scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
arxiv(2024)
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.
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