A statistical approach for array CGH data analysis

BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome

Classification and estimation in the Stochastic Blockmodel based on the empirical degrees

International audienceThe Stochastic Blockmodel [16] is a mixture model for heterogeneous network data. Unlike the usual statistical framework, new nodes give additional information about the previous ones in this model. Thereby the distribution of the degrees concentrates in points conditionally on the node class. We show under a mild assumption that classification, estimation and model selection can actually be achieved with no more than the empirical degree data. We provide an algorithm able to process very large networks and consistent estimators based on it. In particular, we prove a bound of the probability of misclassification of at least one node, including when the number of classes grows

Accuracy of Variational Estimates for Random Graph Mixture Models

International audienceL'analyse des réseaux exerce depuis quelques années un attrait croissant. Les données qui sont sous la forme de mesures de relations entre items sont de plus en plus disponibles, et abandonnent la structure usuelle d'un jeu de données de type individus-variables pour une structure de type individus-individus. Ces données "relationnelles" sont très souvent présentées sous la forme d'un graphe, même si cette représentation a ses limites, notamment quand le nombre d'individus dépasse la centaine. La représentation graphique des données des réseaux est alors attractive, mais nécessite un modèle synthétique. Le modèle de graphe le plus ancien et le plus utilisé est le modèle de Erdös-Rényi, dont les propriétés moyennes ou asymptotiques sont connues. L'écriture littérale de la vraisemblance de ce modèle est très simple, mais son temps de calcul croit de façon exponentielle avec le nombre d'individu. Une utilisation des algorithmes d'estimation usuels comme E-M n'est pas envisageable. Une approche variationnelle a été utilisée comme alternative pour implémenter un algorithme d'estimation des paramètres du modèle, et cela pour des réseaux de très grande taille (Daudin & al 2008). Les propriétés statistiques des estimateurs produits par cette approche sont cependant mal connues. L'objectif est de mener une étude sur la qualité de ces estimateurs et d'en prouver la convergence

Peroxide grafted PDMS: hydrosilylation reaction study and thiol-ene chemistry as an alternative pathway

International audiencePeroxide containing PDMS were synthesized according to a new pathway. Although hydrosilylation is one of the main reaction carried out in silicone chemistry, the catalysts used are very sensitive to the chemical nature of the reactants and remained inefficient to graft allylic peroxide. Radical catalyzed thiol-ene chemistry was involved for the first time to yield an initiator group containing polymer. Peroxide grafted polysiloxane structure and decomposition were characterized using 1H, 13C and 29Si NMR, FT-IR and RAMAN spectroscopies, SEC and DSC. These macroinitiators can be used to obtain polysiloxane able to undergo cross-linking

Extracting biological information from DNA arrays: an unexpected link between arginine and methionine metabolism in Bacillus subtilis

BACKGROUND: In global gene expression profiling experiments, variation in the expression of genes of interest can often be hidden by general noise. To determine how biologically significant variation can be distinguished under such conditions we have analyzed the differences in gene expression when Bacillus subtilis is grown either on methionine or on methylthioribose as sulfur source. RESULTS: An unexpected link between arginine metabolism and sulfur metabolism was discovered, enabling us to identify a high-affinity arginine transport system encoded by the yqiXYZ genes. In addition, we tentatively identified a methionine/methionine sulfoxide transport system which is encoded by the operon ytmIJKLMhisP and is presumably used in the degradation of methionine sulfoxide to methane sulfonate for sulfur recycling. Experimental parameters resulting in systematic biases in gene expression were also uncovered. In particular, we found that the late competence operons comE, comF and comG were associated with subtle variations in growth conditions. CONCLUSIONS: Using variance analysis it is possible to distinguish between systematic biases and relevant gene-expression variation in transcriptome experiments. Co-variation of metabolic gene expression pathways was thus uncovered linking nitrogen and sulfur metabolism in B. subtilis

Epoxidized Oleic Acid‐Based Polymethacrylates as Viscosity Index Improvers

International audienc

Vegetable oil hybrid films cross-linked at the air-water interface: formation kinetics and physical characterization

Vegetable oil based hybrid films were developed thanks to a novel solvent- and heating- free method at the air-water interface using silylated castor oil cross-linked via a sol-gel reaction. To understand the mechanism of the hybrid film formation, the reaction kinetics was studied in detail by using complementary techniques: rheology, thermogravimetric analysis, and infrared spectroscopy. The mechanical properties of the final films were investigated by nano-indentation, whereas their structure was studied using a combination of wide-angle X-ray scattering, electron diffraction, and atomic force microscopy. We found that solid and transparent films form in 24 hours and, by changing the silica precursor to castor oil ratio, their mechanical properties are tunable in the MPa-range by about a factor of twenty. In addition to that, a possible optimization of the cross-linking reaction with different catalysts was explored and finally, cytotoxicity tests were performed on fibroblasts proving the absence of film toxicity. The results of this work pave the way to a straightforward synthesis of castor-oil films with tunable mechanical properties: hybrid films cross-linked at the air-water interface combine an easy and cheap spreading protocol with the features of their thermal history optimized for possible future micro/nano drug loading, thus representing excellent candidates for the replacement of non-environment friendly petroleum-based materials

A mixture model for random graphs

{The Erdos-Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-word networks. The vertices of these networks are often structured in \textit{prior} unknown clusters (functionally related proteins or social communities) with different connectivity properties. We define a generalization of the Erdos-Rényi model called ERMG for Erdos-Rényi Mixtures for Graphs. This new model is based on mixture distributions. We give some of its properties, an algorithm to estimate its parameters and apply this method to uncover the modular structure of a network of enzymatic reactions

Photodimerization as an alternative to photocrosslinking of nanoparticles: proof of concept with amphiphilic linear polyoxazoline bearing coumarin unit

International audiencePhotosensitive amphiphilic linear polyoxazolines (CoumC 11-POx n) bearing alkyl chain decorated by a UV-active coumarin end group have been synthesized by cationic ring-opening polymerization (CROP). Using DLS and DOSY NMR experiments, their self-assemblies in water were compared with those of homologous photo-unreactiveamphiphilic polyoxazolines (C m-POx n). In both cases, spherical nanoparticles with D H-values around 10 nm were observed. The CoumC 11-POx n nanoparticles were illuminated upon 300 nm inducing the photo-dimerization of the coumarin units located in theinnercompartment of the nanoparticles. Finally, the pros and the cons of the photo-dimerization of linear copolymersrelated to the photo-crosslinking of graft copolymerswere discussed

Spotting effect in microarray experiments

BACKGROUND: Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects data obtained with Cy3/Cy5 spotted glass arrays. It yields a periodic pattern altering both signal (Cy3/Cy5 ratio) and intensity across the array. RESULTS: Using the variogram, a geostatistical tool, we characterized the observed variability, called here the spotting effect because it most probably arises during steps in the array printing procedure. CONCLUSIONS: The spotting effect is not appropriately corrected by current normalization methods, even by those addressing spatial variability. Importantly, the spotting effect may alter differential and clustering analysis