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

arXiv: https://arxiv.org/abs/2406.19300 · HTML: https://mlsanity.com/paper/2406.19300/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Nonparametric Variational Auto-encoders for Hierarchical Representation Learning](https://mlsanity.com/paper/1703.07027/) (2017)
- [Tree Variational Autoencoders](https://mlsanity.com/paper/2306.08984/) (2023)
- [Hierarchically Clustered Representation Learning](https://mlsanity.com/paper/1901.09906/) (2019)
- [Conditional out-of-sample generation for unpaired data using trVAE](https://mlsanity.com/paper/1910.01791/) (2019)
- [The LORACs prior for VAEs: Letting the Trees Speak for the Data](https://mlsanity.com/paper/1810.06891/) (2018)

## Built on by (most cited first, 2 of 2)

- [From Logits to Hierarchies: Hierarchical Clustering made Simple](https://mlsanity.com/paper/2410.07858/) (2024)
- [TreeDiffusion: Hierarchical Generative Clustering for Conditional Diffusion](https://mlsanity.com/paper/2410.16910/) (2024)

## Abstract (arXiv)

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.