scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data

2024/06/27 by Moritz Vandenhirtz, Florian Barkmann, Vandenhirtz, Moritz +7
Biochemistry, Genetics and Molecular Biology · #Cell Image Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Single-cell and spatial transcriptomics

paper · pdf · doi:10.48550/arxiv.2406.19300

openalex publication_date 2024/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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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