# HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference

arXiv: https://arxiv.org/abs/2506.23287 · HTML: https://mlsanity.com/paper/2506.23287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 59 references that are in the map.
- First posted on arXiv 29 Jun 2025.

## Builds on (most cited first, 19 of 19)

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- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
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- [Clustering with UMAP: Why and How Connectivity Matters](https://mlsanity.com/paper/2108.05525/) (2021)
- [Vector Quantization for Recommender Systems: A Review and Outlook](https://mlsanity.com/paper/2405.03110/) (2024)
- [GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions](https://mlsanity.com/paper/1908.03919/) (2019)
- [Binder: Hierarchical Concept Representation through Order Embedding of Binary Vectors](https://mlsanity.com/paper/2404.10924/) (2024)
- [MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization](https://mlsanity.com/paper/2410.19504/) (2024)
- [Hierarchical Correlation Clustering and Tree Preserving Embedding](https://mlsanity.com/paper/2002.07756/) (2020)
- [A Review of BioTree Construction in the Context of Information Fusion: Priors, Methods, Applications and Trends](https://mlsanity.com/paper/2410.04815/) (2024)

## Abstract (arXiv)

In single-cell research, tracing and analyzing high-throughput single-cell differentiation trajectories is crucial for understanding biological processes. Key to this is the robust modeling of hierarchical structures that govern cellular development. Traditional methods face limitations in computational cost, performance, and stability. VAE-based approaches have made strides but still require branch-specific network modules, limiting their scalability and stability, while often suffering from posterior collapse. To overcome these challenges, we introduce HDTree, a generative modeling framework designed for robust lineage inference. HDTree captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and employs a quantized diffusion process to model continuous cell state transitions. By aligning the generative process with the Waddington landscape, this method not only improves stability and scalability but also enhances the biological plausibility of inferred lineages. HDTree's effectiveness is demonstrated through comparisons on both general-purpose and single-cell datasets, where it outperforms existing methods in lineage inference accuracy, reconstruction quality, and hierarchical consistency. These contributions enable accurate and efficient modeling of cellular differentiation paths, offering reliable insights for biological discovery.footnoteCode is available at https://github.com/zangzelin/codeHDTreeicml.