Spectral gene set enrichment (SGSE)

Motivation: Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. Results: We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracey-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Availability: http://cran.r-project.org/web/packages/PCGSE/index.html Contact: [email protected] or [email protected]

Statistical approaches of gene set analysis with quantitative trait loci for high-throughput genomic studies.

Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on gene ontology terms, known biological pathways, etc., which may not establish any formal relation between genotype and trait specific phenotype. Further, in plant biology and breeding, gene set analysis with trait specific Quantitative Trait Loci data are considered to be a great source for biological knowledge discovery. Therefore, innovative statistical approaches are developed for analyzing, and interpreting gene expression data from Microarrays, RNA-sequencing studies in the context of gene sets with trait specific Quantitative Trait Loci. The utility of the developed approaches is studied on multiple real gene expression datasets obtained from various Microarrays and RNA-sequencing studies. The selection of gene sets through differential expression analysis is the primary step of gene set analysis, and which can be achieved through using gene selection methods. The existing methods for such analysis in high-throughput studies, such as Microarrays, RNA-sequencing studies, suffer from serious limitations. For instance, in Microarrays, most of the available methods are either based on relevancy or redundancy measures. Through these methods, the ranking of genes is done on single Microarray expression data, which leads to the selection of spuriously associated, and redundant gene sets. Therefore, newer, and innovative differential expression analytical methods have been developed for Microarrays, and single-cell RNA-sequencing studies for identification of gene sets to successfully carry out the gene set and other downstream analyses. Furthermore, several methods specifically designed for single-cell data have been developed in the literature for the differential expression analysis. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to review the performance of the existing methods. Hence, a comprehensive overview, classification, and comparative study of the available single-cell methods is hereby undertaken to study their unique features, underlying statistical models and their shortcomings on real applications. Moreover, to address one of the shortcomings (i.e., higher dropout events due to lower cell capture rates), an improved statistical method for downstream analysis of single-cell data has been developed. From the users’ point of view, the different developed statistical methods are implemented in various software tools and made publicly available. These methods and tools will help the experimental biologists and genome researchers to analyze their experimental data more objectively and efficiently. Moreover, the limitations and shortcomings of the available methods are reported in this study, and these need to be addressed by statisticians and biologists collectively to develop efficient approaches. These new approaches will be able to analyze high-throughput genomic data more efficiently to better understand the biological systems and increase the specificity, sensitivity, utility, and relevance of high-throughput genomic studies

Analyse intégrative des données multi-dimensionnelles pour l'étude de la vaccination vis-à-vis des infections mammaires et pulmonaires chez les bovins

Dans le but de prévenir la sélection de bactéries résistantes aux antibiotiques, la volonté de réduire leur usage en médecine vétérinaire est un choix sociétal largement affiché. Le développement de méthodes alternatives telles que la vaccination est nécessaire. Dans l’espèce bovine, les infections mammaires et pulmonaires restent très présentes, et de nombreuses zones d’ombre subsistent sur la compréhension des mécanismes immunitaires de défense pour les prévenir. Cette thèse s’inscrit dans le cadre de l’étude de la vaccination à l’aide d’approches multidimensionnelles dans le double objectif de comprendre les mécanismes qu’elles mobilisent et d’élaborer de nouvelles méthodes vaccinales plus performantes. Les techniques dites de haut débit telles que la transcriptomique ou encore la protéomique ciblée sont de plus en plus utilisées dans les questions de recherche. En effet, leur utilisation permet d’identifier et d’étudier de façon large et sans a priori des mécanismes encore méconnus notamment dans le domaine de l’infectiologie. La stratégie vaccinale associant une immunisation par les voies systémique et locale permet une meilleure protection des vaches laitières vis-à-vis d’une infection expérimentale par E. coli, comparée à l’immunisation systémique seule. Pour la compréhension des mécanismes immunitaires protecteurs induits par la vaccination locale, une étude transcriptomique haut débit à l’aide du séquençage de l’ARN a été réalisée sur les cellules du sang, puis sur les lymphocytes CD4 extraits du tissu mammaire. En parallèle, une étude protéomique moyen débit via le dosage de cytokines par une méthode multiplexe utilisant des billes magnétiques a été conduite en parallèle. Une intégration de l’ensemble des données couplées à des informations sur l’état clinique après l’épreuve infectieuse a permis d’attribuer la protection induite par la vaccination à l’activité des lymphocytes producteurs d’interleukine 17 dans le tissu mammaire. La deuxième étude s’inscrit dans le cadre de l’étude de la vaccination contre les maladies respiratoires des bovins. Les effets d’un protocole de préparation des animaux incluant la vaccination ont été mesurés chez des jeunes bovins et un suivi de la réponse immunitaire, en parallèle des performances zootechniques et de la morbidité a été réalisé. Via l’utilisation de méthodes multidimensionnelles, les résultats montrent que la prévalence des signes cliniques et des lésions pulmonaires n’ont pas été efficacement prévenus par les interventions vaccinales. Des conditions d’hébergement défavorables ont un impact négatif sur la santé respiratoire malgré la vaccination. Ces travaux, outre la collection d’informations nouvelles sur la réponse immunitaire induite par la vaccination, ouvrent des perspectives sur de nouvelles modalités de prévention des deux principales maladies justifiant l’utilisation d’antibiotiques en élevage bovin

Прикладна фізика : українсько-російсько-англійський тлумачний словник. У 4 т. Т. 2. З – Н

Словник охоплює близько 30 тис. термінів з прикладної фізики і дотичних до неї галузей знань та їх тлумачення трьома мовами (українською, російською та англійською). Багато термінів і визначень, наведених у словнику, якими послуговуються у відповідній галузі знань, досі не входили до жодного зі спеціалізованих словників. Словник призначений для викладачів, науковців, інженерів, аспірантів, студентів вищих навчальних закладів, перекладачів з природничих і технічних дисциплін

Unsupervised gene set testing based on random matrix theory

Background: Gene set testing, or pathway analysis, is a bioinformatics technique that performs statistical testing on biologically meaningful sets of genomic variables. Although originally developed for supervised analyses, i.e., to test the association between gene sets and an outcome variable, gene set testing also has important unsupervised applications, e.g., p-value weighting. For unsupervised testing, however, few effective gene set testing methods are available with support especially poor for several biologically relevant use cases. Results: In this paper, we describe two new unsupervised gene set testing methods based on random matrix theory, the Mar. cenko-Pastur Distribution Test (MPDT) and the Tracy-Widom Test (TWT), that support both self-contained and competitive null hypotheses. For the self-contained case, we contrast our proposed tests with the classic multivariate test based on a modified likelihood ratio criterion. For the competitive case, we compare the new tests against a competitive version of the classic test and our recently developed Spectral Gene Set Enrichment (SGSE) method. Evaluation of the TWT and MPDT methods is based on both simulation studies and a weighted p-value analysis of two real gene expression data sets using gene sets drawn from MSigDB collections. Conclusions: The MPDT and TWT methods are novel and effective tools for unsupervised gene set analysis with superior statistical performance relative to existing techniques and the ability to generate biologically important results on real genomic data sets