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    Multi-way blockmodels for analyzing coordinated high-dimensional responses

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    Genre
    Journal Article
    Date
    2013-12-01
    Author
    Airoldi, EM
    Wang, X
    Lin, X
    Subject
    High dimensional data
    variational inference
    molecular biology
    yeast
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5927
    
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    DOI
    10.1214/13-AOAS643
    Abstract
    We consider the problem of quantifying temporal coordination between multiple high-dimensional responses. We introduce a family of multi-way stochastic blockmodels suited for this problem, which avoids preprocessing steps such as binning and thresholding commonly adopted for this type of data, in biology. We develop two inference procedures based on collapsed Gibbs sampling and variational methods.We provide a thorough evaluation of the proposed methods on simulated data, in terms of membership and blockmodel estimation, predictions out-of-sample and run-time. We also quantify the effects of censoring procedures such as binning and thresholding on the estimation tasks. We use these models to carry out an empirical analysis of the functional mechanisms driving the coordination between gene expression and metabolite concentrations during carbon and nitrogen starvation, in S. cerevisiae. © Institute of Mathematical Statistics, 2013.
    Citation to related work
    Institute of Mathematical Statistics
    Has part
    Annals of Applied Statistics
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    http://dx.doi.org/10.34944/dspace/5909
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