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.

To install DeepTalk, make sure you have PyTorch and scanpy 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

Load your spatial data and your single cell data (which should be in AnnData 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.

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)

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

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