Latent Variable Modelling and Variational Inference for scRNA-seq Differential Expression Analysis

Disease profiling, treatment development, and the identification of new cell populations are some of the most relevant applications relying on differentially expressed genes (DEG) analysis. Three leading technologies emerged; namely, DNA microarrays, bulk RNA sequencing (RNA-seq), and single-cell RNA sequencing (scRNA-seq), the main focus of this work. We introduce two novel approaches to assess DEG: extended Bayesian zero-inflated negative binomial factorization (ext-ZINBayes) and single-cell differential analysis (SIENA). We benchmark the proposed methods with known DEG analysis tools using two real public datasets. The results show that the two procedures can be very competitive with existing methods (scVI, SCDE, MAST, and DEseq) in identifying relevant putative biomarkers. In terms of scalability and correctness, SIENA stands out and may emerge as a powerful tool to discover functional differences between two conditions. Both methods are publicly available at https://github.com/JoanaGodinho/.