Cette thèse regroupe des contributions méthodologiques à l'analyse statistique des données issues des technologies de séquençage du transcriptome (RNA-seq). Les difficultés de modélisation des données de comptage RNA-seq sont liées à leur caractère discret et au faible nombre d'échantillons disponibles, limité par le coût financier du séquençage. Une première partie de travaux de cette thèse porte sur la classification à l'aide de modèle de mélange. L'objectif de la classification est la détection de modules de gènes co-exprimés. Un choix naturel de modélisation des données RNA-seq est un modèle de mélange de lois de Poisson. Mais des transformations simples des données permettent de se ramener à un modèle de mélange de lois gaussiennes. Nous proposons de comparer, pour chaque jeu de données RNA-seq, les différentes modélisations à l'aide d'un critère objectif permettant de sélectionner la modélisation la plus adaptée aux données. Par ailleurs, nous présentons un critère de sélection de modèle prenant en compte des informations biologiques externes sur les gènes. Ce critère facilite l'obtention de classes biologiquement interprétables. Il n'est pas spécifique aux données RNA-seq. Il est utile à toute analyse de co-expression à l'aide de modèles de mélange visant à enrichir les bases de données d'annotations fonctionnelles des gènes. Une seconde partie de travaux de cette thèse porte sur l'inférence de réseau à l'aide d'un modèle graphique. L'objectif de l'inférence de réseau est la détection des relations de dépendance entre les niveaux d'expression des gènes. Nous proposons un modèle d'inférence de réseau basé sur des lois de Poisson, prenant en compte le caractère discret et la grande variabilité inter-échantillons des données RNA-seq. Cependant, les méthodes d'inférence de réseau nécessitent un nombre d'échantillons élevé.Dans le cadre du modèle graphique gaussien, modèle concurrent au précédent, nous présentons une approche non-asymptotique pour sélectionner des sous-ensembles de gènes pertinents, en décomposant la matrice variance en blocs diagonaux. Cette méthode n'est pas spécifique aux données RNA-seq et permet de réduire la dimension de tout problème d'inférence de réseau basé sur le modèle graphique gaussien. / This thesis gathers methodologicals contributions to the statistical analysis of next-generation high-throughput transcriptome sequencing data (RNA-seq). RNA-seq data are discrete and the number of samples sequenced is usually small due to the cost of the technology. These two points are the main statistical challenges for modelling RNA-seq data.The first part of the thesis is dedicated to the co-expression analysis of RNA-seq data using model-based clustering. A natural model for discrete RNA-seq data is a Poisson mixture model. However, a Gaussian mixture model in conjunction with a simple transformation applied to the data is a reasonable alternative. We propose to compare the two alternatives using a data-driven criterion to select the model that best fits each dataset. In addition, we present a model selection criterion to take into account external gene annotations. This model selection criterion is not specific to RNA-seq data. It is useful in any co-expression analysis using model-based clustering designed to enrich functional annotation databases.The second part of the thesis is dedicated to network inference using graphical models. The aim of network inference is to detect relationships among genes based on their expression. We propose a network inference model based on a Poisson distribution taking into account the discrete nature and high inter sample variability of RNA-seq data. However, network inference methods require a large number of samples. For Gaussian graphical models, we propose a non-asymptotic approach to detect relevant subsets of genes based on a block-diagonale decomposition of the covariance matrix. This method is not specific to RNA-seq data and reduces the dimension of any network inference problem based on the Gaussian graphical model.

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The rapid development of biotechnology has enabled researchers to collect high-throughput data for studying various biological processes at the genomic level, transcriptomic level, and proteomic level. Due to the large noise in the data and the high complexity of diseases (such as cancer), it is a challenging task for researchers to extract biologically meaningful information that can help reveal the underlying molecular mechanisms. The challenges call for more efforts in developing efficient and effective computational methods to analyze the data at different levels so as to understand the biological systems in different aspects. In this dissertation research, we have developed novel Bayesian approaches to infer alternative splicing mechanisms in biological systems using RNA sequencing data. Specifically, we focus on two research topics in this dissertation: isoform identification and phenotype-specific transcript assembly. For isoform identification, we develop a computational approach, SparseIso, to jointly model the existence and abundance of isoforms in a Bayesian framework. A spike-and-slab prior is incorporated into the model to enforce the sparsity of expressed isoforms. A Gibbs sampler is developed to sample the existence and abundance of isoforms iteratively. For transcript assembly, we develop a Bayesian approach, IntAPT, to assemble phenotype-specific transcripts from multiple RNA sequencing profiles. A two-layer Bayesian framework is used to model the existence of phenotype-specific transcripts and the transcript abundance in individual samples. Based on the hierarchical Bayesian model, a Gibbs sampling algorithm is developed to estimate the joint posterior distribution for phenotype-specific transcript assembly. The performances of our proposed methods are evaluated with simulation data, compared with existing methods and benchmarked with real cell line data. We then apply our methods on breast cancer data to identify biologically meaningful splicing mechanisms associated with breast cancer. For the further work, we will extend our methods for de novo transcript assembly to identify novel isoforms in biological systems; we will incorporate isoform-specific networks into our methods to better understand splicing mechanisms in biological systems. / Ph. D.

Computational systems biology is an inter-disciplinary field that aims to develop computational approaches for a system-level understanding of biological systems. Advances in high-throughput biotechnology offer broad scope and high resolution in multiple disciplines. However, it is still a major challenge to extract biologically meaningful information from the overwhelming amount of data generated from biological systems. Effective computational approaches are of pressing need to reveal the functional components. Thus, in this dissertation work, we aim to develop computational approaches for differential analysis of RNA-seq and methylation data to detect aberrant events associated with cancers. We develop a novel Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. BayesIso features a joint model of the variability of RNA-seq data and the differential state of isoforms. BayesIso can not only account for the variability of RNA-seq data but also combines the differential states of isoforms as hidden variables for differential analysis. The differential states of isoforms are estimated jointly with other model parameters through a sampling process, providing an improved performance in detecting isoforms of less differentially expressed. We propose to develop a novel probabilistic approach, DM-BLD, in a Bayesian framework to identify differentially methylated genes. The DM-BLD approach features a hierarchical model, built upon Markov random field models, to capture both the local dependency of measured loci and the dependency of methylation change. A Gibbs sampling procedure is designed to estimate the posterior distribution of the methylation change of CpG sites. Then, the differential methylation score of a gene is calculated from the estimated methylation changes of the involved CpG sites and the significance of genes is assessed by permutation-based statistical tests. We have demonstrated the advantage of the proposed Bayesian approaches over conventional methods for differential analysis of RNA-seq data and methylation data. The joint estimation of the posterior distributions of the variables and model parameters using sampling procedure has demonstrated the advantage in detecting isoforms or methylated genes of less differential. The applications to breast cancer data shed light on understanding the molecular mechanisms underlying breast cancer recurrence, aiming to identify new molecular targets for breast cancer treatment. / Ph. D.

RNA-Seq is an increasingly popular approach to transcriptome profiling that uses the capabilities of next generation sequencing technologies and provides better measurement of levels of transcripts and their isoforms. In this thesis, we apply RNA-Seq protocol and transcriptome quantification to estimate gene expression and pathway activity levels. We present a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. In the first version of IsoDE, we compared the tool against four existing methods: Fisher's exact test, GFOLD, edgeR and Cuffdiff on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. We also introduce the second version of IsoDE which runs 10 times faster than the first implementation due to some in-memory processing applied to the underlying gene expression frequencies estimation tool and we also perform more optimization on the analysis. The second part of this thesis presents a set of tools to differentially analyze metabolic pathways from RNA-Seq data. Metabolic pathways are series of chemical reactions occurring within a cell. We focus on two main problems in metabolic pathways differential analysis, namely, differential analysis of their inferred activity level and of their estimated abundance. We validate our approaches through differential expression analysis at the transcripts and genes levels and also through real-time quantitative PCR experiments. In part Four, we present the different packages created or updated in the course of this study. We conclude with our future work plans for further improving IsoDE 2.0.