Multi-way blockmodels for analyzing coordinated high-dimensional responses

DSpace/Manakin Repository

Multi-way blockmodels for analyzing coordinated high-dimensional responses

Citable link to this page

 

 
Title: Multi-way blockmodels for analyzing coordinated high-dimensional responses
Author: Airoldi, Edoardo Maria; Wang, Xiaopei

Note: Order does not necessarily reflect citation order of authors.

Citation: Airoldi, Edoardo M., Xiaopei Wang, and Xiaodong Lin. 2013. “Multi-Way Blockmodels for Analyzing Coordinated High-Dimensional Responses.” Ann. Appl. Stat. 7 (4) (December): 2431–2457.
Full Text & Related Files:
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 com- monly adopted for this type of data, in biology. We develop two in- ference 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 estima- tion, 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 be- tween gene expression and metabolite concentrations during carbon and nitrogen starvation, in S. cerevisiae.
Published Version: doi:10.1214/13-AOAS643
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:13064708
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters