Statistics in Biosciences

, Volume 4, Issue 1, pp 66–83 | Cite as

Adaptive Thresholding for Reconstructing Regulatory Networks from Time-Course Gene Expression Data

Article

Abstract

Discovering regulatory interactions from time-course gene expression data constitutes a canonical problem in functional genomics and systems biology. The framework of graphical Granger causality allows one to estimate such causal relationships from these data. In this study, we propose an adaptively thresholding estimates of Granger causal effects obtained from the lasso penalization method. We establish the asymptotic properties of the proposed technique, and discuss the advantages it offers over competing methods, such as the truncating lasso. Its performance and that of its competitors is assessed on a number of simulated settings and it is applied on a data set that captures the activation of T-cells.

Keywords

Regulatory networks Time-course gene expression data Graphical Granger causality Thresholding Lasso 

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Copyright information

© International Chinese Statistical Association 2011

Authors and Affiliations

  1. 1.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  2. 2.Department of StatisticsUniversity of MichiganAnn ArborUSA

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