Back

Spatial geometry-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics

Li, X.; Jia, X.; Zhao, D.; Xu, J.; Du, G.; Qi, Y.; Chen, Y.; Wu, Y.; Zhu, J.; Wei, F.; Shang, X.

2025-05-15 bioinformatics
10.1101/2025.05.11.652625 bioRxiv
Show abstract

Recent advances in spatially resolved transcriptomics have enabled high-throughput gene expression profiling while preserving spatial context, thereby facilitating the investigation of spatial heterogeneity within tissues. Here, we present SpatialGEO, a spatial geometry-aware deep learning framework designed to decipher tissue organizational structures through dual-encoder feature extraction and geometric graph learning. Experimental results demonstrate the superior accuracy of SpatialGEO in identifying spatial regions across datasets encompassing diverse biological contexts and spatial resolutions. Moreover, SpatialGEO uncovers key mechanisms of immune evasion and regulation within the tumor microenvironment and provides novel biological insights into mouse embryonic development.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 6%
22.4%
Similar papers in this journal
2
Advanced Science
○ 286 papers in training set
Top 0.3%
9.8%
Similar papers in this journal
3
Nature Methods
○ 385 papers in training set
Top 1%
9.7%
Similar papers in this journal
4
Briefings in Bioinformatics
◐ 354 papers in training set
Top 0.8%
7.9%
Similar papers in this journal
5
Bioinformatics
◐ 1204 papers in training set
Top 4%
6.3%
Similar papers in this journal
50% of probability mass above
6
Genome Biology
○ 637 papers in training set
Top 3%
5.2%
7
Nature Machine Intelligence
○ 70 papers in training set
Top 0.7%
4.0%
8
Patterns
○ 78 papers in training set
Top 0.7%
2.8%
9
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.4%
10
Genome Research
● 468 papers in training set
Top 3%
2.1%
11
PLOS Computational Biology
● 1863 papers in training set
Top 13%
1.9%
12
Nature Computational Science
○ 55 papers in training set
Top 0.6%
1.7%
13
Communications Biology
○ 993 papers in training set
Top 16%
1.5%
14
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 30%
1.5%
15
iScience
○ 1154 papers in training set
Top 23%
1.3%
16
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 2%
1.1%
17
Science Advances
● 1243 papers in training set
Top 27%
1.0%
18
Scientific Reports
○ 3612 papers in training set
Top 69%
1.0%
19
Genome Medicine
○ 183 papers in training set
Top 5%
0.9%
20
Cell Genomics
○ 172 papers in training set
Top 4%
0.8%
21
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.8%
22
IEEE Journal of Biomedical and Health Informatics
● 37 papers in training set
Top 1%
0.8%
23
Science China Life Sciences
○ 29 papers in training set
Top 0.7%
0.6%
24
Small Methods
○ 29 papers in training set
Top 0.9%
0.6%
25
Cell Reports
○ 1498 papers in training set
Top 29%
0.6%
26
Cell Systems
○ 201 papers in training set
Top 5%
0.6%
27
Bioinformatics Advances
◐ 203 papers in training set
Top 5%
0.6%
28
Nature Biotechnology
○ 172 papers in training set
Top 5%
0.6%
29
eLife
● 5828 papers in training set
Top 70%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.