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December 2013 Multi-way blockmodels for analyzing coordinated high-dimensional responses
Edoardo M. Airoldi, Xiaopei Wang, Xiaodong Lin
Ann. Appl. Stat. 7(4): 2431-2457 (December 2013). DOI: 10.1214/13-AOAS643


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.


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Edoardo M. Airoldi. Xiaopei Wang. Xiaodong Lin. "Multi-way blockmodels for analyzing coordinated high-dimensional responses." Ann. Appl. Stat. 7 (4) 2431 - 2457, December 2013.


Published: December 2013
First available in Project Euclid: 23 December 2013

zbMATH: 1283.62215
MathSciNet: MR3161729
Digital Object Identifier: 10.1214/13-AOAS643

Keywords: high dimensional data , molecular biology , variational inference , yeast

Rights: Copyright © 2013 Institute of Mathematical Statistics


Vol.7 • No. 4 • December 2013
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