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, pp.139-140, 2010.

M. I. Love, W. Huber, and S. Anders, Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2, Genome Biology, vol.15, issue.12, 2014.

C. W. Law, Y. Chen, W. Shi, and G. K. Smyth, Voom: Precision Weights Unlock Linear Model Analysis Tools for RNA-Seq Read Counts, Genome biology, vol.15, issue.2, pp.29-29, 2014.

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 (Methodological), pp.289-300, 1995.

Z. H. Zhang, D. J. Jhaveri, V. M. Marshall, D. C. Bauer, J. Edson et al., A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data, PLOS ONE, vol.9, issue.8, p.103207, 2014.

M. Tang, J. Sun, K. Shimizu, and K. Kadota, Evaluation of Methods for Differential Expression Analysis on Multi-Group RNA-Seq Count Data, BMC Bioinformatics, vol.16, issue.1, pp.1-14, 2015.

F. Seyednasrollah, A. Laiho, and L. L. Elo, Comparison of Software Packages for Detecting Differential Expression in RNA-Seq Studies, Briefings in Bioinformatics, vol.16, issue.1, pp.59-70, 2015.

J. Costa-silva, D. Domingues, and F. M. Lopes, RNA-Seq Differential Expression Analysis: An Extended Review and a Software Tool, PLOS ONE, vol.12, issue.12, p.190152, 2017.

S. Lamarre, P. Frasse, M. Zouine, D. Labourdette, E. Sainderichin et al., Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size, Frontiers in Plant Science, vol.9, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01849534

P. P. Labaj and D. P. Kreil, Sensitivity, Specificity, and Reproducibility of RNASeq Differential Expression Calls, Biology Direct, vol.11, issue.1, p.66, 2016.

G. Mazzoni, L. Kogelman, P. Suravajhala, and H. N. Kadarmideen, Systems Genetics of Complex Diseases Using RNA-Sequencing Methods, International Journal of Bioscience, vol.5, issue.4, pp.264-279, 2015.

, Excess False Positive Rates in Methods for Differential Gene Expression Analysis Using RNA-Seq Data

P. L. Germain, A. Vitriolo, A. Adamo, P. Laise, V. Das et al., RNAontheBENCH: Computational and empirical resources for benchmarking RNAseq quantification and differential expression methods, Nucleic Acids Research, vol.44, issue.11, pp.5054-5067, 2016.

G. Rigaill, S. Balzergue, V. Brunaud, E. Blondet, A. Rau et al., Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis, Briefings in bioinformatics, vol.092, pp.1-12, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01595551

A. T. Assefa, K. De-paepe, C. Everaert, P. Mestdagh, O. Thas et al., Differential Gene Expression Analysis Tools Exhibit Substandard Performance for Long Non-Coding RNA-Sequencing Data, Genome Biology, vol.19, issue.1, p.96, 2018.

A. Singhania, R. Verma, C. M. Graham, J. Lee, T. Tran et al., A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection, Nature communications, vol.9, issue.1, p.2308, 2018.

A. Conesa, P. Madrigal, S. Tarazona, D. Gomez-cabrero, A. Cervera et al., A Survey of Best Practices for RNA-Seq Data Analysis

, Genome Biol, vol.17, issue.1, pp.13-13, 2016.

. Seqc/maqc-iii and . Consortium, A Comprehensive Assessment of RNASeq Accuracy, Reproducibility and Information Content by the Sequencing Quality Control Consortium, Nature Biotechnology, vol.32, issue.9, pp.903-917, 2014.

M. P. Berry, C. M. Graham, F. W. Mcnab, Z. Xu, S. A. Bloch et al., An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis, Nature, vol.466, issue.7309, p.973, 2010.

G. W. Brier, Verification of forecasts expressed in terms of probability. Monthey Weather Review, vol.78, pp.1-3, 1950.

B. Phipson and G. K. Smyth, Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Statistical applications in genetics and molecular biology, vol.9, 2010.

C. J. Burden, S. E. Qureshi, and S. R. Wilson, Error Estimates for the Analysis of Differential Expression from RNA-Seq Count Data, vol.2, p.576, 2014.

W. Yang, P. C. Rosenstiel, and H. Schulenburg, ABSSeq: A New RNA-Seq Analysis Method Based on Modelling Absolute Expression Differences, BMC Genomics, vol.17, issue.1, p.541, 2016.

L. León-novelo, C. Fuentes, and S. Emerson, Marginal Likelihood Estimation of Negative Binomial Parameters with Applications to RNA-Seq Data, Biostatistics, vol.18, issue.4, pp.637-650, 2017.

R. Patro, G. Duggal, M. I. Love, R. A. Irizarry, and C. Kingsford, Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression, Nature Methods, vol.14, issue.4, pp.417-419, 2017.

N. L. Bray, H. Pimentel, P. Melsted, and L. Pachter, Near-Optimal Probabilistic RNA-Seq Quantification, Nature Biotechnology, vol.34, issue.5, pp.525-527, 2016.

D. Agniel and B. P. Hejblum, Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, vol.18, issue.4, pp.589-604, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01579077

X. Lin, Variance Component Testing in Generalised Linear Models with Random Effects, vol.84, pp.309-326

Y. T. Huang and X. Lin, Gene Set Analysis Using Variance Component Tests, vol.14, pp.210-210

D. Agniel and B. Hejblum, Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, p.5, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01579077

B. P. Hejblum, J. Skinner, and R. Thiébaut, Time-Course Gene Set Analysis for Longitudinal Gene Expression Data, PLOS Computational Biology, vol.11, issue.6, p.1004310, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01203446

D. Agniel, W. Xie, M. Essex, and T. Cai, Functional Principal Variance Component Testing for a Genetic Association Study of HIV Progression, vol.12, pp.1871-1893

J. J. Goeman, S. A. Van-de-geer, and H. C. Van-houwelingen, Testing against a High Dimensional Alternative, Journal of the Royal Statistical Society Series B-Statistical Methodology, vol.68, pp.477-493, 2006.

L. Wasserman, All of Nonparametric Statistics. Springer Texts in Statistics, 2006.