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.

提出了一种新的方法 scTree，用于单细胞树状变分自动编码器，扩展了一种层次聚类方法用于单细胞 RNA 测序数据。通过这种基于 VAE 的方法，同时纠正批次效应和学习树状数据表示，从而深入理解复杂细胞景观，并独立于批次的偏差效应。通过对七个数据集的实证研究表明，scTree 可以发现数据的潜在聚类以及其中的层次关系，并且在这些数据集上表现优于已有的基准方法。此外，我们还分析了学习得到的层次结构以了解其生物相关性，从而强调了直接将批次纠正引入聚类过程的重要性。