论文标题
通过层次dirichlet进程在单细胞RNA测序数据集上共享差异聚类
Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process
论文作者
论文摘要
单细胞RNA测序（SCRNA-SEQ）是强大的技术，它使研究人员可以在单细胞水平上理解基因表达模式。但是，由于数据收集的问题和偏见，分析SCRNA-SEQ数据是具有挑战性的。在这项工作中，我们构建了一个集成的贝叶斯模型，该模型同时解决了跨多个数据集的归一化，插补和批处理效应以及非参数将细胞分为组。基于HDP的有限维近似的Gibbs采样器是用于后推断的。
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.
