Journal
STATISTICS IN BIOSCIENCES
Volume 4, Issue 1, Pages 66-83Publisher
SPRINGER
DOI: 10.1007/s12561-011-9050-5
Keywords
Regulatory networks; Time-course gene expression data; Graphical Granger causality; Thresholding; Lasso
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Funding
- NIH [1RC1CA145444-0110]
- Statistical and Applied Mathematical Sciences Institute (SAMSI)
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1545277] Funding Source: National Science Foundation
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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.
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