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

被引：0

作者：

Shojaie, Ali ^{[1
]}

Basu, Sumanta ^{[2
]}

Michailidis, George ^{[2
]}

机构：

[1] Univ Washington, Dept Biostat, Seattle, WA 98195 USA

[2] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

来源：

STATISTICS IN BIOSCIENCES | 2012年 / 4卷 / 01期

基金：

美国国家科学基金会;

关键词：

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

D O I：

10.1007/s12561-011-9050-5

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：66 / 83

页数：18

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