Estimating heterogeneous gene regulatory networks from zero-inflated single-cell expression data

Inferring gene regulatory networks can elucidate how genes work coop-eratively. The gene-gene collaboration information is often learned by Gaussian 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 matrix 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.