Differential network analysis via lasso penalized D-trace loss

Huili Yuan, Ruibin Xi, Chong Chen and Minghua Deng

Biometrika, 2017, vol. 104, issue 4, 755-770

Abstract: SummaryBiological networks often change under different environmental and genetic conditions. In this paper, we model network change as the difference of two precision matrices and propose a novel loss function called the D-trace loss, which allows us to directly estimate the precision matrix difference without attempting to estimate the precision matrices themselves. Under a new irrepresentability condition, we show that the D-trace loss function with the lasso penalty can yield consistent estimators in high-dimensional settings if the difference network is sparse. A very efficient algorithm is developed based on the alternating direction method of multipliers to minimize the penalized loss function. Simulation studies and a real-data analysis show that the proposed method outperforms other methods.

Keywords: Gaussian graphical model; Gene regulatory network; High dimensionality; Precision matrix; Sign consistency (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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