scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
<|reference_start|>scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data: 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.<|reference_end|>
@article{vandenhirtz2024sctree:,
    title={scTree: Discovering Cellular Hierarchies in the Presence of Batch
  Effects in scRNA-seq Data},
    author={Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi, Julia E. Vogt,
  Valentina Boeva},
    journal={arXiv preprint arXiv:2406.19300},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19300},
    primaryClass={cs.LG}
}