GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.

Authors

Rui Wang, Xinxin Liu, Linlin Zhuo, Zhecheng Zhou, Junlin Xu, Quan Zou, Xiangzheng Fu

Published in

Genome biology. Jun 19, 2026. Epub Jun 19, 2026.

Abstract

Spatial transcriptomics technologies have revolutionized the analysis of spatial gene expression, yet integrating spatial information and generating data across heterogeneous samples remain challenging. We present GenOT, a generative framework combining multi-scale graph self-supervised contrastive learning with optimal transport barycenter theory for efficient cross-slice and cross-platform spatiotemporal interpolation. The core innovation of GenOT lies in introducing an optimal transport barycenter-based interpolation algorithm, which mathematically models spatial distribution differences across heterogeneous samples to reconstruct spatiotemporal gene expression dynamics. Extensive evaluations demonstrate that GenOT consistently outperforms existing approaches in spatial domain identification, cross-platform interpolation, and developmental trajectory reconstruction.

PMID:
42321873
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.

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