Hierarchical Quantized Diffusion Based Tree Generation Method for Hierarchical Representation and Lineage Analysis

2025-06-29

AI Summary

Modeling single-cell differentiation trajectories requires accurate representation of tree-like hierarchical structures; however, existing methods suffer from limited generation stability, inadequate modeling of deep hierarchical relationships, and poor computational efficiency. To address these challenges, we propose HDTree—the first diffusion-based hierarchical tree generation framework. HDTree jointly models tree topology and node evolution in latent space via a unified hierarchical codebook and hierarchical vector-quantized diffusion mechanism, eliminating branch-specific modules used in prior approaches. Integrating variational autoencoding, hierarchical vector quantization, and explicit tree-structure learning, HDTree enables progressive, stable, and expressive tree generation. Evaluated on both synthetic and single-cell datasets, HDTree achieves significant improvements in generation accuracy and robustness over state-of-the-art baselines. It provides an efficient, interpretable, and scalable tool for inferring cellular lineages, advancing the computational analysis of developmental dynamics.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In single-cell research, tracing and analyzing high-throughput single-cell differentiation trajectories is crucial for understanding complex biological processes. Key to this is the modeling and generation of hierarchical data that represents the intrinsic structure within datasets. Traditional methods face limitations in terms of computational cost, performance, generative capacity, and stability. Recent VAEs based approaches have made strides in addressing these challenges but still require specialized network modules for each tree branch, limiting their stability and ability to capture deep hierarchical relationships. To overcome these challenges, we introduce diffusion-based approach called HDTree. HDTree captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and quantized diffusion processes to model tree node transitions. This method improves stability by eliminating branch-specific modules and enhancing generative capacity through gradual hierarchical changes simulated by the diffusion process. HDTree's effectiveness is demonstrated through comparisons on both general-purpose and single-cell datasets, where it outperforms existing methods in terms of accuracy and performance. These contributions provide a new tool for hierarchical lineage analysis, enabling more accurate and efficient modeling of cellular differentiation paths and offering insights for downstream biological tasks. The code of HDTree is available at anonymous link https://anonymous.4open.science/r/code_HDTree_review-A8DB.

Problem

• Modeling hierarchical single-cell differentiation trajectories efficiently
• Overcoming computational cost and stability limitations in tree generation
• Enhancing generative capacity for deep hierarchical relationships

Innovation

• Hierarchical codebook for unified tree relationships
• Quantized diffusion processes for node transitions
• Gradual hierarchical changes enhance generative capacity