Céline Delmas

Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication)

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

Effect of copolymer composition of RAFT/MADIX-derived N-vinylcaprolactam/N-vinylpyrrolidone statistical copolymers on their thermoresponsive behavior and hydrogel properties

Thermoresponsive statistical copolymers of N-vinylcaprolactam and N-vinylpyrrolidone of controlled average molar masses and composition were synthesized by RAFT/MADIX polymerization. The cloud point temperature was finely tuned in the range of temperature 36–70 °C. DSC measurements combined with statistical calculations suggest that the whole polymer chain is involved in the hydration/dehydration process rather than short polymer sequences. At high concentration, these polymers enable the formation of thermoresponsive hydrogels. Cryo-SEM analysis of these solutions showed the presence of globular substructures, with a looser 3-D network at higher VP content. This decrease of density of entanglements in the gel network could explain the decrease of mechanical properties and faster rehydration kinetics of the thermogelling polymer.

Likelihood ratio test process for quantitative trait locus detection

We consider the likelihood ratio test (LRT) process related to the test of the absence of QTL (a QTL denotes a quantitative trait locus, i.e. a gene with quantitative effect on a trait) on the interval [0, T], representing a chromosome. The observation is the trait and the composition of the genome at some locations called ‘markers’. We give the asymptotic distribution of this LRT process under the null hypothesis that there is no QTL on [0, T] and under local alternatives with a QTL at t☆ on [0, T]. We show that the LRT is asymptotically the square of some Gaussian process. We give a description of this process as an ‘non-linear interpolated and normalized process’. We propose a simple method to calculate the maximum of the LRT process using only statistics on markers and their ratio. This gives a new method to calculate thresholds for the QTL detection.

Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

The EADGENE Microarray Data Analysis Workshop (Open Access publication)

Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from 10 countries performed and discussed the statistical analyses of real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays from a direct comparison of two treatments (dye-balanced). While there was broader agreement with regards to methods of microarray normalisation and significance testing, there were major differences with regards to quality control. The quality control approaches varied from none, through using statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful in facilitating interaction between scientists with a diverse background but a common interest in microarray analyses.

Computing the Distribution of the Maximum of Gaussian Random Processes

The aim of this paper is to propose an Splus program to calculate bounds for the distribution of the maximum of a smooth Gaussian process on a fixed interval. We generalize the results given in Azaïs et al. (1999) to the case of the absolute value of the Gaussian process and to the non-homogeneous case. Our method relies on calculations of the first three terms of the Rices series. Some applications are given to illustrate the method and the performances of the program. The corresponding Splus functions are available at the URL: http://www.lsp.ups-tlse.fr/Cdelmas/software.html.

Projections on spherical cones, maximum of Gaussian fields and Rice's method

Using Rices method to obtain the distribution of the maximum of Gaussian random fields we give the distribution of the length of the projection of a Gaussian variable onto any convex or non-convex spherical cone.