Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single omic data can provide useful information about the underlying biological processes, multi-omics data integration would be much more comprehensive about the cause-effect processes responsible for diseases and their subtypes. This project investigates the combination of miRNAseq, proteomics, and RNAseq data on seven types of muscular dystrophies and control group. These unique multi-omics data sets provide us with the opportunity to identify disease-specific and most relevant biological pathways. We first perform t-test and OVEPUG test separately to define the differential expressed genes in protein and mRNA data sets. In multi-omics data sets, miRNA also plays a significant role in muscle development by regulating their target genes in mRNA dataset. To exploit the relationship between miRNA and gene expression, we consult with the commonly used gene library - Targetscan to collect all paired miRNA-mRNA and miRNA-protein co-expression pairs. Next, by conducting statistical analysis such as Pearson's correlation coefficient or t-test, we measured the biologically expected correlation of each gene with its upstream miRNAs and identify those showing negative correlation between the aforementioned miRNA-mRNA and miRNA-protein pairs. Furthermore, we identify and assess the most relevant disease-specific pathways by inputting the differential expressed genes and negative correlated genes into the gene-set libraries respectively, and further characterize these prioritized marker subsets using IPA (Ingenuity Pathway Analysis) or KEGG. We will then use Fisher method to combine all these p-values derived from separate gene sets into a joint significance test assessing common pathway relevance. In conclusion, we will find all negative correlated paired miRNA-mRNA and miRNA-protein, and identifying several pathophysiological pathways related to muscular dystrophies by gene set enrichment analysis. This novel multi-omics data integration study and subsequent pathway identification will shed new light on pathophysiological processes in muscular dystrophies and improve our understanding on the molecular pathophysiology of muscle disorders, preventing and treating disease, and make people become healthier in the long term. / Master of Science

Depuis plusieurs années, des projets d'intégration de données omiques se sont développés, notamment avec objectif de participer à la description fine de caractères complexes d'intérêt socio-économique. Dans ce contexte, l'objectif de cette thèse est de combiner différentes données omiques hétérogènes afin de mieux décrire et comprendre le dernier tiers de gestation chez le porc, période influençant la mortinatalité porcine. Durant cette thèse, nous avons identifié les bases moléculaires et cellulaires sous-jacentes de la fin de gestation, en particulier au niveau du muscle squelettique. Ce tissu est en effet déterminant à la naissance car impliqué dans l'efficacité de plusieurs fonctions physiologiques comme la thermorégulation et la capacité à se déplacer. Au niveau du plan expérimental, les tissus analysés proviennent de foetus prélevés à 90 et 110 jours de gestation (naissance à 114 jours), issus de deux lignées extrêmes pour la mortalité à la naissance, Large White et Meishan, et des deux croisements réciproques. Au travers l'application de plusieurs études statistiques et computationnelles (analyses multidimensionnelles, inférence de réseaux, clustering et intégration de données), nous avons montré l'existence de mécanismes biologiques régulant la maturité musculaire chez les porcelets, mais également chez d'autres espèces d'intérêt agronomique (bovin et mouton). Quelques gènes et protéines ont été identifiées comme étant fortement liées à la mise en place du métabolisme énergétique musculaire durant le dernier tiers de gestation. Les porcelets ayant une immaturité du métabolisme musculaire seraient sujets à un plus fort risque de mortalité à la naissance. Un second volet de cette thèse concerne l'imputation de données manquantes (tout un groupe de variables pour un individu) dans les méthodes d'analyses multidimensionnelles, comme l'analyse factorielle multiple (AFM) (ou multiple factor analysis (MFA)). Dans notre contexte, l'AFM fut particulièrement intéressante pour l'intégration de données d'un ensemble d'individus sur différents tissus (deux ou plus). Afin de conserver ces individus manquants pour tout un groupe de variables, nous avons développé une méthode, appelée MI-MFA (multiple imputation - MFA), permettant l'estimation des composantes de l'AFM pour ces individus manquants. / Over the last decades, some omics data integration studies have been developed to participate in the detailed description of complex traits with socio-economic interests. In this context, the aim of the thesis is to combine different heterogeneous omics data to better describe and understand the last third of gestation in pigs, period influencing the piglet mortality at birth. In the thesis, we better defined the molecular and cellular basis underlying the end of gestation, with a focus on the skeletal muscle. This tissue is specially involved in the efficiency of several physiological functions, such as thermoregulation and motor functions. According to the experimental design, tissues were collected at two days of gestation (90 or 110 days of gestation) from four fetal genotypes. These genotypes consisted in two extreme breeds for mortality at birth (Meishan and Large White) and two reciprocal crosses. Through statistical and computational analyses (descriptive analyses, network inference, clustering and biological data integration), we highlighted some biological mechanisms regulating the maturation process in pigs, but also in other livestock species (cattle and sheep). Some genes and proteins were identified as being highly involved in the muscle energy metabolism. Piglets with a muscular metabolism immaturity would be associated with a higher risk of mortality at birth. A second aspect of the thesis was the imputation of missing individual row values in the multidimensional statistical method framework, such as the multiple factor analysis (MFA). In our context, MFA was particularly interesting in integrating data coming from the same individuals on different tissues (two or more). To avoid missing individual row values, we developed a method, called MI-MFA (multiple imputation - MFA), allowing the estimation of the MFA components for these missing individuals.