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.

翻译：单细胞RNA测序(scRNA-seq)是一种强大的技术,使研究人员能够理解单细胞层次的基因表达模式。然而,由于数据收集中的问题和偏见,分析 scRNA-seq数据具有挑战性。在这项工作中,我们建立了一个综合性的Bayesian模型,同时处理正常化、估算和批量效应以及非对称组集细胞,将其分成多个数据集组。根据HDP的有限维近似值开发了一个Gibs取样器,用于后推推推。