scTree：在存在批次效应的scRNA-seq数据中发现细胞层级结构
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
AI总结：
本研究提出基于VAE的scTree方法，可在scRNA-seq数据中同时校正批次效应并学习树结构细胞层级表示，在7个数据集上表现优于基准方法，且层级结构具有生物学相关性，验证了批次校正整合进聚类的价值。
摘要：
我们提出了一种名为scTree的新方法，用于单细胞树变分自编码器（single-cell Tree Variational Autoencoders），将层次聚类方法扩展到单细胞RNA测序数据。scTree在校正批次效应的同时，学习树结构的数据表示。这种基于VAE的方法能够不受批次偏差效应的影响，更深入地理解复杂细胞图景。我们在7个数据集上通过实验证明，scTree能够发现数据的潜在聚类及其之间的层级关系，并且在这些数据集上的表现优于已有的基准方法。此外，我们分析了学习到的层级结构以理解其生物学相关性，从而佐证了将批次校正直接整合到聚类流程中的重要性。
英文摘要：
We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree corrects for batch effects while simultaneously learning a tree-structured data representation. This VAE-based method allows for a more in-depth understanding of complex cellular landscapes independently of the biasing effects of batches. We show empirically on seven datasets that scTree discovers the underlying clusters of the data and the hierarchical relations between them, as well as outperforms established baseline methods across these datasets. Additionally, we analyze the learned hierarchy to understand its biological relevance, thus underpinning the importance of integrating batch correction directly into the clustering procedure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
