Bourdieusian Reflections on Language: Unavoidable Conditions of the Real Speech Situation

The main purpose of this paper is to shed light on Pierre Bourdieu’s conception of language. Although he has dedicated a significant part of his work to the study of language and even though his analysis of language has been extensively discussed in the literature, almost no attention has been paid to the factthat Bourdieu’s account of language is based on a number of ontological presuppositions, that is, on a set of universal assumptions about the very nature of language. This article aims to fill this gap in the literature by offering a detailed overview of 10 key features which, from a Bourdieusian point of view, can be regarded as inherent in language. On the basis of this enquiry,the study seeks todemonstrate that——contraryto commonbelief——there is not only a Bourdieusian sociology of language but also a Bourdieusian philosophy of language, which provides a useful theoretical framework for examining the unavoidable conditions of the real speech situation. The paper draws to a close by reflecting on the flaws and limitations of Bourdieu’s approach to language

Role of the nuclear envelope in calcium signalling.: Nuclear envelope and calcium signalling

International audienceThe endoplasmic reticulum (ER) is the major Ca(2+) store inside the cell. Its organisation in specialised subdomains allows the local delivery of Ca(2+) to specific cell areas on stimulation. The nuclear envelope (NE), which is continuous with the ER, has a double role: it insulates the nucleoplasm from the cytoplasm and it stores Ca(2+) around the nucleus. Furthermore, all the constituents of the signalling cascade leading to Ca(2+) mobilisation are found in the NE; this allows the nuclear Ca(2+) to be regulated autonomously. On the other hand, cytosolic Ca(2+) transients can propagate within the nucleus via the nuclear pore complex. The variations in nuclear Ca(2+) concentration are important for controlling gene transcription and progression in the cell cycle. Recent data suggest that invaginations of the NE modify the morphology of the nucleus and may affect Ca(2+) dynamics in the nucleus and regulate transcriptional activity

PINTMF: PENALIZED INTEGRATIVE MATRIX FACTORIZATION METHOD FOR MULTI-OMICS DATA

It is more and more common to explore the genome at diverse levels and not only at a single omic level. Through integrative statistical methods, omics data have the power to reveal new biological processes, potential biomarkers, and subgroups of a cohort. The matrix factorization (MF) is a unsupervised statistical method that allows giving a clustering of individuals, but also revealing relevant omic variables from the various blocks. Here, we present PIntMF (Penalized Integrative Matrix Factorization), a model of MF with sparsity, positivity and equality constraints.To induce sparsity in the model, we use a classical Lasso penalization on variable and individual matrices. For the matrix of samples, sparsity helps for the clustering, and normalization (matching an equality constraint) of inferred coefficients is added for a better interpretation. Besides, we add an automatic tuning of the sparsity parameters using the famous glmnet package. We also proposed three criteria to help the user to choose the number of latent variables. PIntMF was compared to other state-of-the-art integrative methods including feature selection techniques in both synthetic and real data. PIntMF succeeds in finding relevant clusters as well as variables in two types of simulated data (correlated and uncorrelated). Then, PIntMF was applied to two real datasets (Diet and cancer), and it reveals interpretable clusters linked to available clinical data. Our method outperforms the existing ones on two criteria (clustering and variable selection). We show that PIntMF is an easy, fast, and powerful tool to extract patterns and cluster samples from multi-omics data

Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration

International audienceRecent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article

PIntMF : Une méthode de factorisation matricielle pénalisée pour l'intégration de données multi-omiques

International audienceThe generation of multi-omics data is growing with the improvement of high-throughput technologies. The integration in the same analysis of several levels of the genome could allow a better understanding of diseases or biological systems. Here, we propose a non-supervised penalized matrix factorization method to integrate multi-omics data which aims to identify new groups of individuals within the same disease, as well as relevant markers leading to this classification. We applied this method to simulated data and compared its performances with existing integrative unsupervised methods. Our method leads to a correct clustering of individuals and identifies relevant biomarkers with more precision. The results on real data highlight a new clustering linked to the patient's survival.La génération de données multi-omiques est en pleine expansion avec l'amélioration des tech-nologiesà haut débit. L'intégration au sein d'une seule analyse de plusieurs sources d'information du génome pourrait permettre une meilleure compréhension des maladies ou des systèmes biologiques. Nous proposons ici une méthode non-supervisée de factorisation matricielle pénalisée multi-blocs pour intégrer des données multi-omiques. Cette méthode a pour but d'identifier de nouveaux groupes d'individus au sein d'une même maladie ainsi que d'identifier les variables pertinentes conduisantà cette classification. Nous avons appliqué cette méthode sur des données simulées pour comparer ses performancesà des méthodes intégratives non-supervisées existantes et sur des données réelles. Cette nouvelle méthode permet de bien classer les individus et d'identifier les variables liées aux groupes avec plus de précision. Sur les données réelles, la méthode permet d'établir une nouvelle classification qui a un lien avec la survie des patients. Abstract. The generation of multi-omics data is growing with the improvement of high-throughput technologies. The integration in the same analysis of several levels of the genome could allow a better understanding of diseases or biological systems. Here, we propose a non-supervised penalized matrix factorization method to integrate multi-omics data which aims to identify new groups of individuals within the same disease , as well as relevant markers leading to this classification. We applied this method to simulated data and compared its performances with existing integrative unsupervised methods. Our method leads to a correct clustering of individuals and identifies relevant biomarkers with more precision. The results on real data highlight a new clustering linked to the patient's survival