Inférence de réseaux causaux à partir de données interventionnelles

The purpose of this thesis is the use of current transcriptomic data in order to infer a gene regulatory network. These data are often complex, and in particular intervention data may be present. The use of causality theory makes it possible to use these interventions to obtain acyclic causal networks. I question the notion of acyclicity, then based on this theory, I propose several algorithms and / or improvements to current techniques to use this type of data.L'objet de cette thèse est l'utilisation de données transcriptomiques actuelles dans le but d'en inférer un réseau de régulation génique. Ces données sont souvent complexes, et en particulier des données d'interventions peuvent être présente. L'utilisation de la théorie de la causalité permet d'utiliser ces interventions afin d'obtenir des réseaux causaux acycliques. Je questionne la notion d'acyclicité, puis en m'appuyant sur cette théorie, je propose plusieurs algorithmes et/ou améliorations à des techniques actuelles permettant d'utiliser ce type de données particulières

Causal network inference from intervention data

L'objet de cette thèse est l'utilisation de données transcriptomiques actuelles dans le but d'en inférer un réseau de régulation génique. Ces données sont souvent complexes, et en particulier des données d'interventions peuvent être présente. L'utilisation de la théorie de la causalité permet d'utiliser ces interventions afin d'obtenir des réseaux causaux acycliques. Je questionne la notion d'acyclicité, puis en m'appuyant sur cette théorie, je propose plusieurs algorithmes et/ou améliorations à des techniques actuelles permettant d'utiliser ce type de données particulières.The purpose of this thesis is the use of current transcriptomic data in order to infer a gene regulatory network. These data are often complex, and in particular intervention data may be present. The use of causality theory makes it possible to use these interventions to obtain acyclic causal networks. I question the notion of acyclicity, then based on this theory, I propose several algorithms and / or improvements to current techniques to use this type of data

Identification of marginal causal relationships in gene networks from observational and interventional expression data

Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small number of genes, due to the large number of parameters to be estimated and the limited number of biological replicates available. In this work, we consider the specific case of transcriptomic studies made up of both observational and interventional data in which a single gene of biological interest is knocked out. We focus on a marginal causal estimation approach, based on the framework of Gaussian directed acyclic graphs, to infer causal relationships between the knocked-out gene and a large set of other genes. In a simulation study, we found that our proposed method accurately differentiates between downstream causal relationships and those that are upstream or simply associative. It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub)

Le sentiment linguistique chez Saussure

« Tout ce qui est dans le sentiment des sujets parlants est phénomène réel », écrivait Saussure dans les années 1880 en vue d’un possible cours de morphologie. Mais le terme sentiment ne fait pas partie de ceux, tels signe, système, synchronie ou diachronie, qu’on associe au canon des concepts saussuriens. Le projet de cet ouvrage est de montrer au contraire que le sentiment linguistique occupe une place essentielle dans la pensée du linguiste genevois, et qu’il est peut-être l’instance principale qui lui permet de définir ce qu’il appelle la « langue ». Les contributions de cet ouvrage mènent l’enquête autour de ce qu’on peut appeler le sentiment linguistique chez Saussure, en explorant les inspirations que Saussure a pu prendre chez ses prédécesseurs, étudient les diverses inflexions que le motif prend chez lui, notamment à partir des sources manuscrites, et explorent les enjeux de la notion aujourd’hui. Prenant place dans le champ de l’histoire des idées linguistiques, il est aussi susceptible d’ouvrir de nouvelles pistes de recherche sur l’appréhension des faits linguistiques par le sujet parlant.“Everything in the feeling of speakers is a real phenomenon,” wrote Saussure in the 1880s for the purpose of a prospective morphology course. But the term feeling is not one of those which, like sign, system, synchrony and diachrony, is associated with the canon of Saussure's concepts. The purpose of this work is to illustrate, on the contrary, that linguistic feeling is essential to the Geneva linguist’s reasoning, and that it is perhaps the central focus which enables him to define what he calls “language”. The contributions in this work investigate what can be called Saussure's linguistic feeling, by exploring the inspirations which Saussure may have picked up from his predecessors, by studying the various inflections which the motif embraces in his texts, particularly from manuscript sources, and by exploring the issues of the notion today. As it is rooted in the sphere of the history of linguistic ideas, it also has the potential to open up new avenues for research on the apprehension of linguistic facts by the speaker

Genome-wide transcriptomic analysis of liver in sex-linked dwarf and wild-type chickens

Genome-wide transcriptomic analysis of liver in sex-linked dwarf and wild-type chickens. 35. International Society for Animal Genetics Conferenc

Models given observational or interventional data.

<p>Graphical representation of the <i>M</i>₁ (downstream) and <i>M</i>₀ (upstream or correlated) models under observational and interventional data.</p

Illustration of upstream and downstream causality.

<p>Nodes <i>X</i>₀ and <i>X</i>₁ are both upstream causally related to knocked-out gene <i>G</i>, while nodes <i>X</i>₂ and <i>X</i>₃ are both downstream causally related to <i>G</i>.</p