  Single-cell RNA sequencing (scRNA-seq) is powerful technology that allows
researchers to understand gene expression patterns at the single-cell level.
However, analysing scRNA-seq data is challenging due to issues and biases in
data collection. In this work, we construct an integrated Bayesian model that
simultaneously addresses normalization, imputation and batch effects and also
nonparametrically clusters cells into groups across multiple datasets. A Gibbs
sampler based on a finite-dimensional approximation of the HDP is developed for
posterior inference.
