Skip to content

SpatialGlue is a novel deep learning methods for spatial multi-omics data integration.

License

Notifications You must be signed in to change notification settings

ONERAI/SpatialGlue-1

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

50 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Integrated analysis of spatial multi-omics with SpatialGlue

DOI

Overview

SpatialGlue is a novel deep learning method for integrating spatial multi-omics data in a spatially informed manner. It utilizes a cycle graph neural network with a dual-attention mechanism to learn the significance of each modality at cross-omics and intra-omics integration. The method can accurately aggregate cell types or cell states at a higher resolution on different tissue types and technology platforms. Besides, it can provide interpretable insights into cross-modality spatial correlations. SpatialGlue is computationally efficient and it only requires about 5 mins for spatial multi-omics data at single-cell resolution (e.g., Spatial-ATAC-RNA-seq data, ~10,000 spots).

Requirements

You'll need to install the following packages in order to run the codes.

  • python==3.8
  • torch>=1.8.0
  • cudnn>=10.2
  • numpy==1.22.3
  • scanpy==1.9.1
  • anndata==0.8.0
  • rpy2==3.4.1
  • pandas==1.4.2
  • scipy==1.8.1
  • scikit-learn==1.1.1
  • tqdm==4.64.0
  • matplotlib==3.4.2
  • R==4.0.3

Tutorial

For the step-by-step tutorial, please refer to: https://spatialglue-tutorials.readthedocs.io/en/latest/

Citation

Yahui Long, Kok Siong Ang, Sha Liao, Raman Sethi, Yang Heng, Chengwei Zhong, Hang Xu, Nazihah Husna, Min Jian, Lai Guan Ng, Ao Chen, Nicholas RJ Gascoigne, Xun Xu, Jinmiao Chen. Integrated analysis of spatial multi-omics with SpatialGlue. bioRxiv. 2023.

About

SpatialGlue is a novel deep learning methods for spatial multi-omics data integration.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages