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STIFT: Spatiotemporal Transcriptomics Integration by Spatially Informed Multi-Timepoint Bridging

Abstract Number:

1450 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Ji Qi (1), Muyang Ge (2), Jishuai Miao (2), Xiaocheng Zhou (2), Zhixiang Lin (2)

Institutions:

(1) N/A, N/A, (2) The Chinese University of Hong Kong, N/A

Co-Author(s):

Muyang Ge  
The Chinese University of Hong Kong
Jishuai MIAO  
The Chinese University of Hong Kong
Xiaocheng Zhou  
The Chinese University of Hong Kong
Zhixiang Lin  
The Chinese University of Hong Kong

First Author:

Ji Qi  
N/A

Presenting Author:

Ji Qi  
N/A

Abstract Text:

Recent advances in spatial transcriptomics have highlighted the need for integrating spatial transcriptomics data across multiple developmental and regenerative stages. We present STIFT (SpatioTemporal Integration Framework for Transcriptomics), a three-component framework combining developmental spatiotemporal optimal transport, spatiotemporal graph construction, and triplet-informed graph attention autoencoder (GATE) specifically designed for integrating spatiotemporal transcriptomics data. STIFT efficiently processes large-scale 2D and 3D spatiotemporal trancriptomics data while preserving temporal patterns and biological structures, enabling batch effect removal, spatial domain identification, trajectory inference and exploration of developmental dynamics. Applied to axolotl brain regeneration, mouse embryonic development, and 3D planarian regeneration datasets, STIFT efficiently removes batch effects and achieves clear spatial domain identification while preserving temporal developmental patterns and biological variations across hundreds of thousands of spots, demonstrating its effectiveness and specificity in integrating spatiotemporal transcriptomics data.

Keywords:

spatial transcriptomics|spatiotemporal data integration|graph attention autoencoder|developmental biology| |

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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