ESTIMATING HETEROGENEOUS GENE REGULATORY NETWORKS FROM ZERO-INFLATED SINGLE-CELL EXPRESSION DATA

• 成果类型: Article
• 署名作者: Wu, Qiuyu; Luo, Xiangyu
• 署名单位: Renmin University of China
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1582
• 发表日期: 2022
• 页码: 2183-2200
• 关键词: inverse covariance estimation; Gaussian graphical models; hematopoietic stem; joint estimation; distributions; inference; Lasso

摘要

Inferring gene regulatory networks can elucidate how genes work coop-eratively. The gene-gene collaboration information is often learned by Gaus-sian graphical models (GGM) that aim to identify whether the expression levels of any pair of genes are dependent, given other genes' expression values. One basic assumption that guarantees the validity of GGM is data normality, and this often holds for bulk-level expression data which aggregate biological signals from a collection of cells. However, fine-grained cell-level expression profiles collected in single-cell RNA-sequencing (scRNA-seq) reveal non -normality features-cellular heterogeneity and zero inflation. We propose a Bayesian latent mixture GGM to jointly estimate multiple gene regulatory networks accounting for the zero inflation and unknown heterogeneity of single-cell expression data. The proposed approach outperforms competing methods on synthetic data in terms of network structure and precision ma-trix estimation accuracy and provides biological insights when applied to two real-world scRNA-seq datasets. An R package implementing the proposed model is available on GitHub https://github.com/WgitU/BLGGM.