ArticleThe annals of applied statistics2017
TESTING HIGH-DIMENSIONAL COVARIANCE MATRICES, WITH APPLICATION TO DETECTING SCHIZOPHRENIA RISK GENES.
Article in The annals of applied statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A spectral framework to map QTLs affecting joint differential networks of gene co-expression.PLoS computational biology · 2025Article
- AWGE-ESPCA: An edge sparse PCA model based on adaptive noise elimination regularization and weighted gene network for Hermetia illucens genomic data analysis.PLoS computational biology · 2025Article
- Sex-Differential Gene Expression in Developing Human Cortex and Its Intersection With Autism Risk Pathways.Biological psychiatry global open science · 2024Article
- A novel method for multiple phenotype association studies based on genotype and phenotype network.PLoS genetics · 2024Article
- Article
- The cluster D-trace loss for differential network analysis.Journal of applied statistics · 2024Article
- On Graphical Models and Convex Geometry.Computational statistics & data analysis · 2023Article
- Use of random integration to test equality of high dimensional covariance matrices.Statistica Sinica · 2023Article
- Gradient-based sparse principal component analysis with extensions to online learning.Biometrika · 2023Article
- CONTRASTIVE LATENT VARIABLE MODELING WITH APPLICATION TO CASE-CONTROL SEQUENCING EXPERIMENTS.The annals of applied statistics · 2022Article
- Constructing local cell-specific networks from single-cell data.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A mixture model to detect edges in sparse co-expression graphs with an application for comparing breast cancer subtypes.PloS one · 2021Article
- Inherited L1 Retrotransposon Insertions Associated With Risk for Schizophrenia and Bipolar Disorder.Schizophrenia bulletin open · 2021Article
- decorate: differential epigenetic correlation test.Bioinformatics (Oxford, England) · 2020Article
- Set-based differential covariance testing for genomics.Stat (International Statistical Institute) · 2019Article
- Differential Network Analysis: A Statistical Perspective.Wiley interdisciplinary reviews. Computational statisticsArticle
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4 authors.
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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.
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