PLNFGL: joint estimation of multi-condition gene networks from single-cell RNA-seq data

MOTIVATION: Graphical models have been widely used in bioinformatics to infer the conditional dependence structure among random variables, but traditional Gaussian graphical models (GGMs) are suboptimal for single-cell RNA sequencing (scRNA-seq) due to dropout events and distributional mismatch. Moreover, most existing methods estimate networks under a single condition, limiting their utility in multi-condition studies. RESULTS: We propose PLNFGL (Poisson Log-Normal Fused Graphical Lasso), a joint network estimation framework for scRNA-seq data. PLNFGL uses a multivariate Poisson log-normal model to accommodate dropout effects and estimates the covariance via moment methods. A joint graphical model is then employed to infer condition-specific precision matrices. Simulations show improved estimation accuracy. Applications to scRNA-seq data of Alzheimer's disease and spatial transcriptomics of lung cancer reveal cell-type-specific interaction networks. Edge set enrichment enables pathway analysis, validating known interactions and highlighting novel disease-related targets. This work provides a powerful tool for the integrative analysis of scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The R implementation of PLNFGL is available at https://github.com/jijiadong/PLNFGL, and an archival version is available on Zenodo at https://doi.org/10.5281/zenodo.20744172.