Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.
The 9 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
- [1] § Experimental Section › Data Preprocessing ↔ load_data.py, lines 251–275 · score 0.75 · highly variable genes, Seurat v3, filtered gene, Scanpy, matrix, Preprocessing
- [2] § Experimental Section › Data Description ↔ load_data.py, lines 17–162 · score 0.70 · mouse brain, human tonsil, chicken heart, multi omics, DLPFC, slices
- [3] § Experimental Section › Data Description ↔ plot.py, lines 20–61 · score 0.64 · mouse brain, human tonsil, chicken heart, STransformer, spots, DLPFC
- [4] § Results › Resolving Mouse Embryonic Brain Structures from Spatial Epigenome‐Transcriptome Data ↔ load_data.py, lines 213–248 · score 0.60 · E15.5, mouse brain, multi omics, ATAC, gene
- [5] § Experimental Section › Long‐Range Tissue‐Wide Dependency Learning ↔ transformer/decoder.py, the whole file · a weak match · score 0.59 · Feed Forward Networks, Layer Normalization, dropout
- [6] § Experimental Section › Data Preprocessing ↔ BYOL/image_extract.py, lines 155–275 · score 0.56 · resnet50, BYOL, grayscale, filtering
- [7] § Experimental Section › Long‐Range Tissue‐Wide Dependency Learning ↔ transformer/transformer.py, the whole file · a weak match · score 0.54 · encoder layer, Multi Head, linearly, embedding, Transformer, model
- [8] § Experimental Section › Spatial Graph Construction ↔ graph.py, lines 118–153 · score 0.52 · adjacency matrix, KNN, row, graph, preprocessed
- [9] § Experimental Section › Long‐Range Tissue‐Wide Dependency Learning ↔ transformer/transformer.py, the whole file · a weak match · score 0.51 · decoder layer, Multi Head, Transformer, encoder
Paper
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The authors' code
Python · 313 lines · 12 KB · no license · 3 matches
load_data.py at commit cf827c4, no license · at the source
Overview
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China
- Shenzhen Research Institute of Northwestern Polytechnical University, Shenzhen, Guangdong, China
- Faculty of Data Science, City University of Macau, Macau, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Abstract
Advances in spatially resolved technologies enable the simultaneous acquisition of diverse data modalities within a tissue slice while preserving critical spatial context, which presents unprecedented opportunities to decipher intricate tissue heterogeneity. However, existing computational approaches lack the intrinsic flexibility to universally process both spatial multi‐modal and multi‐omics data. Here, we introduce STransformer, a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short‐range cellular interactions and tissue‐wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity. Systematic evaluations across diverse species, tissue types, and data modalities highlight its profound versatility. For spatial multi‐modal data, STransformer delineates intricate anatomical structures in the human cortex, uncovers pathological mechanisms in Alzheimer's disease, and characterizes dynamic spatiotemporal developmental trajectories during chicken cardiogenesis. Scaling to spatial multi‐omics data, STransformer synergizes spatial transcriptomic and proteomic profiles to decipher intricate immune microenvironments within the human tonsil, and jointly analyzes spatial epigenomic and transcriptomic data to infer regulatory mechanisms in the mouse embryonic brain. Consequently, STransformer serves as a highly versatile and robust analytical framework for advancing our understanding of tissue heterogeneity and disease pathogenesis.
Reproduced under the paper's license (CC BY), from the paper cited above.
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xingyili/STransformer
cf827c46c1e3c21af4f424d980aa056a871eb4be, 1 May 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
18 files, not copied: shown from their source
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- BYOL/
adata_processing.py — Python, 35 lines, shown from its source - BYOL/
image_extract.py — Python, 305 lines, 1 match, shown from its source - graph.py — Python, 191 lines, 1 match, shown from its source
- load_data.py — Python, 313 lines, 3 matches, shown from its source
- loss.py — Python, 16 lines, shown from its source
- main.py — Python, 148 lines, shown from its source
- models/
__init__.py — Python, 1 line, shown from its source - models/
gae.py — Python, 95 lines, shown from its source - models/
model.py — Python, 130 lines, shown from its source - plot.py — Python, 85 lines, 1 match, shown from its source
- transformer/
__init__.py — Python, 1 line, shown from its source - transformer/
decoder.py — Python, 116 lines, 1 match, shown from its source - transformer/
encoder.py — Python, 103 lines, shown from its source - transformer/
multiHeadAttention.py — Python, 364 lines, shown from its source - transformer/
positionwiseFeedForward. — Python, 46 lines, shown from its sourcepy - transformer/
transformer.py — Python, 158 lines, 2 matches, shown from its source - transformer/
utils.py — Python, 93 lines, shown from its source - README.md — Text, 78 lines, shown from its source
The paper's code and data availability statement is in the Data section.
Tracing map
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Data
Datasets cited
- zenodo:19345129 — at Zenodo; found in “Data Availability Statement”
Data Availability Statement
The open‐source Python implementation of STransformer is available at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 4 keywords, 11 MeSH terms, 4 funders, 39 references.
Cite
This paper
Li, X., Xu, J., Du, G., Jia, X., Zhao, D., Zhou, C., Xiao, K., Gu, J., Zhu, J., & Shang, X. (2026). Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 13(49), e75969. https://
BibTeX
@article{li2026accuratel
author = {Li, Xingyi and Xu, Jialuo and Du, Gaoyuan and Jia, Xiangting and Zhao, Dongmin and Zhou, Chunyan and Xiao, Kexin and Gu, Jia and Zhu, Junnan and Shang, Xuequn},
title = {{Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = jun,
volume = {13},
number = {49},
pages = {e75969},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/
url = {https://
pmid = {42263243},
pmcid = {PMC13336508}
}
RIS
TY - JOUR
AU - Li, Xingyi
AU - Xu, Jialuo
AU - Du, Gaoyuan
AU - Jia, Xiangting
AU - Zhao, Dongmin
AU - Zhou, Chunyan
AU - Xiao, Kexin
AU - Gu, Jia
AU - Zhu, Junnan
AU - Shang, Xuequn
TI - Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/
VL - 13
IS - 49
SP - e75969
SN - 2198-3844
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
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