期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2021
卷号：118
期号：51
DOI：10.1073/pnas.2113178118
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
Understanding gene regulatory networks is a topic of great interest because it can provide insights into cellular development, and identify factors that differ between normal and abnormal cells and phenotypes. Single-cell RNA sequencing provides a unique opportunity to gain understanding at the cellular level, but the technical features of the data create severe challenges when constructing gene networks. We develop a method that successfully skirts these challenges to estimate a cell-specific network for each single cell and cell type. Application of our algorithm to two brain cell samples furthers our understanding of autism spectrum disorder by examining the evolution of gene networks in fetal brain cells and comparing the networks of cells sampled from case and control subjects.
Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.
