Multi-way blockmodels for analyzing coordinated high-dimensional responses
Edoardo M. Airoldi, Xiaopei Wang, Xiaodong Lin
The Annals of Applied Statistics · 2013 · Volume 7 · Issue 4
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.
Keywords
high dimensional data, molecular biology, variational inference, yeast
Enable JavaScript for citation tools and additional paper interactions.