5 research outputs found

    Unidimensional nonnegative scaling for genome-wide Linkage Disequilibrium maps

    No full text
    The main aim of this paper is to propose and develop a unidimensional nonnegative scaling model to construct Linkage Disequilibrium (LD) maps. The proposed constrained scaling model can be efficiently solved by transforming it to an unconstrained model. The method is implemented in PC Clusters at Hong Kong Baptist University. The LD maps are constructed for four populations from Hapmap data sets with chromosomes of several ten thousand Single Nucleotide Polymorphisms (SNPs). The similarities and dissimilarities of the LD maps are studied and analysed. Computational results are also reported to show the effectiveness of the method using parallel computation. Copyright © 2008 Inderscience Enterprises Ltd.link_to_subscribed_fulltex

    A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

    Get PDF
    When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

    A Statistical Approach to the Alignment of fMRI Data

    Get PDF
    Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods
    corecore