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Activity Number: 560
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 11:15 AM
Sponsor: Biometrics Section
Abstract #321584
Title: A New Method to Construct Large Gene Regulatory Networks Using Genetical Genomics Data
Author(s): Chen Chen* and Min Zhang and Dabao Zhang
Companies: Purdue University and Purdue University and Purdue University
Keywords: Graphical model ; High dimensional data ; Reciprocal graphical model ; Simultaneous equation model ; Two-stage penalized least squares
Abstract:

Constructing whole-genome gene regulatory networks using genetical genomics data is challenged by limited computer memory and intensive computation. We propose a two-stage penalized least squares method to study regulatory interactions among massive genes, building up reciprocal graphical models on the basis of simultaneous equation models. Fitting a single regression model for each gene at each stage, the method employs the L2 penalty at the first stage to obtain consistent estimation of surrogate variables, and the L1 penalty at the second stage to consistently select regulatory genes among massive candidates. The resultant estimates of the regulatory effects enjoy the oracle properties. Without fitting a full information model, the method is computationally fast and also allows for parallel implementation. We demonstrated the effectiveness of the method by conducting simulation studies, showing its improvements over other methods. Our method was also applied to construct a yeast gene regulatory network.


Authors who are presenting talks have a * after their name.

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