The Annals of Applied Statistics

Testing high-dimensional covariance matrices, with application to detecting schizophrenia risk genes

Lingxue Zhu, Jing Lei, Bernie Devlin, and Kathryn Roeder

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Abstract

Scientists routinely compare gene expression levels in cases versus controls in part to determine genes associated with a disease. Similarly, detecting case-control differences in co-expression among genes can be critical to understanding complex human diseases; however, statistical methods have been limited by the high-dimensional nature of this problem. In this paper, we construct a sparse-Leading-Eigenvalue-Driven (sLED) test for comparing two high-dimensional covariance matrices. By focusing on the spectrum of the differential matrix, sLED provides a novel perspective that accommodates what we assume to be common, namely sparse and weak signals in gene expression data, and it is closely related with sparse principal component analysis. We prove that sLED achieves full power asymptotically under mild assumptions, and simulation studies verify that it outperforms other existing procedures under many biologically plausible scenarios. Applying sLED to the largest gene-expression dataset obtained from post-mortem brain tissue from Schizophrenia patients and controls, we provide a novel list of genes implicated in Schizophrenia and reveal intriguing patterns in gene co-expression change for Schizophrenia subjects. We also illustrate that sLED can be generalized to compare other gene-gene “relationship” matrices that are of practical interest, such as the weighted adjacency matrices.

Article information

Source
Ann. Appl. Stat. Volume 11, Number 3 (2017), 1810-1831.

Dates
Received: November 2016
Revised: April 2017
First available in Project Euclid: 5 October 2017

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1507168849

Digital Object Identifier
doi:10.1214/17-AOAS1062

Keywords
Permutation test high-dimensional data covariance matrix sparse principal component analysis

Citation

Zhu, Lingxue; Lei, Jing; Devlin, Bernie; Roeder, Kathryn. Testing high-dimensional covariance matrices, with application to detecting schizophrenia risk genes. Ann. Appl. Stat. 11 (2017), no. 3, 1810--1831. doi:10.1214/17-AOAS1062. https://projecteuclid.org/euclid.aoas/1507168849


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Supplemental materials

  • Supplement to “Testing high dimensional covariance matrices, with application to detecting schizophrenia risk genes”. This supplement provides additional simulation results as well as proofs for the theorems.