Multiple hypothesis testing and clustering with mixtures of non-central t-distributions applied in microarray data analysis

Multiple testing analysis, based on clustering methodologies, is usually applied in Microarray Data Analysis for comparisons between pair of groups. In this paper, we generalize this methodology to deal with multiple comparisons among more than two groups obtained from microarray expressions of genes. Assuming normal data, we define a statistic which depends on sample means and sample variances, distributed as a non-central t-distribution. As we consider multiple comparisons among groups, a mixture of non-central t-distributions is derived. The estimation of the components of mixtures is obtained via a Bayesian approach, and the model is applied in a multiple comparison problem from a microarray experiment obtained from gorilla, bonobo and human cultured fibroblasts.Clustering, MCMC computation, Microarray analysis, Mixture distributions, Multiple hypothesis testing, Non-central t-distribution

Coherence of the posterior predictive p-value based on the posterior odds.

^aIt is well-known that classical p-values sometimes behave incoherently for testing hypotheses in the sense that, when $\Theta_{0} \subset \Theta_{0}{'}$, the support given to $\Theta_{0}$ is greater than or equal to the support given to $\Theta_{0}^{'}$ . This problem is also found for posterior predictive p-values (a Bayesian-motivated alternative to classical p-values). In this paper, it is proved that, under some conditions, the posterior predictive p-value based on the posterior odds is coherent, showing that the choice of a suitable discrepancy variable is crucial

Multiparticie aggregation model for dendritic growth applied to experiments on amorphous Co-P alloys

We introduce a multiparticle biased diffusion limited aggregation model for dendritic growth. Its most relevant feature is that it includes the overall effect of strong applied electric fields and therefore applies to nonequilibrium situations. We compare simulations of a two species version of our model to actual experiments on preparation of amorphous Co-P alloys with very good agreement: The model accurately reproduces the dependence of composition, morphology, and growth time of the alloy on the current. We conclude with a discussion of specific predictions and possible generalizations of the model.Supported by DGICyT (Spain) through Grant No. PB92-0248 and by MEC/Fulbright. MJ.B. and J.M.R. acknowledge support from DGICyT (Spain) Grant No. MAT91-0031. Work at Los Alamos is performed under the auspices of the V.S. D.O.E.Publicad

Effect of processing conditions on the thermal and electrical conductivity of poly (butylene terephthalate) nanocomposites prepared via ring-opening polymerization

Successful preparation of polymer nanocomposites, exploiting graphene-related materials, via melt mixing technology requires precise design, optimization and control of processing. In the present work, the effect of different processing parameters during the preparation of poly (butylene terephthalate) nanocomposites, through ring-opening polymerization of cyclic butylene terephthalate in presence of graphite nanoplatelets (GNP), was thoroughly addressed. Processing temperature (240{\deg}C or 260{\deg}C), extrusion time (5 or 10 minutes) and shear rate (50 or 100 rpm) were varied by means of a full factorial design of experiment approach, leading to the preparation of polybutylene terephthalate/GNP nanocomposite in 8 different processing conditions. Morphology and quality of GNP were investigated by means of electron microscopy, X-ray photoelectron spectroscopy, thermogravimetry and Raman spectroscopy. Molecular weight of the polymer matrix in nanocomposites and nanoflake dispersion were experimentally determined as a function of the different processing conditions. The effect of transformation parameters on electrical and thermal properties was studied by means of electrical and thermal conductivity measurement. Heat and charge transport performance evidenced a clear correlation with the dispersion and fragmentation of the GNP nanoflakes; in particular, gentle processing conditions (low shear rate, short mixing time) turned out to be the most favourable condition to obtain high conductivity values