Direct estimation of differential networks

Sihai Dave Zhao, T. Tony Cai, Hongzhe Li

Research output: Contribution to journalArticlepeer-review

Abstract

It is often of interest to understand how the structure of a genetic network differs between two conditions. In this paper, each condition-specific network is modelled using the precision matrix of a multivariate normal random vector, and a method is proposed to directly estimate the difference of the precision matrices. In contrast to other approaches, such as separate or joint estimation of the individual matrices, direct estimation does not require those matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Under the assumption that the true differential network is sparse, the direct estimator is shown to be consistent in support recovery and estimation. It is also shown to outperform existing methods in simulations, and its properties are illustrated on gene expression data from late-stage ovarian cancer patients.

Original languageEnglish (US)
Pages (from-to)253-268
Number of pages16
JournalBiometrika
Volume101
Issue number2
DOIs
StatePublished - Jun 2014
Externally publishedYes

Keywords

  • Differential network
  • Graphical model
  • High dimensionality
  • Precision matrix

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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