Multiple kernel self-organizing maps

International audienceIn a number of real-life applications, the user is interested in analyzing several sources of information together: a graph combined with the additional information known on its nodes, numerical variables measured on individuals and factors describing these individuals... The combination of all sources of information can help him to understand the dataset in its whole better. The present article focuses on such an issue, by using self-organizing maps. The use a kernel version of the algorithm allows us to combine various types of information and automatically tune the data combination. This approach is illustrated on a simulated example

Carte auto-organisatrice pour graphes étiquetés.

National audienceDans de nombreux cas d'études concrets, l'analyse de données sur les graphes n'est pas limitée à la seule connaissance du graphe. Il est courant que des informations supplémentaires soient disponibles sur les sommets et que l'utilisateur souhaite combiner ces informations à la structure du graphe lui-même pour comprendre l'intégralité des données en sa possession. C'est ce problème que nous souhaitons aborder dans cet article, en nous focalisant sur une méthode de fouille de données qui combine classification (non supervisée) et visualisation : les cartes auto-organisatrices. Nous expliquons comment l'utilisation de méthodes à noyaux permet de combiner de manière efficace des informations de natures diverses (graphe, variables numériques, facteurs, variables textuelles...) pour décortiquer la structure des données et en offrir une représentation simplifiée. Notre approche est illustrée sur divers exemples : un premier exemple, sur des données simulées, permet de comprendre comment se comporte l'algorithme. Un second exemple illustre la méthode sur un graphe réel de plusieurs centaines de sommets, qui modélise un corpus de documents médiévaux

Linkage disequilibrium interval mapping of quantitative trait loci

BACKGROUND: For many years gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of gene location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods. RESULTS: Under a three-locus Wright-Fisher model, we derived approximate expressions for the expected haplotype frequencies in a population. We considered haplotypes comprising one trait locus and two flanking markers. Using these theoretical expressions, we built a likelihood-maximization method, called HAPim, for estimating the location of a quantitative trait locus. For each postulated position, the method only requires information from the two flanking markers. Over a wide range of simulation scenarios it was found to be more accurate than a two-marker composite likelihood method. It also performed as well as identity by descent methods, whilst being valuable in a wider range of populations. CONCLUSION: Our method makes efficient use of marker information, and can be valuable for fine mapping purposes. Its performance is increased if multiallelic markers are available. Several improvements can be developed to account for more complex evolution scenarios or provide robust confidence intervals for the location estimates

Classification non supervisée d'un graphe de co-expression avec des méta-données pour la détection de micro-ARNs

National audienceNous présentons dans cet article une méthode de classification non supervisée de sommets d'un graphe qui est utilisée dans un contexte biologique particulier. La problématique est de détecter de manière non supervisée des micro-ARNs probables. Pour ce faire, nous utilisons une approche multi-noyaux permettant d'intégrer des informations sur le graphe de co-expression et des informations supplémentaires sur les sommets de ce graphe. Cette approche est rendue robuste par une technique de bagging de classifications. Les résultats obtenus donnent des groupes de miRNAs potentiels dont certains permettent de discriminer avec une bonne confiance les vrais miRNAs des faux positifs

Linkage disequilibrium fine mapping of quantitative trait loci: A simulation study

Recently, the use of linkage disequilibrium (LD) to locate genes which affect quantitative traits (QTL) has received an increasing interest, but the plausibility of fine mapping using linkage disequilibrium techniques for QTL has not been well studied. The main objectives of this work were to (1) measure the extent and pattern of LD between a putative QTL and nearby markers in finite populations and (2) investigate the usefulness of LD in fine mapping QTL in simulated populations using a dense map of multiallelic or biallelic marker loci. The test of association between a marker and QTL and the power of the test were calculated based on single-marker regression analysis. The results show the presence of substantial linkage disequilibrium with closely linked marker loci after 100 to 200 generations of random mating. Although the power to test the association with a frequent QTL of large effect was satisfactory, the power was low for the QTL with a small effect and/or low frequency. More powerful, multi-locus methods may be required to map low frequent QTL with small genetic effects, as well as combining both linkage and linkage disequilibrium information. The results also showed that multiallelic markers are more useful than biallelic markers to detect linkage disequilibrium and association at an equal distance

Does probabilistic modelling of linkage disequilibrium evolution improve the accuracy of QTL location in animal pedigree?

peer reviewe

Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis

Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth “Dialogue for Reverse Engineering Assessments and Methods” (DREAM5) challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on “Systems Genetics” proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics

Bounds and asymptotic expansions for the distribution of the maximum of a smooth stationary gaussian process

National audienc

Bounds and asymptotic expansions for the distribution of the Maximum of a smooth stationary Gaussian process

This paper uses the Rice method [18] to give bounds to the distribution of the maximum of a smooth stationary Gaussian process. We give simpler expressions of the first two terms of the Rice series [3,13] for the distribution of the maximum. Our main contribution is a simpler form of the second factorial moment of the number of upcrossings which is in some sense a generalization of Steinberg et al.'s formula ([7] p. 212). Then, we present a numerical application and asymptotic expansions that give a new interpretation of a result by Piterbarg [15]