MULTI-WAY BLOCKMODELS FOR ANALYZING COORDINATED HIGH-DIMENSIONAL RESPONSES

• 成果类型: Article
• 署名作者: Airoldi, Edoardo M.; Wang, Xiaopei; Lin, Xiaodong
• 署名单位: Harvard University; University System of Ohio; University of Cincinnati; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/13-AOAS643
• 发表日期: 2013
• 页码: 2431-2457
• 关键词: variational inference; gene ontology; prediction; models; cycle

摘要

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.