D. Mcpherson, and W. R. Mccombie, Coming of age: ten years of next-generation sequencing technologies, Nature Reviews Genetics, vol.17, issue.2, p.333, 2016.

J. D. Lee, D. L. Sun, Y. Sun, and J. E. Taylor, Exact post-selection inference, with application to the lasso, The Annals of Statistics, vol.44, p.23, 2016.

R. Nakato and K. Shirahige, Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation, Briefings in bioinformatics, vol.18, pp.279-290, 2016.

M. Pierre-jean, Development of statistical methods for DNA copy number analysis in cancerology, p.60
URL : https://hal.archives-ouvertes.fr/tel-01436012

J. E. Taylor, J. R. Loftus, and R. J. Tibshirani, Inference in adaptive regression via the Kac-Rice formula, Ann. Statist, vol.44, issue.2, p.23, 2016.

R. J. Tibshirani, J. Taylor, R. Lockhart, and R. Tibshirani, Exact Post-Selection Inference for Sequential Regression Procedures, Journal of the American Statistical Association, vol.111, p.23, 2016.

A. Boulesteix, V. Stierle, and A. Hapfelmeier, Publication bias in methodological computational research, CIN-S30747 (cit, vol.14, p.100, 2015.

J. Chiquet, Contributions to Sparse Methods for Complex Data Analysis". Habilitation thesis, p.5, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01288976

D. Clayton, snpStats: SnpMatrix and XSnpMatrix classes and methods, p.93

A. Dehman, Spatial clustering of linkage disequilibrium blocks for genome-wide association studies, p.60
URL : https://hal.archives-ouvertes.fr/tel-01288568

R. Dezeure, P. Bühlmann, L. Meier, and N. Meinshausen, High-dimensional inference: Confidence intervals, p-values and R-software hdi, Statistical science, vol.30, p.23, 2015.

C. Giraud, Introduction to high-dimensional statistics, Monographs on Statistics and Applied Probability, vol.139, 2015.

H. Leeb, B. M. Pötscher, and K. Ewald, On various confidence intervals postmodel-selection, Statistical Science, vol.30, p.23, 2015.

J. T. Leek and R. D. Peng, Statistics: P values are just the tip of the iceberg", Nature News, vol.520, p.100, 2015.

A. T. Lun and G. K. Smyth, diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data, BMC bioinformatics, vol.16, issue.1, p.99, 2015.

R. J. Meijer, T. J. Krebs, and J. J. Goeman, A region-based multiple testing method for hypotheses ordered in space or time, Statistical Applications in Genetics and Molecular Biology, vol.14, p.52, 2015.

S. Miyamoto, R. Abe, Y. Endo, and J. Takeshita, Ward method of hierarchical clustering for non-Euclidean similarity measures, Proceedings of the VIIth International Conference of Soft Computing and Pattern Recognition, 2015.

J. Fukuoka, , p.91, 2015.

J. A. Reuter, D. V. Spacek, and M. P. Snyder, High-throughput sequencing technologies, Molecular Cell, vol.58, p.59, 2015.

G. , A pruned dynamic programming algorithm to recover the best segmentations with 1 to K max change-points, Journal de la Société Française de Statistique, vol.156, pp.81-83, 2015.

E. Roquain, Contributions to multiple testing theory for high-dimensional data". Habilitation thesis, vol.14, p.11, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01203305

X. Tang, K. Li, and M. Ghosh, Bayesian Multiple Testing Under Sparsity for Polynomial-Tailed Distributions, p.39, 2015.

J. Taylor and R. J. Tibshirani, Statistical learning and selective inference, Proceedings of the National Academy of Sciences, vol.112, pp.7629-7634, 2015.

Y. Benjamini and M. Bogomolov, Selective inference on multiple families of hypotheses, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.76, p.22, 2014.

P. Bühlmann and J. Mandozzi, High-dimensional variable screening and bias in subsequent inference, with an empirical comparison, Computational Statistics, vol.29, p.23, 2014.

T. Dickhaus, Simultaneous statistical inference, AMC, vol.10, p.11, 2014.

J. J. Goeman and A. Solari, Multiple hypothesis testing in genomics, Statistics in medicine, vol.33, pp.1946-1978, 2014.

R. Lockhart, J. Taylor, R. J. Tibshirani, and R. Tibshirani, A significance test for the lasso, Ann. Statist, vol.42, issue.2, p.23, 2014.

A. T. Lun and G. K. Smyth, De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly, Nucleic acids research, vol.42, pp.95-95, 2014.

M. D. Robinson, A. Kahraman, C. W. Law, H. Lindsay, M. Nowicka et al., Statistical methods for detecting differentially methylated loci and regions, Frontiers in genetics, vol.5, p.324, 2014.

C. Zhang and S. S. Zhang, Confidence intervals for low dimensional parameters in high dimensional linear models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.76, p.25, 2014.

R. Berk, L. Brown, A. Buja, K. Zhang, and L. Zhao, Valid post-selection inference, The Annals of Statistics, vol.41, pp.22-25, 2013.

F. Frommlet and M. Bogdan, Some optimality properties of FDR controlling rules under sparsity, Electron. J. Statist, vol.7, p.39, 2013.

T. D. Hocking, G. Schleiermacher, I. Janoueix-lerosey, V. Boeva, J. Cappo et al., Learning smoothing models of copy number profiles using breakpoint annotations, BMC Bioinformatics, vol.14, p.59, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00663790

T. Hocking, G. Rigaill, J. Vert, and F. Bach, Learning sparse penalties for changepoint detection using max margin interval regression, International Conference on Machine Learning, p.82, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00824075

L. R. Jager and J. T. Leek, An estimate of the science-wise false discovery rate and application to the top medical literature, Biostatistics, vol.15, p.100, 2013.

K. Li, Bayesian multiple testing under sparsity for exponential distributions, p.39, 2013.

C. Xu, D. Tao, and C. Xu, A survey on multi-view learning, 2013.

J. Dixon, S. Selvaraj, F. Yue, A. Kim, Y. Li et al., Topological domains in mammalian genomes identified by analysis of chromatin interactions, Nature, vol.485, pp.376-380, 2012.

R. Killick, P. Fearnhead, and I. A. Eckley, Optimal detection of changepoints with a linear computational cost, Journal of the American Statistical Association, vol.107, p.82, 2012.

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, C. Keribin et al., A supervised clustering approach for fMRI-based inference of brain states, Pattern Recognition, vol.45, issue.6, p.90, 2012.
URL : https://hal.archives-ouvertes.fr/inria-00589201

D. Mosén-ansorena, A. Aransay, and N. Rodríguez-ezpeleta, Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data, BMC bioinformatics, vol.13, issue.1, p.83, 2012.

, Comprehensive molecular portraits of human breast tumours, Nature, vol.490, p.66, 2012.

K. Bleakley and J. Vert, The group fused Lasso for multiple change-point detection, vol.86, p.83, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00602121

M. Bogdan, A. Chakrabarti, F. Frommlet, and J. K. Ghosh, Asymptotic Bayes optimality under sparsity of some multiple testing procedures, The Annals of Statistics, vol.39, pp.35-37, 2011.

H. Chen, H. Xing, and N. R. Zhang, Estimation of parent specific DNA copy number in tumors using high-density genotyping arrays, PLoS Computational Biology, vol.7, issue.1, p.81, 2011.

S. Delattre and E. Roquain, On the false discovery proportion convergence under Gaussian equi-correlation, Statistics & Probability Letters, vol.81, issue.1, p.34, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00497134

J. Goeman and A. Solari, Multiple testing for exploratory research, Statistical Science, vol.26, pp.45-47, 2011.

D. Hanahan and R. A. Weinberg, Hallmarks of cancer: the next generation, p.3

K. D. Hansen, Z. Wu, R. A. Irizarry, and J. T. Leek, Sequencing technology does not eliminate biological variability, Nature biotechnology, vol.29, issue.6, p.572, 2011.

R. Louhimo and S. Hautaniemi, CNAmet: an R package for integrating copy number, methylation and expression data, Bioinformatics, vol.27, p.61, 2011.

M. Rasmussen, M. Sundström, H. Kultima, J. Botling, P. Micke et al., Allele-specific copy number analysis of tumor samples with aneuploidy and tumor heterogeneity, Genome Biology, vol.12, p.83, 2011.

E. Roquain, Type I error rate control in multiple testing: a survey with proofs, Journal de la Société Française de Statistique, vol.152, p.11, 2011.

Z. Sun, Integrated analysis of gene expression, CpG island methylation, and gene copy number in breast cancer cells by deep sequencing, PLoS One, vol.6, p.61, 2011.

M. J. Van-der-laan and S. Rose, Targeted Learning: Causal Inference for Observational and Experimental Data, p.62, 2011.

H. Wickham, testthat: Get Started with Testing, The R Journal, vol.3, p.101, 2011.

J. Andrews, Multi-Platform Whole-Genome Microarray Analyses Refine the Epigenetic Signature of Breast Cancer Metastasis with Gene Expression and Copy Number, PLoS ONE, vol.5, issue.1, p.61, 2010.

R. Bourgon, R. Gentleman, and W. Huber, Independent filtering increases detection power for high-throughput experiments, p.3, 2010.

S. X. Chen and Y. Qin, A two-sample test for high-dimensional data with applications to gene-set testing, Ann. Stat, vol.38, issue.2, p.74, 2010.

J. J. Goeman and A. Solari, The sequential rejection principle of familywise error control, The Annals of Statistics, vol.38, p.16, 2010.

Z. Harchaoui and C. Lévy-leduc, Multiple change-point estimation with a total variation penalty, Journal of the American Statistical Association, vol.105, p.83, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00923474

J. Vert and K. Bleakley, Fast detection of multiple change-points shared by many signals using group LARS, Advances in Neural Information Processing Systems, vol.23, p.83, 2010.

H. Finner, T. Dickhaus, and M. Roters, On the false discovery rate and an asymptotically optimal rejection curve, The Annals of Statistics, vol.37, p.29, 2009.

Y. Gavrilov, Y. Benjamini, and S. K. Sarkar, An adaptive step-down procedure with proven FDR control under independence, The Annals of Statistics, p.19, 2009.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2009.

N. Meinshausen, L. Meier, and P. Bühlmann, P-values for high-dimensional regression, Journal of the American Statistical Association, vol.104, p.23, 2009.

A. B. Tsybakov, Introduction to Nonparametric Estimation, vol.64, p.33, 2009.

Z. Chi and Z. Tan, Positive false discovery proportions: intrinsic bounds and adaptive control, Statistica Sinica, vol.18, pp.837-860, 2008.

L. Chin and J. W. Gray, Translating insights from the cancer genome into clinical practice, Nature, vol.452, p.3, 2008.

C. Dalmasso, Distinct genetic loci control plasma HIV-RNA and cellular HIV-DNA levels in HIV-1 infection: the ANRS Genome Wide Association 01 study, PLoS ONE, vol.3, p.3907, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02566809

S. Gey and E. Lebarbier, Using CART to Detect Multiple Change Points in the Mean for Large Sample, Statistics for Systems Biology research group, vol.86, p.83, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00327146

S. Guha, Y. Li, and D. Neuberg, Bayesian hidden Markov modeling of array CGH data, Journal of the American Statistical Association, vol.103, p.81, 2008.

T. L. Lai, H. Xing, and N. Zhang, Stochastic segmentation models for array-based comparative genomic hybridization data analysis, Biostat, vol.9, issue.2, p.81, 2008.

H. Leeb and B. M. Pötscher, Can one estimate the unconditional distribution of post-model-selection estimators?, Econometric Theory, vol.24, p.23, 2008.

S. K. Sarkar, On the Simes inequality and its generalization". Beyond parametrics in interdisciplinary research: Festschrift in honor of Professor Pranab K, Sen. Institute of Mathematical Statistics, pp.231-242, 2008.

M. S. Srivastava and M. Du, A test for the mean vector with fewer observations than the dimension, Journal of Multivariate Analysis, vol.99, p.74, 2008.

J. Staaf, D. Lindgren, J. Vallon-christersson, A. Isaksson, H. Göransson et al., Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays, Genome biology, vol.9, issue.9, p.80, 2008.

, Comprehensive genomic characterization defines human glioblastoma genes and core pathways, Nature, vol.455, p.64, 2008.

R. Tibshirani and P. Wang, Spatial smoothing and hot spot detection for CGH data using the fused lasso, Biostatistics, vol.9, p.83, 2008.

W. N. Van-wieringen and M. A. Van-de-wiel, Nonparametric Testing for DNA Copy Number Induced Differential mRNA Gene Expression, Biometrics, vol.5, issue.1, p.61, 2008.

W. B. Wu, On false discovery control under dependence, Ann. Statist, vol.36, issue.1, p.34, 2008.

Z. Chi, On the performance of FDR control: constraints and a partial solution, The Annals of Statistics, vol.35, pp.1409-1431, 2007.

F. S. Collins and A. D. Barker, Mapping the cancer genome, Scientific American, vol.296, p.64, 2007.

J. J. Goeman and P. Bühlmann, Analyzing gene expression data in terms of gene sets: methodological issues, Bioinformatics, vol.23, p.70, 2007.

Z. Harchaoui and O. Cappé, Retrospective mutiple change-point estimation with kernels, Proceedings of the 14th Workshop on Statistical Signal Processing (SSP'07)

W. I. Madison and . Usa, , p.60, 2007.

S. Purcell, B. Neale, K. Todd-brown, L. Thomas, M. A. Ferreira et al., PLINK: a tool set for wholegenome association and population-based linkage analyses, The American Journal of Human Genetics, vol.81, p.95, 2007.

M. J. Van-der-laan, E. C. Polley, and A. E. Hubbard, Article 25 (cit, Stat. Appl. Genet. Mol. Biol, vol.6, p.63, 2007.

E. S. Venkatraman and A. B. Olshen, A faster circular binary segmentation algorithm for the analysis of array CGH data, Bioinformatics, vol.23, p.82, 2007.

F. Abramovich, Y. Benjamini, D. L. Donoho, and I. M. Johnstone, Adapting to Unknown Sparsity by controlling the False Discovery Rate, Ann. Statist, vol.34, issue.2, pp.35-37, 2006.

Y. Benjamini, A. M. Krieger, and D. Yekutieli, Adaptive linear step-up procedures that control the false discovery rate, Biometrika, vol.93, issue.3, pp.491-507, 2006.

D. L. Donoho and J. Jin, Asymptotic minimaxity of false discovery rate thresholding for sparse exponential data, Ann. Statist, vol.34, issue.6, pp.35-37, 2006.

C. R. Genovese and L. Wasserman, Exceedance Control of the False Discovery Proportion, J. Amer. Statist. Assoc, vol.101, pp.1408-1417, 2006.

P. Kabaila and H. Leeb, On the large-sample minimal coverage probability of confidence intervals after model selection, Journal of the American Statistical Association, vol.101, issue.474, p.23, 2006.

H. Leeb and B. M. Pötscher, Can one estimate the conditional distribution of post-model-selection estimators, The Annals of Statistics, vol.34, issue.5, p.23, 2006.

E. L. Lehmann and J. P. Romano, Testing statistical hypotheses, p.11, 2006.

N. Meinshausen, False discovery control for multiple tests of association under general dependence, Scandinavian Journal of Statistics, vol.33, pp.227-237, 2006.

N. Meinshausen and J. Rice, Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses, Ann. Statist, vol.34, issue.1, p.45, 2006.

M. J. Van-der-laan and D. Rubin, Targeted maximum likelihood learning, Int. J. Biostat, vol.2, p.58, 2006.

Y. Benjamini and D. Yekutieli, False discovery rate-adjusted multiple confidence intervals for selected parameters, Journal of the American Statistical Association, vol.100, p.22, 2005.

S. Chiaretti, X. Li, R. Gentleman, A. Vitale, K. S. Wang et al., Gene expression profiles of B-lineage adult acute lymphocytic leukemia reveal genetic patterns that identify lineage derivation and distinct mechanisms of transformation, Clinical cancer research, vol.11, pp.7209-7219, 2005.

J. P. Ioannidis, Why most published research findings are false, PLoS medicine, vol.2, p.100, 2005.

W. R. Lai, M. D. Johnson, R. Kucherlapati, and P. J. Park, Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data, Bioinformatics, vol.21, p.83, 2005.

H. Leeb and B. M. Pötscher, Model selection and inference: Facts and fiction, Econometric Theory, vol.21, p.23, 2005.

E. L. Lehmann and J. P. Romano, Generalizations of the familywise error rate, Ann. Statist, vol.33, pp.1138-1154, 2005.

N. Meinshausen and P. Bühlmann, Lower bounds for the number of false null hypotheses for multiple testing of associations, Biometrika, vol.92, p.22, 2005.

F. Picard, S. Robin, M. Lavielle, C. Vaisse, and J. Daudin, A statistical approach for array-CGH data analysis, BMC Bioinformatics, vol.6, pp.1471-2105, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00427846

J. P. Romano and M. Wolf, Exact and approximate stepdown methods for multiple hypothesis testing, J. Amer. Statist. Assoc, vol.100, p.17, 2005.

J. P. Romano and M. Wolf, Exact and approximate stepdown methods for multiple hypothesis testing, Journal of the American Statistical Association, vol.100, p.49, 2005.

A. Subramanian, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proc Natl Acad Sci U S A, vol.102, p.70, 2005.

G. J. Székely and M. L. Rizzo, Hierarchical clustering via joint between-within distances: extending Ward's minimum variance method, Journal of Classification, vol.22, p.91, 2005.

R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, Sparsity and smoothness via the fused lasso, Journal of the Royal Statistical Society. Series B, Statistical Methodology, p.83, 2005.

H. Willenbrock and J. Fridlyand, A comparison study: applying segmentation to array-CGH data for downstream analyses, Bioinformatics, vol.21, p.83, 2005.

T. Beissbarth and T. P. Speed, GOstat: find statistically overrepresented Gene Ontologies within a group of genes, Bioinformatics, vol.20, issue.9, p.70, 2004.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals of statistics, vol.32, p.83, 2004.

J. Fridlyand, A. Snijders, D. Pinkel, D. G. Albertson, and A. N. Jain, Hidden Markov models approach to the analysis of array CGH data, Journal of Multivariate Analysis, vol.90, issue.1, p.81, 2004.

C. R. Genovese and L. Wasserman, A Stochastic Process Approach to False Discovery Control, The Annals of Statistics, vol.32, pp.1035-1061, 2004.

A. B. Olshen, E. S. Venkatraman, R. Lucito, and M. Wigler, Circular binary segmentation for the analysis of array-based DNA copy number data, Biostatistics, vol.5, p.82, 2004.

J. D. Storey, J. E. Taylor, and D. O. Siegmund, Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.66, pp.187-205, 2004.

M. J. Van-der-laan, S. Dudoit, and K. S. Pollard, Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives, Stat Appl Genet Mol Biol, vol.3, p.pp, 2004.

X. Cui and G. A. Churchill, Statistical tests for differential expression in cDNA microarray experiments, Genome Biol, vol.4, p.21, 2003.

J. D. Storey, The Positive False Discovery Rate: A Bayesian Interpretation and the q-value, The Annals of Statistics, vol.31, pp.2013-2035, 2003.

J. S. Yedidia, W. T. Freeman, and Y. Weiss, Understanding Belief Propagation and Its Generalizations, p.59, 2003.

G. Casella and R. L. Berger, Statistical inference, vol.2, p.12, 2002.

R. Edgar, M. Domrachev, and A. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository, Nucleic acids research, vol.30, p.84, 2002.

S. B. Gabriel, The structure of haplotype blocks in the human genome, Science, vol.296, p.89, 2002.

J. R. Pollack, Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors, Proc Natl Acad Sci U S A, vol.99, p.61, 2002.

J. D. Storey, A direct approach to false discovery rates, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.64, p.19, 2002.

Y. Benjamini and D. Yekutieli, The control of the false discovery rate in multiple testing under dependency, The Annals of Statistics, vol.29, pp.1165-1188, 2001.

B. Efron, R. J. Tibshirani, J. D. Storey, and V. G. Tusher, Empirical Bayes Analysis of a Microarray Experiment, Journal of the American Statistical Association, vol.96, p.28, 2001.

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, p.95, 2001.

D. Eppstein, Fast hierarchical clustering and other applications of dynamic closest pairs, Journal of Experimental Algorithmics (JEA), vol.5, p.92, 2000.

D. Hanahan and R. A. Weinberg, The hallmarks of cancer, Cell, vol.100, issue.1, p.3, 2000.

L. C. Evans, Partial Differential Equations (Graduate Studies in Mathematics

, GSM/19, p.72, 1998.

J. Fan and S. Lin, Test of Significance when data are curves, J. Am. Statist. Assoc, vol.93, p.74, 1998.

D. , High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays, Nat. Genet, vol.20, p.3, 1998.

A. W. Van-der and . Vaart, Cambridge Series in Statistical and Probabilistic Mathematics, vol.3, p.31, 1998.

A. W. Van-der and . Vaart, Cambridge Series in Statistical and Probabilistic Mathematics, vol.3, p.62, 1998.

Z. Bai and H. Saranadasa, Effect of high dimension : by an example of a two sample problem, Statistica Sinica, vol.6, p.71, 1996.

R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.58, pp.267-288, 1996.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.57, p.31, 1995.

P. H. Westfall and S. S. Young, Resampling-Based Multiple Testing, p.17, 1993.

P. Massart, The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality, The Annals of Probability, p.53, 1990.

R. R. Picard and K. N. Berk, Data splitting, The American Statistician, vol.44, p.23, 1990.

G. Hommel, A stagewise rejective multiple test procedure based on a modified Bonferroni test, Biometrika, vol.75, p.17, 1988.

E. Grimm, CONISS: a fortran 77 program for stratigraphically constrained analysis by the method of incremental sum of squares, Computers & Geosciences, vol.13, p.90, 1987.

R. J. Simes, An improved Bonferroni procedure for multiple tests of significance, Biometrika, vol.73, p.15, 1986.

D. B. Pollard, Convergence of Stochastic Processes, p.33, 1984.

G. Hommel, Tests of the overall hypothesis for arbitrary dependence structures, Biometrical J, vol.25, p.15, 1983.

S. Holm, A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics, vol.6, p.15, 1979.

L. Lebart, Programme d'agrégation avec contraintes, p.90

G. Schwarz, Estimating the dimension of a model, The annals of statistics, vol.6, p.82, 1978.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the royal statistical society. Series B (methodological, p.58, 1977.

R. Marcus, P. Eric, and K. R. Gabriel, On closed testing procedures with special reference to ordered analysis of variance, Biometrika, vol.63, p.16, 1976.

D. R. Cox, A note on data-splitting for the evaluation of significance levels, Biometrika, vol.62, p.23, 1975.

A. Sen and M. Srivastava, On tests for detecting change in mean, The Annals of Statistics, vol.3, p.82, 1975.

F. B. Baker, Stability of two hierarchical grouping techniques case I: sensitivity to data errors, Journal of the American Statistical Association, vol.69, p.94, 1974.

H. Akaike, Information Theory and an Extension of the Maximum Likelihood Principle, p.82, 1973.

Z. ?idák, Rectangular confidence regions for the means of multivariate normal distributions, Journal of the American Statistical Association, vol.62, p.16, 1967.

J. W. Williams, Algorithm 232 -heapsort, Communications of the ACM, vol.7, issue.6, p.93, 1964.

J. H. Ward, Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, vol.58, pp.236-244, 1963.

H. Scheffé, The analysis of variance, p.24, 1959.

A. Dvoretzky, J. Kiefer, and J. Wolfowitz, Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator, The Annals of Mathematical Statistics, p.53, 1956.

H. Scheffé, A method for judging all contrasts in the analysis of variance, Biometrika, vol.40, pp.87-110, 1953.

N. Aronszajn, Theory of reproducing kernels, Transactions of the American Mathematical Society, vol.68, p.91, 1950.