scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data
This addresses the challenge of analyzing complex cellular hierarchies in scRNA-seq data for researchers, though it is incremental as it extends existing hierarchical clustering approaches.
The paper tackles the problem of batch effects in single-cell RNA sequencing data by proposing scTree, a method that corrects for batch effects while learning a tree-structured representation, and it outperforms baseline methods on seven datasets.
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.