deepstkit 2.0.2

Identification of spatial domains in spatial transcriptomics by deep learning.

Identification of spatial domains in spatial transcriptomics by deep learning

Update

May 28, 2025

(1) Updated the installation method for DeepST.
(2) Fixed some bugs.

Overview

DeepST first uses H&E staining to extract tissue morphology information through a pre-trained deep learning model, and normalizes each spot’s gene expression according to the similarity of adjacent spots. DeepST further learns a spatial adjacency matrix on spatial location for the construction of graph convolutional network. DeepST uses a graph neural network autoencoder and a denoising autoencoder to jointly generate a latent representation of augmented ST data, while domain adversarial neural networks (DAN) are used to integrate ST data from multi-batches or different technologies. The output of DeepST can be applied to identify spatial domains, batch effect correction and downstream analysis.

How to install DeepST

To install DeepST, make sure you have PyTorch and PyG installed. For more details on dependencies, refer to the environment.yml file.

Step 1: Set Up Conda Environment

conda create -n deepst-env python=3.9 

Step 2: Install PyTorch and PyG

Activate the environment and install PyTorch and PyG. Adjust the installation commands based on your CUDA version or choose the CPU version if necessary.

• General Installation Command

conda activate deepst-env
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118
pip install pyg_lib==0.3.1+pt21cu118 torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cu118.html

• Tips for selecting the correct CUDA version
  • Run the following command to verify CUDA version:

  nvcc --version
  

  • Alternatively, use:

  nvidia-smi
  

• Modify installation commands based on CUDA
  • For CUDA 12.1

    pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
    pip install pyg_lib==0.3.1+pt21cu121 torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cu121.html
    

  • For CPU-only

    pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cpu
    pip install pyg_lib==0.3.1+pt21cpu torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cpu.html
    

Step 3: Install dirac from shell

    pip install sodeepst

Step 4: Import DIRAC in your jupyter notebooks or/and scripts

    import sodeepst as dt

Quick Start

• DeepST on DLPFC from 10x Visium.

import os 
from DeepST import run
import matplotlib.pyplot as plt
from pathlib import Path
import scanpy as sc

data_path = "../data/DLPFC" #### to your path
data_name = '151673' #### project name
save_path = "../Results" #### save path
n_domains = 7 ###### the number of spatial domains.

deepen = run(save_path = save_path,
	task = "Identify_Domain", #### DeepST includes two tasks, one is "Identify_Domain" and the other is "Integration"
	pre_epochs = 800, ####  choose the number of training
	epochs = 1000, #### choose the number of training
	use_gpu = True)
###### Read in 10x Visium data, or user can read in themselves.
adata = deepen._get_adata(platform="Visium", data_path=data_path, data_name=data_name)
###### Segment the Morphological Image
adata = deepen._get_image_crop(adata, data_name=data_name) 

###### Data augmentation. spatial_type includes three kinds of "KDTree", "BallTree" and "LinearRegress", among which "LinearRegress"
###### is only applicable to 10x visium and the remaining omics selects the other two.
###### "use_morphological" defines whether to use morphological images.
adata = deepen._get_augment(adata, spatial_type="LinearRegress", use_morphological=True)

###### Build graphs. "distType" includes "KDTree", "BallTree", "kneighbors_graph", "Radius", etc., see adj.py
graph_dict = deepen._get_graph(adata.obsm["spatial"], distType = "BallTree")

###### Enhanced data preprocessing
data = deepen._data_process(adata, pca_n_comps = 200)

###### Training models
deepst_embed = deepen._fit(
		data = data,
		graph_dict = graph_dict,)
###### DeepST outputs
adata.obsm["DeepST_embed"] = deepst_embed

###### Define the number of space domains, and the model can also be customized. If it is a model custom priori = False.
adata = deepen._get_cluster_data(adata, n_domains=n_domains, priori = True)

###### Spatial localization map of the spatial domain
sc.pl.spatial(adata, color='DeepST_refine_domain', frameon = False, spot_size=150)
plt.savefig(os.path.join(save_path, f'{data_name}_domains.pdf'), bbox_inches='tight', dpi=300)

• DeepST integrates data from mutil-batches or different technologies.

import os 
from DeepST import run
import matplotlib.pyplot as plt
from pathlib import Path
import scanpy as sc

data_path = "../data/DLPFC" 
data_name_list = ['151673', '151674', '151675', '151676']
save_path = "../Results" 
n_domains = 7

deepen = run(save_path = save_path, 
	task = "Integration",
	pre_epochs = 800, 
	epochs = 1000, 
	use_gpu = True,
	)

###### Generate an augmented list of multiple datasets
augement_data_list = []
graph_list = []
for i in range(len(data_name_list)):
	adata = deepen._get_adata(platform="Visium", data_path=data_path, data_name=data_name_list[i])
	adata = deepen._get_image_crop(adata, data_name=data_name_list[i])
	adata = deepen._get_augment(adata, spatial_type="LinearRegress")
	graph_dict = deepen._get_graph(adata.obsm["spatial"], distType = "KDTree")
	augement_data_list.append(adata)
	graph_list.append(graph_dict)

######## Synthetic Datasets and Graphs
multiple_adata, multiple_graph = deepen._get_multiple_adata(adata_list = augement_data_list, data_name_list = data_name_list, graph_list = graph_list)

###### Enhanced data preprocessing
data = deepen._data_process(multiple_adata, pca_n_comps = 200)

deepst_embed = deepen._fit(
		data = data,
		graph_dict = multiple_graph,
		domains = multiple_adata.obs["batch"].values,  ##### Input to Domain Adversarial Model
		n_domains = len(data_name_list))
multiple_adata.obsm["DeepST_embed"] = deepst_embed
multiple_adata = deepen._get_cluster_data(multiple_adata, n_domains=n_domains, priori = True)

sc.pp.neighbors(multiple_adata, use_rep='DeepST_embed')
sc.tl.umap(multiple_adata)
sc.pl.umap(multiple_adata, color=["DeepST_refine_domain","batch_name"])
plt.savefig(os.path.join(save_path, f'{"_".join(data_name_list)}_umap.pdf'), bbox_inches='tight', dpi=300)

for data_name in data_name_list:
	adata = multiple_adata[multiple_adata.obs["batch_name"]==data_name]
	sc.pl.spatial(adata, color='DeepST_refine_domain', frameon = False, spot_size=150)
	plt.savefig(os.path.join(save_path, f'{data_name}_domains.pdf'), bbox_inches='tight', dpi=300)

• DeepST works on other spatial omics data.

import os 
from DeepST import run
import matplotlib.pyplot as plt
from pathlib import Path
import scanpy as sc

data_path = "../data" 
data_name = 'Stereoseq' 
save_path = "../Results" 
n_domains = 15 

deepen = run(save_path = save_path,
	task = "Identify_Domain", 
	pre_epochs = 800, 
	epochs = 1000, 
	use_gpu = True)
###### Read in other spatial data, or user can read in themselves. Including original expression
###### information and spatial location information, where the location information is saved in .obsm["spatial"]
adata = deepen._get_adata(platform="Stereoseq", data_path=data_path, data_name=data_name)

###### Data augmentation. spatial_type includes three kinds of "KDTree", "BallTree" and "LinearRegress", among which "LinearRegress"
###### is only applicable to 10x visium and the remaining omics selects the other two.
###### "use_morphological" defines whether to use morphological images.
adata = deepen._get_augment(adata, spatial_type="BallTree", use_morphological=False)

###### Build graphs. "distType" includes "KDTree", "BallTree", "kneighbors_graph", "Radius", etc., see adj.py
graph_dict = deepen._get_graph(adata.obsm["spatial"], distType = "BallTree")

###### Enhanced data preprocessing
data = deepen._data_process(adata, pca_n_comps = 200)

###### Training models
deepst_embed = deepen._fit(
		data = data,
		graph_dict = graph_dict,)
###### DeepST outputs
adata.obsm["DeepST_embed"] = deepst_embed

###### Define the number of space domains, and the model can also be customized. If it is a model custom priori = False.
adata = deepen._get_cluster_data(adata, n_domains=n_domains, priori = True)

###### Spatial localization map of the spatial domain
sc.pl.spatial(adata, color='DeepST_refine_domain', frameon = False, spot_size=150)
plt.savefig(os.path.join(save_path, f'{data_name}_domains.pdf'), bbox_inches='tight', dpi=300)

Compared tools

Tools that are compared include:

• BayesSpace
• stLearn
• SpaGCN
• Seurat
• SEDR

Download data

Platform	Tissue	SampleID
10x Visium	Human dorsolateral pre-frontal cortex (DLPFC)	151507, 151508, 151509, 151510, 151669, 151670, 151671, 151672, 151673, 151674, 151675, 151676
10x Visium	Mouse brain section	Coronal, Sagittal-Anterior, Sagittal-Posterior
10x Visium	Human breast cancer	Invasive Ductal Carcinoma breast, Ductal Carcinoma In Situ & Invasive Carcinoma
Stereo-Seq	Mouse olfactory bulb	Olfactory bulb
Slide-seq	Mouse hippocampus	Coronal
MERFISH	Mouse brain slice	Hypothalamic preoptic region

Spatial transcriptomics data of other platforms can be downloaded https://www.spatialomics.org/SpatialDB/

Contact

Feel free to submit an issue or contact us at [e-mail] for problems about the packages.