RT Journal Article
SR Electronic
T1 Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer’s disease
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.21.473558
DO 10.1101/2021.12.21.473558
A1 Su, Chang
A1 Zhang, Jingfei
A1 Zhao, Hongyu
YR 2022
UL http://biorxiv.org/content/early/2022/01/11/2021.12.21.473558.abstract
AB Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregate view of the underlying gene regulations and can be confounded by heterogeneity in cell type compositions, failing to reveal gene coordination that may be distinct across different cell types. In this paper, we describe a flexible framework for estimating cell-type-specific gene co-expression networks from bulk sample data, without making specific assumptions on the distributions of gene expression profiles in different cell types. We develop a novel sparse least squares estimator, referred to as CSNet, that is efficient to implement and has good theoretical properties. Using CSNet, we analyzed the bulk gene expression data from a cohort study on Alzheimer’s disease and identified previously unknown cell-type-specific co-expressions among Alzheimer’s disease risk genes, suggesting cell-type-specific disease pathology for Alzheimer’s disease.Competing Interest StatementThe authors have declared no competing interest.