2021/11/07 by Feiyi Xiao, Junjie Tang, Xiao, Feiyi +5
Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Methodology (stat.ME) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.2111.04037
openalex publication_date 2021/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Gene regulatory network inference is crucial for understanding the complex molecular interactions in various genetic and environmental conditions. The rapid development of single-cell RNA sequencing (scRNA-seq) technologies unprecedentedly enables gene regulatory networks inference at the single cell resolution. However, traditional graphical models for continuous data, such as Gaussian graphical models, are inappropriate for network inference of scRNA-seq's count data. Here, we model the scRNA-seq data using the multivariate Poisson log-normal (PLN) distribution and represent the precision matrix of the latent normal distribution as the regulatory network. We propose to first estimate the latent covariance matrix using a moment estimator and then estimate the precision matrix by minimizing the lasso-penalized D-trace loss function. We establish the convergence rate of the covariance matrix estimator and further establish the convergence rates and the sign consistency of the proposed PLNet estimator of the precision matrix in the high dimensional setting. The performance of PLNet is evaluated and compared with available methods using simulation and gene regulatory network analysis of scRNA-seq data.