Covariate-Adjusted Precision Matrix Estimation with an Application in Genetical Genomics.
Journal Article (Journal Article)
Motivated by analysis of genetical genomics data, we introduce a sparse high dimensional multivariate regression model for studying conditional independence relationships among a set of genes adjusting for possible genetic effects. The precision matrix in the model specifies a covariate-adjusted Gaussian graph, which presents the conditional dependence structure of gene expression after the confounding genetic effects on gene expression are taken into account. We present a covariate-adjusted precision matrix estimation method using a constrained ℓ1 minimization, which can be easily implemented by linear programming. Asymptotic convergence rates in various matrix norms and sign consistency are established for the estimators of the regression coefficients and the precision matrix, allowing both the number of genes and the number of the genetic variants to diverge. Simulation shows that the proposed method results in significant improvements in both precision matrix estimation and graphical structure selection when compared to the standard Gaussian graphical model assuming constant means. The proposed method is also applied to analyze a yeast genetical genomics data for the identification of the gene network among a set of genes in the mitogen-activated protein kinase pathway.
Full Text
Duke Authors
Cited Authors
- Cai, TT; Li, H; Liu, W; Xie, J
Published Date
- March 2013
Published In
Volume / Issue
- 100 / 1
Start / End Page
- 139 - 156
PubMed ID
- 28316337
Pubmed Central ID
- PMC5351557
International Standard Serial Number (ISSN)
- 0006-3444
Digital Object Identifier (DOI)
- 10.1093/biomet/ass058
Language
- eng
Conference Location
- England