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

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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.

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Authors and Affiliations

  1. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Ali Shojaie

  2. Department of Statistics, University of Michigan, Ann Arbor, MI, USA

    Sumanta Basu & George Michailidis

Authors
  1. Ali Shojaie
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  2. Sumanta Basu
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  3. George Michailidis
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Correspondence to Ali Shojaie.

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Shojaie, A., Basu, S. & Michailidis, G. Adaptive Thresholding for Reconstructing Regulatory Networks from Time-Course Gene Expression Data. Stat Biosci 4, 66–83 (2012). https://doi.org/10.1007/s12561-011-9050-5

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  • DOI: https://doi.org/10.1007/s12561-011-9050-5

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