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Proceedings of the AAAI Conference on Artificial Intelligence

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CellStream: Dynamical Optimal Transport Informed Embeddin...
Yue Ling, Pe · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Yue Ling Peking University
  • Peiqi Zhang Peking University AI for Science Institute, Beijing
  • Zhenyi Zhang Peking University
  • Peijie Zhou Peking University National Engineering Laboratory for Big Data Analysis and Applications AI for Science Institute, Beijing

DOI:

https://doi.org/10.1609/aaai.v40i1.37041

Abstract

Single-cell RNA sequencing (scRNA-seq), especially temporally resolved datasets, enables genome-wide profiling of gene expression dynamics at single-cell resolution across discrete time points. However, current technologies provide only sparse, static snapshots of cell states and are inherently influenced by technical noise, complicating the inference and representation of continuous transcriptional dynamics. Although embedding methods can reduce dimensionality and mitigate technical noise, the majority of existing approaches typically treat trajectory inference separately from embedding construction, often neglecting temporal structure. To address this challenge, here we introduce CellStream, a novel deep learning framework that jointly learns embedding and cellular dynamics from single-cell snapshots data by integrating an autoencoder with unbalanced dynamical optimal transport. Compared to existing methods, CellStream generates dynamics-informed embeddings that robustly capture temporal developmental processes while maintaining high consistency with the underlying data manifold. We demonstrate CellStream’s effectiveness on both simulated datasets and real scRNA-seq data, including spatial transcriptomics. Our experiments indicate significant quantitative improvements over state-of-the-art methods in representing cellular trajectories with enhanced temporal coherence and reduced noise sensitivity. Overall, CellStream provides a new tool for learning and representing continuous streams from the noisy, static snapshots of single-cell gene expression.

How to Cite

Ling, Y., Zhang, P., Zhang, Z., & Zhou, P. (2026). CellStream: Dynamical Optimal Transport Informed Embeddings for Reconstructing Cellular Trajectories from Snapshots Data. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 746–754. https://doi.org/10.1609/aaai.v40i1.37041

Issue

Section

AAAI Technical Track on Application Domains I

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