气候变化领域集成服务门户(CCPortal): Constructing local cell-specific networks from single-cell data

CCPortal

DOI	10.1073/pnas.2113178118
	Constructing local cell-specific networks from single-cell data
	Wang X.; Choi D.; Roeder K.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:51
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Brain cells; Coexpression network; Differential expression; Differential network genes; Single-cell RNA-seq
语种	英语
scopus关键词	autism; brain; cytology; fetus; gene expression regulation; gene regulatory network; human; metabolism; nerve cell; physiology; procedures; sequence analysis; single cell analysis; Autism Spectrum Disorder; Brain; Fetus; Gene Expression Regulation; Gene Regulatory Networks; Humans; Neurons; RNA-Seq; Sequence Analysis, RNA; Single-Cell Analysis
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/250924
作者单位	Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States; Heinz College, Carnegie Mellon University, Pittsburgh, PA 15213, United States; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
推荐引用方式 GB/T 7714	Wang X.,Choi D.,Roeder K.. Constructing local cell-specific networks from single-cell data[J],2021,118(51).
APA	Wang X.,Choi D.,&Roeder K..(2021).Constructing local cell-specific networks from single-cell data.Proceedings of the National Academy of Sciences of the United States of America,118(51).
MLA	Wang X.,et al."Constructing local cell-specific networks from single-cell data".Proceedings of the National Academy of Sciences of the United States of America 118.51(2021).