SpatialPrompt

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spatially aware scalable and accurate tool for spot deconvolution and clustering in spatial transcriptomics

SpatialPrompt 최신버젼 다운로드

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# SpatialPrompt : Spatially aware scalable and accurate tool for spot deconvolution and clustering in spatial transcriptomics

Article url: https://doi.org/10.1038/s42003-024-06349-5  \
SpatialPromptDB database url: https://swainasish.github.io/SpatialPrompt/
### Graphical overview 
![alt text](https://raw.githubusercontent.com/swainasish/SpatialPrompt/main/tutorials/images/graphical_abstract.png)
SpatialPrompt integrates gene expression, spatial location, and reference single cell RNA sequencing data for spatial deconvolution.\
For spatial clustering, tool requires only gene expression with spatial locations.
### Installation using PIP 
Platforms: Ubuntu: 22.04/20.04, Windows: 10/11, Mac-OS: Ventura - Python: 3.10 Preferable
```
pip install spatialprompt
```
## Tutorials and Reproducibility section 
### Tutorial-1 (10X Visium human DLPFC dataset) (Figure 2)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hfn10gV3NbG4guAgOb1DRkSam4RxfdI1?usp=sharing)
### Tutorial-2 (10X Visium mouse cortex dataset) (Figure 3)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1leAZVF45zNLzgj6QQT2CCFLbL9vIdObl?usp=sharing)

## Quick start
### Spatial spot deconvolution 
For spot deconvolutiom SpatialPrompt requires:
- `sc_array`: Matrix of Single-cell data, where rows are the cells and columns are the genes.
- `st_array`: Matrix of Spatial data, where rows are the cells and columns are the genes.
- `sc_genes`: Gene names of the `sc_array` matrix.
- `st_genes`: Gene names of the `st_array` matrix.
- `sc_labels`: Cell type annotations of `sc_array`.
- `x_cord`: X coordinate array of spatial data.
- `y_cord`: Y coordinate array of spatial data.
##### Import the library and download the data
```
import scanpy as sc
import spatialprompt as sp
import gdown
gdown.download("https://drive.google.com/uc?id=1n3ACaWjfjXJ8P6IJhxSUax_vBagIUPLV","sc_m_cortex.h5ad", quiet=False)
gdown.download("https://drive.google.com/uc?id=1h7E5nPs2ga1ixOBDjLJKK7V8xJq93ez5","st_m_cortex.h5ad", quiet=False)
```
##### Load the data into Scanpy and retrieve required information
```
sc_data = sc.read_h5ad("sc_m_cortex.h5ad")
st_data = sc.read_h5ad("st_m_cortex.h5ad")
#Requires raw data
sc_array = sc_data.X
sc_genes = sc_data.var_names
sc_labels = sc_data.obs.loc[:,"label"]
st_array = st_data.X.toarray()
st_genes = st_data.var_names
x_cord = st_data.obs.array_row
y_cord = st_data.obs.array_col
```
##### Perform Deconvolution
```
deconv_model = sp.SpatialDeconvolution()
cortex_predict = deconv_model.predict_cell_prop(sc_array,st_array,
                                                sc_genes,st_genes,
                                                sc_labels,x_cord,y_cord )
```
##### Plot the results 
```
for i in cortex_predict.columns:
    st_data.obs.loc[:,i] = cortex_predict.loc[:,i].values
sc.pl.spatial(st_data,
              color=("L2/3 IT", 'L4', 'L5 IT', 'L5 PT', 'L6 CT', 'L6b',"Oligo"),
              library_id='V1_Mouse_Brain_Sagittal_Anterior')
```
![alt text](https://raw.githubusercontent.com/swainasish/SpatialPrompt/main/tutorials/images/m_cortex_deconv.png)
### Spatial domain identification
For domain identification SpatialPrompt requires:
- `st_array`: Matrix of Spatial data, where rows are the cells and columns are the genes.
- `x_cord`: X coordinate array of spatial data.
- `y_cord`: Y coordinate array of spatial data.
##### Import the library and download the data
```
import scanpy as sc
import spatialprompt as sp
import gdown
gdown.download("https://drive.google.com/uc?id=1h7E5nPs2ga1ixOBDjLJKK7V8xJq93ez5","st_m_cortex.h5ad", quiet=False)
```
##### Load the data into Scanpy and retrieve required information
```
st_data = sc.read_h5ad("st_m_cortex.h5ad")
#Requires raw data
st_array = st_data.X.toarray()
x_cord = st_data.obs.array_row
y_cord = st_data.obs.array_col
```
##### Perform spatial clustering
```
clus_model = sp.SpatialCluster()
cortex_domains = clus_model.fit_predict(st_array = st_array,
                                        x_cord = x_cord,
                                        y_cord = y_cord,n_cluster=20)
st_data.obs.loc[:,"SpatialPrompt: clusters"] = cortex_domains
```
##### Plot the results 
```
sc.pl.spatial(st_data,color=("SpatialPrompt: clusters"),
              library_id='V1_Mouse_Brain_Sagittal_Anterior',
              palette="tab20")
```
![alt text](https://raw.githubusercontent.com/swainasish/SpatialPrompt/main/tutorials/images/m_cortex_domains.png)

Cite the article : 
```
@article{swain2024spatialprompt,
  title={SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics},
  author={Swain, Asish Kumar and Pandit, Vrushali and Sharma, Jyoti and Yadav, Pankaj},
  journal={Communications Biology},
  volume={7},
  number={1},
  pages={639},
  year={2024},
  publisher={Nature Publishing Group UK London}
}
```

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