2,634 research outputs found

    Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons

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    Gene expression levels in a population vary extensively across tissues. Such heterogeneity is caused by genetic variability and environmental factors, and is expected to be linked to disease development. The abundance of experimental data now enables the identification of features of gene expression profiles that are shared across tissues, and those that are tissue-specific. While most current research is concerned with characterising differential expression by comparing mean expression profiles across tissues, it is also believed that a significant difference in a gene expression's variance across tissues may also be associated to molecular mechanisms that are important for tissue development and function. We propose a sparse multi-view matrix factorisation (sMVMF) algorithm to jointly analyse gene expression measurements in multiple tissues, where each tissue provides a different "view" of the underlying organism. The proposed methodology can be interpreted as an extension of principal component analysis in that it provides the means to decompose the total sample variance in each tissue into the sum of two components: one capturing the variance that is shared across tissues, and one isolating the tissue-specific variances. sMVMF has been used to jointly model mRNA expression profiles in three tissues - adipose, skin and LCL - which are available for a large and well-phenotyped twins cohort, TwinsUK. Using sMVMF, we are able to prioritise genes based on whether their variation patterns are specific to each tissue. Furthermore, using DNA methylation profiles available, we provide supporting evidence that adipose-specific gene expression patterns may be driven by epigenetic effects.Comment: in Bioinformatics 201

    Trade liberalization and the great labor reallocation

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    The extent to which a country can benefit from trade openness crucially depends on its ease of reallocating resources. However, we know little about the role of domestic frictions in shaping the effects of trade policies. I address this question by analyzing the impact of tariff reductions on the spatial allocation of labor in China, and how this impact depends on migration frictions that stem from China's household registration system (hukou). I first provide reduced-form evidence that input trade liberalization has induced significant spatial labor reallocation in China, with a stronger effect in regions with less hukou frictions. Then, I construct and estimate a quantitative spatial model with input-output linkages and hukou frictions to examine the general equilibrium effects of tariff reductions and perform counterfactuals. The quantitative exercise shows that trade liberalization increases China's welfare by 0.63%. Abolishing the hukou system leads to a direct welfare improvement of 1.51%. Additionally, it increases gains from tariff reductions by 2% and alleviates its negative distributional consequences. In this process, I develop a novel measure of migration frictions associated with the hukou system
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