Installation

How to install DeepTalk

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_ST 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 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)