Scalable inference of transcriptional variability with BASiCS

Abstract

BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model for the analysis of single-cell RNA sequencing data. BASiCS performs simultaneous data normalization and quantification of technical noise, and enables analysis of mean expression and expression variability within or across cell populations. We extend BASiCS with a divide and conquer inference scheme to enable scalable Bayesian inference for large datasets. We compare the performance of the divide and conquer approach to standard Markov Chain Monte Carlo (MCMC) and variational inference methods (ADVI) in terms of accuracy and scalability. Our results demonstrate that the divide and conquer approach enables large-scale scRNA-seq analysis, providing accurate and efficient inference while maintaining the interpretability and flexibility of the BASiCS framework.