Department of Statistics
West Lafayette. Indiana
A Novel Approach to Reveal Whole Systems of Gene-Gene Regulations
Constructing whole-genome gene regulatory networks using genetical genomics data is challenged by low power, limited computer memory and intensive computation. We propose a two-stage penalized least squares method to study regulatory interactions among massive genes, building up large systems of structural equations based on a new view of the classical two-stage least squares method. We show that, with large numbers of transcriptomic and genotypical values, the system can be constructed via consistent prediction of a set of surrogate variables at the first stage, and consistent selection of regulatory effects at the second stage. While the consistent prediction at the first stage can be obtained via the ridge regression, the adaptive lasso is employed at the second stage to achieve the consistent selection. The resultant estimates of regulatory effects enjoy the oracle properties. This method is computationally fast and allows for parallel implementation. We demonstrate its effectiveness via simulation studies and real data analysis.
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