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https://github.com/JiangBioLab/DeepTalk

Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
https://github.com/JiangBioLab/DeepTalk

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Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network

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### **DeepTalk**



#### **Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network**



![Fig1](https://github.com/JiangBioLab/DeepTalk/assets/72069543/c8ce230a-85dd-443b-b9e7-e24ef2f3ae9f)



The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular behavior and regulatory mechanisms in biological systems. However, current computational methods still encounter substantial constraints in inferring spatially resolved CCC at the single-cell level, hampered by their focus on cell-type-centric communications and struggles with handling the limitations of spatial transcriptomics (ST) data. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and ST data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. Application of DeepTalk to diverse datasets from different platforms demonstrates its promising performance and robustness in discovering meaningful spatial CCCs, which can provide a novel avenue for the exploration and interpretation of various biological processes.



### How to install DeepTalk



To install DeepTalk, make sure you have [PyTorch](https://pytorch.org/) and [scanpy](https://scanpy.readthedocs.io/en/stable/) installed. If you need more details on the dependences, look at the `environment.yml` file.



- set up conda environment for DeepTalk



```

  conda env create -n deeptalk-env python=3.8.0

```



  install DeepTalk from shell:



```

  conda activate deeptalk-env

  pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

  pip install torch_cluster==1.5.9 torch_scatter==2.0.7 torch_sparse==0.6.10 torch_spline_conv==1.2.1 -f https://data.pyg.org/whl/torch-1.8.0%2Bcu111.html

  pip install orderedset

  pip install gensim==3.8.3

  pip install DeepTalk_ST

```



- To start using DeepTalk, import DeepTalk in your jupyter notebooks or/and scripts



```

  import DeepTalk_ST as dt

```



### How to run DeepTalk for cell type identification



Load your spatial data and your single cell data (which should be in [AnnData](https://anndata.readthedocs.io/en/latest/) format), and pre-process them using dt.pp_adatas`:



```

  ad_st = sc.read_h5ad(path)

  ad_sc = sc.read_h5ad(path)

  dt.pp_adatas(ad_sc, ad_st, genes=None)

```



The function `pp_adatas` finds the common genes between adata_sc, adata_sp, and saves them in two `adatas.uns` for mapping and analysis later. Also, it subsets the intersected genes to a set of training genes passed by `genes`. If `genes=None`, DeepTalk maps using all genes shared by the two datasets. Once the datasets are pre-processed we can map:



```

  ad_map = dt.map_cells_to_space(ad_sc, ad_st)

```



The returned AnnData,`ad_map`, is a cell-by-voxel structure where `ad_map.X[i, j]` gives the probability for cell `i` to be in voxel `j`. This structure can be used to project gene expression from the single cell data to space, which is achieved via `dt.project_genes`.



```

  ad_ge = dt.project_genes(ad_map, ad_sc)

```



The returned `ad_ge` is a voxel-by-gene AnnData, similar to spatial data `ad_st`, but where gene expression has been projected from the single cells. 



### How to run DeepTalk for cell-cell communication inference



Generating Training Files for Deep Learning using `ad_ge` :



```

  dt.File_Train(st_data, pathways, lrpairs_train, meta_data, species, LR_train, outdir =  Test_dir)

```

```

  dt.data_for_train(st_data, data_dir, LR_pre)

```



Use subgraph-based graph attention network to construct CCC networks for the ligand-receptor pairs with a spatial distance constraint:



```

  dt.Train(data_name,data_path, outdir, pretrained_embeddings, n_epochs = 50, ft_n_epochs=10)

```

Generating Predicting Files for Deep Learning using `ad_ge` :

```

  dt.File_Pre(st_data, pathways, lrpairs_pre, meta_data, species, LR_Pre, outdir)

```

```

  dt.data_for_pre(st_data, data_dir, LR_pre)

```

Predict CCC networks for ligand-receptor pair.

```

  dt.run_predict(data_name, data_path, outdir, pretrained_embeddings, model_path)

```



## Documentation



See detailed documentation and examples at [https://deeptalk.readthedocs.io/en/latest/index.html](https://deeptalk.readthedocs.io/en/latest/index.html).



## Contact



Feel free to submit an issue or contact us at [[email protected]](mailto:[email protected]) for problems about the package.